CN118859687A - Intelligent stable control method and system for bionic transport robot - Google Patents
Intelligent stable control method and system for bionic transport robot Download PDFInfo
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Abstract
本发明公开了一种仿生运输机器人的智能稳定控制方法及系统，通过实施模型预测控制和自适应控制策略，系统可以实时调整其控制参数，以适应不断变化的环境和载荷条件，从而根据仿生运输机器人的重量、质心和倾角的传感器数据，结合仿生运输机器人的动力学特性设计计算驱动力矩，调节比例阀实现对液压马达的力矩输出，通过实时控制系统参数的动态调整，保证了快速响应和高度的操作精确度，从而大幅提升了机器人的工作效率和减少了因错误操作导致的安全事故。
The present invention discloses an intelligent stable control method and system for a bionic transport robot. By implementing model predictive control and adaptive control strategies, the system can adjust its control parameters in real time to adapt to changing environmental and load conditions, thereby calculating the driving torque based on sensor data of the weight, center of mass and inclination of the bionic transport robot and combining the dynamic characteristics of the bionic transport robot, adjusting the proportional valve to achieve torque output to the hydraulic motor, and ensuring rapid response and high operating accuracy through dynamic adjustment of real-time control system parameters, thereby greatly improving the robot's work efficiency and reducing safety accidents caused by erroneous operations.
Description
Technical Field
The invention relates to the technical field of robot equipment, in particular to an intelligent stable control method and system of a bionic transportation robot.
Background
The intelligent stable control system of the bionic transportation robot mainly depends on the high integration of various sensors, control algorithms and execution mechanisms. Conventional control systems typically employ fixed parameter control strategies, such as PID control, which are not readily adaptable to rapid changes in the environment or load after parameter setting. Although this approach works well in a stable environment, its performance tends to be limited in the face of dynamically changing practical application scenarios, such as different weight loads and varying terrain conditions. For example, when a robot transitions from a flat ground surface to a slope, conventional control systems may not be able to quickly adjust the output, resulting in unstable robot motion or slow response.
In addition, the conventional system has defects in multi-sensor data processing and fusion, and the data of a single sensor often cannot provide enough information to comprehensively evaluate and control the state of the robot, so that the accuracy and reliability of control decisions are insufficient. These limitations are particularly evident in demanding industrial applications and complex operating environments, which may increase machine failure rates, reduce work efficiency and safety.
Chinese patent document CN113204892B discloses a "centroid trace generation method, apparatus, computer-readable storage medium and robot". The method comprises the steps of determining the actual pose of the mass center, the pose of the left foot and the pose of the right foot of the robot; determining a first pose tracking vector of the robot according to the actual pose of the mass center and the pose of the left foot; determining a second pose tracking vector of the robot according to the actual pose of the mass center and the pose of the right foot; and controlling the expected centroid pose of the robot to alternately track the left foot pose and the right foot pose according to the first pose tracking vector and the second pose tracking vector so as to generate an expected centroid track of the robot. The technical scheme is difficult to adjust the control parameters in time to adapt to the continuously changing environment and load conditions.
Disclosure of Invention
The intelligent stable control method and the system for the bionic transportation robot mainly solve the technical problem that the control parameters are difficult to adjust in time to adapt to continuously changing environment and load conditions in the original technical scheme, and the control parameters can be adjusted in real time by implementing model predictive control and self-adaptive control strategies, so that the control parameters are suitable for the continuously changing environment and load conditions, the driving moment is calculated according to sensor data of the weight, the mass center and the inclination angle of the bionic transportation robot and the dynamic characteristics of the bionic transportation robot, the proportional valve is adjusted to realize the moment output of the hydraulic motor, and the dynamic adjustment of the parameters of the real-time control system is adopted, so that the quick response and the high operation accuracy are guaranteed, the working efficiency of the robot is greatly improved, and the safety accidents caused by error operation are reduced.
The technical problems of the invention are mainly solved by the following technical proposal: the invention comprises the following steps:
S1, sensing the state of the bionic transportation robot to obtain the current state quantity of the bionic transportation robot;
S2, calculating a driving moment by combining the dynamics characteristics of the bionic transportation robot, predicting future system output quantity and state quantity based on the current state quantity and the future control quantity, realizing optimal control by solving the constraint optimization problem through rolling, and adjusting control input according to control errors;
And S3, controlling moment output according to the driving moment, and driving the bionic transportation robot to complete preset movement.
Preferably, the current state quantity of the bionic transportation robot in the step S1 includes a centroid position of the bionic transportation robot, an azimuth and an inclination angle of the bionic transportation robot in a three-dimensional space, and further includes a moving speed, a rotating speed and an actual load of the bionic transportation robot on the ground.
Preferably, the step S2 includes continuously optimizing the solution in the control process, that is, at each moment, according to the state quantity error at the current moment, solving the solution through a quadratic programming problem to obtain the control quantity optimal solution when the error between the system output and the reference quantity is the minimum, and repeatedly performing the optimization process online.
Preferably, the step S2 includes correcting each sampling time by feedback, that is, correcting the predicted result by using the actual output, and then performing the next optimization process.
Preferably, the model predicted in the step S2 is expressed as a linear state space model assuming a kinetic model of the bionic transportation robot:
Wherein, Is the state vector at time k,Is a control input, and A and B are a state transition matrix and a control input matrix of the system respectively;
the objective function and constraints are defined as:
Wherein, AndRespectively a reference state and a reference control input, Q and R are weight matrixes, and N is a prediction range;
Constraints of the control input are expressed as:
where u is the control input.
Preferably, in the step S2, the control error is PID controlled, and the control law is expressed as:
Wherein, Is the control error of the device and the control method,，AndProportional, integral and derivative gains, respectively.
Preferably, the control model of the combination of the predictive model and the PID control is expressed as:
Wherein, Is based on the main control signal calculated by the predictive control moduleIs a PID control signal for fine tuning.
The intelligent stable control system of the bionic transportation robot comprises a sensor unit, a control unit and an execution unit, wherein the control unit comprises a model prediction control module and a PID control module. The sensor unit senses the state of the bionic transportation robot in real time, the control unit designs a corresponding module to calculate driving moment according to the obtained sensor data of the weight, the mass center and the inclination angle of the bionic transportation robot and combines the dynamic characteristics of the bionic transportation robot, then the control unit sends a control signal to the execution unit, and the execution unit realizes the moment output of the hydraulic motor through the adjustment of the comparison valve. The model prediction control module predicts future system output quantity and state quantity based on the current state quantity and the future control quantity of the system, and realizes an algorithm of optimal control by solving the constraint optimization problem through rolling, and comprises a prediction model, a rolling optimization module and a feedback correction module; the PID control module adjusts the control input based on the control error, i.e., the difference between the desired value and the actual value.
Preferably, the sensor unit comprises a load detection module, a mass center monitoring module, a gesture monitoring module and a speed monitoring module.
The load detection module is used for measuring the pressure change born by the bionic transportation robot through a weighing sensor arranged on a chassis or a bearing structure of the bionic transportation robot so as to calculate the actual load;
The mass center monitoring module is used for providing data about the gesture, the acceleration and the angular velocity of the bionic transportation robot by the IMU through the integrated accelerometer and gyroscope, and calculating the mass center position of the bionic transportation robot;
The attitude monitoring module monitors the azimuth and the inclination angle of the bionic transportation robot in a three-dimensional space through a gyroscope and a magnetometer in the IMU, the gyroscope measures the angular velocity around three axes, the magnetometer provides information about the earth magnetic field, and the direction of the bionic transportation robot relative to the earth is determined;
the speed monitoring module is used for monitoring the linear speed and the angular speed, and respectively corresponds to the moving speed and the rotating speed of the bionic transportation robot on the ground, and monitors through an encoder, a wheel speed sensor or a visual odometer.
Preferably, the execution unit uses hydraulic fluid as the control system of the energy transmission medium, and comprises a hydraulic pump, a proportional valve, a hydraulic motor and a controller.
The beneficial effects of the invention are as follows: by implementing model predictive control and self-adaptive control strategies, the system can adjust control parameters in real time to adapt to continuously changing environment and load conditions, so that driving moment is calculated according to sensor data of weight, mass center and inclination angle of the bionic transportation robot and by combining dynamic characteristic design of the bionic transportation robot, moment output of a hydraulic motor is realized by adjusting a proportional valve, and through dynamic adjustment of parameters of the real-time control system, quick response and high operation accuracy are ensured, so that working efficiency of the robot is greatly improved and safety accidents caused by misoperation are reduced.
Drawings
Fig. 1 is a flow chart of the present invention.
Fig. 2 is a schematic connection structure diagram of the present invention.
Fig. 3 is a schematic structural view of a sensor unit of the present invention.
Fig. 4 is a schematic structural diagram of an intelligent stabilized electrical module according to the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the present invention more apparent, the technical solutions of the present invention will be further described in detail below by way of examples with reference to the accompanying drawings, and it should be understood that the detailed description herein is merely a preferred embodiment of the present invention, and is not intended to limit the scope of the present invention, but all other embodiments obtained by persons of ordinary skill in the art without making any inventive effort are within the scope of the present invention.
Before discussing the exemplary embodiments in more detail, it should be mentioned that some exemplary embodiments are described as processes or methods depicted as flowcharts. Although a flowchart depicts operations (or steps) as a sequential process, many of the operations (or steps) can be performed in parallel, concurrently, or at the same time. Furthermore, the order of the operations may be rearranged. The process may be terminated when its operations are completed, but may have additional steps not included in the figures; the processes may correspond to methods, functions, procedures, subroutines, and the like.
The technical scheme of the invention is further specifically described below through examples and with reference to the accompanying drawings.
Examples: the embodiment relates to an intelligent stable control method and system for a bionic transportation robot, as shown in fig. 1,2 and 3. An intelligent stable control method of a bionic transportation robot comprises the following steps:
S1, sensing the state of the bionic transportation robot, and obtaining the current state quantity of the bionic transportation robot. The current state quantity of the bionic transportation robot comprises the mass center position of the bionic transportation robot, the azimuth and the inclination angle of the bionic transportation robot in a three-dimensional space, and the current state quantity of the bionic transportation robot further comprises the moving speed, the rotating speed and the actual load of the bionic transportation robot on the ground.
S2, calculating a driving moment by combining the dynamics characteristics of the bionic transportation robot, and predicting future system output and state quantity based on the current state quantity and the future control quantity, wherein the predicted model is expressed as a linear state space model of a hypothetical bionic transportation robot dynamics model:
Wherein, Is the state vector at time k,Is a control input, and A and B are a state transition matrix and a control input matrix of the system respectively;
the objective function and constraints are defined as:
Wherein, AndRespectively a reference state and a reference control input, Q and R are weight matrixes, and N is a prediction range;
Constraints of the control input are expressed as:
where u is the control input.
Optimal control is achieved by solving the constrained optimization problem by scrolling, and the control input is adjusted according to the control error, i.e. the difference between the desired value and the actual value. The control error adopts PID control, and the control law is expressed as:
Wherein, Is the control error of the device and the control method,，AndProportional, integral and derivative gains, respectively.
And continuously optimizing and solving in the control process, namely solving through a quadratic programming problem according to the state quantity error at the current moment at each moment to obtain the optimal solution of the control quantity when the error between the system output and the reference quantity is the minimum, and repeatedly carrying out the optimization process on line.
And correcting each sampling moment in a feedback mode, namely correcting the predicted result by utilizing the actual output, and then carrying out the next optimization process.
The control model of the predicted model in combination with the PID control is expressed as:
Wherein, Is based on the main control signal calculated by the predictive control moduleIs a PID control signal for fine tuning.
And S3, controlling moment output according to the driving moment, and driving the bionic transportation robot to complete preset movement.
An intelligent stable control system of a bionic transportation robot comprises a sensor unit, a control unit and an execution unit. The sensor unit senses the state of the bionic transportation robot in real time, the control unit designs a corresponding module to calculate driving moment according to the obtained sensor data of the weight, the mass center and the inclination angle of the bionic transportation robot and combines the dynamic characteristics of the bionic transportation robot, then the control unit sends a control signal to the execution unit, and the execution unit realizes the moment output of the hydraulic motor through the adjustment of the comparison valve.
The control unit comprises a model prediction control module and a PID control module; the model prediction control module predicts future system output quantity and state quantity based on the current state quantity and the future control quantity of the system, and realizes an algorithm of optimal control by solving the constraint optimization problem through rolling, and comprises a prediction model, a rolling optimization module and a feedback correction module; the PID control module adjusts the control input based on the control error, i.e., the difference between the desired value and the actual value.
The sensor unit comprises a load detection module, a mass center monitoring module, a posture monitoring module and a speed monitoring module.
The load detection module is used for measuring the pressure change born by the bionic transportation robot through a weighing sensor arranged on a chassis or a bearing structure of the bionic transportation robot so as to calculate the actual load;
The mass center monitoring module is used for providing data about the gesture, the acceleration and the angular velocity of the bionic transportation robot by the IMU through the integrated accelerometer and gyroscope, and calculating the mass center position of the bionic transportation robot;
The attitude monitoring module monitors the azimuth and the inclination angle of the bionic transportation robot in a three-dimensional space through a gyroscope and a magnetometer in the IMU, the gyroscope measures the angular velocity around three axes, the magnetometer provides information about the earth magnetic field, and the direction of the bionic transportation robot relative to the earth is determined;
the speed monitoring module is used for monitoring the linear speed and the angular speed, and respectively corresponds to the moving speed and the rotating speed of the bionic transportation robot on the ground, and monitors through an encoder, a wheel speed sensor or a visual odometer.
The execution unit uses hydraulic fluid as a control system of the energy transfer medium, comprising a hydraulic pump and a proportional valve, as well as components of a hydraulic motor and a controller.
The intelligent stable control system further comprises an adaptive control module, and the adaptive control module specifically comprises: load weight self-adaptive control; self-adaptive control of the ground gradient; the adaptive model predicts, controls and adjusts; fusing multi-sensor data; and dynamically adjusting parameters of a real-time control system.
In the adaptive control module, for the change of the load weight, the controller adjusts the parameters thereof in real time based on the load weight adaptive control to cope with the weight increase and decrease, and ensures the stability, the following is an adaptive PID control formula:
Wherein the method comprises the steps of Is a function of adjusting the PID gain based on the load weight W, assuming:
The intelligent stable control system further comprises a safety protection module, wherein the safety protection module is provided with a limit switch, an overload protection device, a collision detection and obstacle avoidance system, temperature monitoring, voltage and current monitoring, software monitoring and fault diagnosis.
The intelligent stable control system further comprises an intelligent stable electric module, and the architecture of the intelligent stable electric module is divided into a perception planning layer and an execution layer:
the perception planning layer comprises an unmanned area controller, a sensor of a laser radar and USERCAN;
The execution layer controls drive-by-wire, brake-by-wire and steering-by-wire through the vehicle control unit, so as to control corresponding mechanical parts and realize accurate control of the bionic transportation robot.
Examples:
Referring to fig. 1, an intelligent stable control system of a bionic transportation robot includes a sensor unit, a control unit and an execution unit, wherein the sensor unit senses the state of the bionic transportation robot in real time, the control unit designs corresponding modules to calculate driving moment by combining dynamic characteristics of the bionic transportation robot according to sensor data of weight, mass center and inclination angle of the bionic transportation robot, then sends control signals to the execution unit, and the execution unit realizes torque output of a hydraulic motor through adjustment of a comparison valve.
The sensor unit comprises a load detection module, a mass center monitoring module, a gesture monitoring module and a speed monitoring module.
Referring to fig. 2, in the sensor unit:
The load detection module is used for measuring pressure changes born by the bionic transportation robot through a weighing sensor arranged on a chassis or a bearing structure of the bionic transportation robot, so as to calculate the actual load.
The weighing sensor can be of a piezoelectric type, a strain gauge type or a piezoresistive type, load monitoring is helpful for preventing overload, the bionic transportation robot is ensured to operate in a safe load range, and meanwhile, the weighing sensor can be used for optimizing load distribution. The following mechanism is adopted:
A weighing sensor: and installing high-precision weighing sensors at key supporting points of the bionic transportation robot so as to measure the pressure of each supporting point.
Data fusion: by analyzing the data of the plurality of sensors, the load of the whole bionic transportation robot can be estimated more accurately.
Dynamic adjustment: the load data can be used to dynamically adjust motion parameters, such as acceleration and speed, of the biomimetic transport robot to ensure stable operation.
The centroid monitoring module, centroid position monitoring is very important for maintaining stability of the bionic transportation robot, especially when dynamic operations (such as turning, accelerating or climbing) are performed. This is typically achieved by an integrated accelerometer and gyroscope. The IMU may provide data regarding the pose, acceleration, and angular velocity of the biomimetic transport robot, thereby helping to calculate the centroid position of the biomimetic transport robot. Centroid monitoring is helpful for adjusting balance of the bionic transportation robot in the motion process, and overturn or out of control is avoided. The following mechanism is adopted:
Pressure distribution analysis: by analyzing the data of the weighing sensor, the centroid position of the bionic transportation robot can be estimated.
Multidimensional sensor: a multidimensional force sensor is used to measure forces in multiple directions to more accurately determine the location of the centroid.
Real-time feedback: and feeding the centroid position data back to a control system to adjust the gesture and the motion strategy of the bionic transportation robot in real time.
The gesture monitoring module comprises a step of monitoring the azimuth and the inclination angle of the bionic transportation robot in a three-dimensional space. This is accomplished primarily by means of gyroscopes and magnetometers in the IMU. The gyroscope may measure angular velocity about three axes, while the magnetometer may provide information about the earth's magnetic field, helping to determine the orientation of the biomimetic transport robot relative to the earth. The attitude monitoring is beneficial to the stability of the bionic transportation robot on uneven terrain, and simultaneously supports the navigation and positioning functions. The following mechanism is adopted:
IMU sensor: the pose of the biomimetic transport robot is monitored using IMU sensors that integrate accelerometers, gyroscopes and magnetometers.
Inertial navigation: through analyzing IMU data, inertial navigation and gesture stabilization of the bionic transportation robot can be realized.
Calibration and compensation: and (3) calibrating the IMU sensor regularly and compensating the system error to improve the accuracy of attitude monitoring.
The speed monitoring module is used for monitoring the speed, and the speed monitoring module comprises two aspects, namely a linear speed and an angular speed, which respectively correspond to the moving speed and the rotating speed of the bionic transportation robot on the ground. This is typically accomplished by an encoder, wheel speed sensor, or visual odometer. The speed monitoring is helpful for controlling the motion of the bionic transportation robot, ensuring that the bionic transportation robot runs at a preset speed, and avoiding collision or out of control. The following mechanism is adopted:
an encoder: encoders are mounted at the drive wheels or joints to measure linear and angular velocities.
And (3) sliding detection: by monitoring the change of the encoder signal, the sliding or slipping condition of the bionic transportation robot can be detected.
Multisensor fusion: the speed and the direction of the bionic transportation robot can be estimated more accurately by combining the data of the IMU and other sensors.
The control unit includes a Model Predictive Control (MPC) module and a PID control module.
The Model Predictive Control (MPC) module predicts future system output quantity and state quantity based on the current state quantity and the future control quantity of the system, and solves the constraint optimization problem through rolling to realize the optimal control algorithm, and comprises a predictive model, a rolling optimization module and a feedback correction module.
In this embodiment, the model predictive control module is configured to:
The predictive model is expressed as a linear state space model assuming a biomimetic transport robot dynamics model:
Wherein, Is the state vector at time k,Is a control input, and A and B are a state transition matrix and a control input matrix of the system respectively;
Wherein, AndRespectively a reference state and a reference control input, Q and R are weight matrixes, and N is a prediction range;
Constraints of the control input are expressed as:
where u is the control input.
In this embodiment, the model predictive control module is configured to:
the rolling optimization module needs to be continuously optimized and solved in the control process, namely, at each moment, the controller solves the problem through quadratic programming according to the state quantity error at the current moment to obtain the optimal solution of the control quantity when the error between the system output and the reference quantity is the minimum, and the optimization process is repeatedly performed on line;
The feedback correction module can generate deviation between a model prediction result and an ideal state caused by factors such as model mismatch, environmental interference and the like in the model prediction control process. Therefore, in order to prevent such phenomena, the controller corrects the system in a feedback manner at each sampling time, that is, corrects the model prediction result by using the actual output of the system, and then performs the next optimization process.
In the present embodiment, the model predictive control principle is: when the system is at the current moment (k moment), the model prediction controller can obtain a section of system output in the future prediction time domain according to the current state measurement value and the control measurement value and combines the prediction model, then obtains a series of control sequences in the control time domain by optimizing and solving the objective function with the constraint, the controller takes the first element of the control sequences as the actual control quantity of the system, inputs the controlled object, further controls the controlled object to come to the next moment (k+1 moment), circulates the process, and scrolls to complete the optimization problem of the objective function with the constraint, thereby realizing the control of the researched object.
The PID control module adjusts the control input based on the control error, i.e., the difference between the desired value and the actual value.
In this embodiment, the PID control module is built with a PID control model, and for PID control, the control law is expressed as:
Wherein, Is the control error of the device and the control method,，AndProportional, integral and derivative gains, respectively.
In this embodiment, in the control unit, the prediction control module is used for controlling a policy, the PID control module is used for adjusting a small error and a fast response, and a control model combined by the prediction control module and the PID control module is expressed as:
Wherein, Is based on the main control signal calculated by the predictive control moduleIs a PID control signal for fine tuning. PID control provides a control mechanism with quick response on the basis of MPC, and can correct system deviation in real time, so as to ensure the stability of the bionic transportation robot under instantaneous disturbance. The PID controller realizes the quick response and elimination of the system deviation by adjusting the gains of the proportional term, the integral term and the differential term, and is an important supplement of the MPC control strategy.
In this embodiment, the execution unit is a hydraulic servo control system, and the control system using hydraulic fluid as an energy transfer medium includes components of a hydraulic pump, a proportional valve, a hydraulic motor, a sensor, and a controller; and the control signal is converted into actual mechanical moment to drive the bionic transportation robot to complete preset movement.
In the bionic transportation robot, a hydraulic servo system is responsible for converting a control signal into an actual mechanical moment to drive the bionic transportation robot to complete preset movement. In biomimetic transport robot applications, hydraulic servo control systems are commonly used to achieve high precision, high force motion control.
The proportional valve is a key component in the hydraulic servo control system, and can adjust the flow and pressure of hydraulic fluid flowing to the hydraulic motor according to the control signal, so that torque output of the hydraulic motor is realized. By accurately controlling the opening of the proportional valve, the accurate moment control of the hydraulic motor can be realized, so that the accurate motion control of the bionic transportation robot is realized.
The control signals generated by the MPC and PID control algorithms are sent to the hydraulic servo control system. The pressure and flow of the hydraulic fluid are controlled through accurate adjustment of the proportional valve, so that the rotating speed and torque output of the hydraulic motor are adjusted. This process requires a high degree of accuracy and response speed to ensure stability and accuracy of the biomimetic transport robot when performing complex actions.
The basic steps of the MPC algorithm are as follows: first, a dynamic model of the system is built. For hydraulic servo control systems, hydraulic transmission theory and hydrodynamic principles can be used to build dynamic models of the system. Next, future behavior of the system is predicted based on the predictive model. This may be achieved by numerically solving differential equations or using an optimization algorithm. Then, the optimal control strategy is solved by using an optimization algorithm. The optimization objective may be to minimize errors, minimize energy consumption, maximize performance, etc. And finally, converting the optimal control strategy into an actual control signal to be applied to the system, so as to realize the control of the system.
In MPC control algorithms, PID controllers are often used as part of a predictive model for local tuning based on the current state of the system. The input of the PID controller is the current error of the system, and the output is the adjustment of the controller. The PID controller is composed of three parts: a proportional part, an integral part and a differential part. The proportional part generates a control amount according to the current error magnitude, the integral part generates a control amount according to the past error accumulation, and the differential part generates a control amount according to the rate of change of the error. The output of the PID controller is the sum of these three parts. The PID controller controls the system by adjusting parameters of the three parts.
For hydraulic servo control systems, the application of the MPC+PID control algorithm can be performed as follows: first, a dynamic model of the hydraulic servo control system is established. According to the hydraulic transmission theory and the fluid mechanics theory, a mathematical model of the hydraulic servo system can be established. Second, the MPC algorithm is used to predict the future behavior of the system. Based on the dynamic model and the current state of the system, the behavior of the system in a future period of time can be predicted. Then, an optimization algorithm is used to solve the optimal control strategy. According to the predicted result and the optimization target, an optimal control strategy can be determined. And finally, converting the optimal control strategy into an actual control signal and sending the actual control signal to a hydraulic servo control system. And the torque output of the hydraulic motor is realized through the adjustment of the comparison valve.
The specific implementation mode is as follows:
firstly, establishing a mathematical model of a system, including a system dynamics model, a hydraulic servo system model and the like;
Then, predicting system behaviors in a future period of time by utilizing an MPC algorithm, and designing optimal control input;
Then, the difference between the actual system behavior and the expected behavior is adjusted by using a PID controller so as to reduce errors and maintain the stability of the system;
and finally, sending control signals generated by the MPC and the PID controller to a hydraulic servo control system, and realizing torque output of the hydraulic motor through adjustment of a comparative example valve.
Through reasonable controller design, parameter adjustment and system configuration, the combination of the MPC+PID control algorithm and the hydraulic servo control system can realize stable operation and accurate control of the bionic transportation robot under various complex working conditions.
In this embodiment, the intelligent stability control system further includes an adaptive control module, where the adaptive control module specifically includes:
Load weight self-adaptive control;
When the bionic transportation robot bears cargoes with different weights, the dynamic characteristics of the bionic transportation robot can be changed remarkably, and the stability and the motion control performance of the bionic transportation robot can be directly affected. The self-adaptive control strategy can dynamically adjust control parameters by estimating the load quality on line, and when the load increase is detected, the self-adaptive control algorithm correspondingly increases the proportional gain of the PID controller so as to enhance the response speed of the system; meanwhile, the integral gain and the differential gain are properly adjusted to inhibit overshoot and oscillation, so that the stable operation of the bionic transportation robot under the load change is ensured. To compensate for the effects due to load variations.
The specific implementation mode is as follows: measuring the load weight in real time by using a weighing sensor; according to the measurement result, the centroid position and control parameters of the bionic transportation robot are adjusted; a model predictive control algorithm is adopted, future system behaviors are predicted according to the current load weight and the centroid position, and optimal control input is designed; and adjusting the control input according to the difference between the actual system behavior and the expected behavior by using a PID control algorithm so as to reduce errors and maintain the system stability.
Self-adaptive control of the ground gradient;
the change of the ground gradient can also influence the stability of the bionic transportation robot. On a slope or rugged ground, the bionic transportation robot needs to adjust its posture and moment output to maintain stability and normal running. The self-adaptive control strategy is used for adjusting the torque distribution and steering angle of the driving wheels by monitoring the ground gradient in real time so as to maintain the stable running of the bionic transportation robot on the slope. When the bionic transportation robot detects that the vehicle is ascending, the self-adaptive control algorithm can increase the torque of the rear wheel so as to overcome the trend of gravity sliding downwards, and simultaneously adjust the steering angle of the front wheel, so that the bionic transportation robot is ensured to stably advance along a target path. When the vehicle runs down a slope, the torque of the rear wheels is reduced, the braking force of the front wheels is increased, and uncontrolled sliding is prevented.
The specific implementation mode is as follows: measuring the gradient of the ground in real time by using an inclination sensor; according to the measurement result, adjusting the gesture and moment output of the bionic transportation robot; a model predictive control algorithm is adopted, future system behaviors are predicted according to the current ground gradient and the gesture of the bionic transportation robot, and optimal control input is designed; and adjusting the control input according to the difference between the actual system behavior and the expected behavior by using a PID control algorithm so as to reduce errors and maintain the stability of the system.
Fusing multi-sensor data;
In order to improve the self-adaptive capacity of the intelligent stabilization system, a multi-sensor data fusion technology can be adopted. By fusing various sensor data such as a weighing sensor, an inclination angle sensor, an acceleration sensor and the like, the state of the bionic transportation robot can be estimated more accurately, and the robustness of a control strategy is improved.
Sensor fusion: and various sensors (such as an IMU, a moment sensor, a GPS and the like) are utilized to acquire information of the attitude, the speed, the position and the like of the bionic transportation robot, and environmental parameters such as load weight, ground gradient and the like.
Real-time data processing: and through a high-speed processor, the sensor data is analyzed in real time, the environmental change is rapidly identified, and a decision basis is provided for self-adaptive control.
Dynamic parameter adjustment: and designing an adaptive control algorithm, and adjusting control parameters such as PID gain, torque distribution proportion and the like in real time according to environmental changes so as to optimize control performance.
Model prediction and learning: the model predictive control is combined with the machine learning technology, so that the predictability and the adaptivity of a control strategy are improved, and the bionic transportation robot can make more accurate response in an unknown or changing environment.
And dynamically adjusting parameters of a real-time control system.
In order to achieve real-time control, intelligent stabilization systems need to have high-speed data processing and transmission capabilities. The high-performance microprocessor, the real-time operating system and the high-speed communication interface can be adopted to ensure the real-time performance and the effectiveness of the control strategy.
In a word, the intelligent stabilization system should have self-adaptive capability, and can adjust the control strategy in real time according to the environmental change, so as to maintain the stability of the bionic transportation robot. By adopting the self-adaptive control strategy, the multi-sensor data fusion technology and the real-time control system, the bionic transportation robot can be precisely controlled, and the stability and the reliability of the bionic transportation robot in a complex environment are improved.
In the adaptive control module, for the change of the load weight, the controller adjusts the parameters of the load weight adaptive control module in real time based on the load weight adaptive control so as to cope with the weight increase and decrease, and ensure the stability, the following is an adaptive PID control formula:
Wherein the method comprises the steps of Is a function of adjusting the PID gain based on the load weight W, assuming:
in this embodiment, the intelligent stability control system further includes a safety protection module, where the safety protection module is provided with a limit switch, an overload protection device, a collision detection and obstacle avoidance system, temperature monitoring, voltage and current monitoring, software monitoring, and fault diagnosis.
(1) Limit switch: is a safety device for detecting and limiting the range of mechanical movement. In the bionic transportation robot, the limit switch can be arranged on the moving part, and when the bionic transportation robot approaches the limit of the movement range of the bionic transportation robot, the limit switch can be triggered, so that a power source is cut off, the movement of related parts is stopped immediately, and collision or damage is prevented.
(2) Overload protection device: the motor load monitoring device is mainly used for monitoring and controlling the working state of the motor, and when the motor load exceeds a set value, the power supply is automatically cut off, so that the motor is prevented from being damaged due to overheat and overcurrent. This is typically achieved by installing pressure sensors or force sensors that can monitor the load of the biomimetic transport robot in real time. The overload protection device will immediately shut off the power source to prevent further damage once it detects that the load exceeds a set safety threshold.
(3) Collision detection and obstacle avoidance system: through integrated sensor (such as laser radar, ultrasonic sensor, vision sensor etc.), the bionic transportation robot can real-time perception surrounding environment, discernment barrier. Once a potential collision risk is detected, the system can quickly adjust the path or slow down and stop, and avoid contacting with obstacles, thereby protecting the safety of the bionic transportation robot and surrounding personnel.
In addition, an emergency stop button is arranged on the operation interface of the bionic transportation robot, when an emergency is met, an operator can immediately press the button to immediately stop all actions of the bionic transportation robot, and the button is easy to access and is obviously marked so as to ensure that the operation can be rapidly carried out in the emergency.
(4) And (3) temperature monitoring:
During the motion of the bionic transportation robot, a large amount of heat can be generated by the motor and the hydraulic system. Temperature sensors may be used to monitor the temperature of these critical components to prevent overheating. If the temperature exceeds the safe range, the system will automatically power down or cease operation to prevent damage.
(5) Voltage and current monitoring:
Abnormal fluctuations in voltage and current may lead to failure of the biomimetic transport robotic system. The working states of the power supply and the motor can be monitored in real time by installing the voltage and current sensors. Upon detection of an anomaly, the system will automatically take action, such as powering down or powering down, to protect the biomimetic transport robot from damage.
(6) Software monitoring and fault diagnosis:
the software portion of the intelligent stabilization system should include monitoring and fault diagnosis functions. This includes monitoring critical parameters of the system, such as speed, acceleration, torque, etc., in real time and being able to identify any abnormal behavior. If a potential problem is detected, the system should be able to automatically adjust the operation or notify the operator to take manual intervention. The bionic transportation robot is provided with a self-diagnosis function, can periodically check the states of hardware and software of the bionic transportation robot, and immediately starts protective measures, such as automatic shutdown, alarm information sending and the like, once abnormality is found, so that problems are ensured to be timely processed, and further expansion of faults is prevented.
Referring to fig. 4, in this embodiment, the intelligent stability control system further includes an intelligent stability electric module, where an architecture of the intelligent stability electric module is divided into a perception planning layer and an execution layer;
In the perception planning layer, the system mainly comprises unmanned area controllers, sensors such as laser radars and USERCAN. The unmanned area controller is the core of the whole system and is responsible for processing data from various sensors, carrying out data fusion and processing and making decisions, such as determining driving routes, speeds and the like. Sensors such as lidar are used to obtain information about the surrounding environment, such as the location, distance, etc. of obstacles. USERCAN refers to a portion of a user interface and communication protocol for data transfer between different components.
In the execution layer, the VCU (Vehicle Control Unit ) is mainly used for controlling the drive-by-wire, brake-by-wire and steering-by-wire, so as to control corresponding mechanical components, thereby realizing accurate control on the vehicle. Including acceleration, braking, steering, etc. The chassis control system may perform the functions as shown in table 1.
| Chassis control system | Control mode | Key hardware device | Can realize the functions of | Communication method |
| Brake-by-wire system | Braking pressure | ESC, wire control brake assembly | Active regulation of brake pressure | CAN network communication |
| Steer-by-wire system | Target rotation angle | Drive-by-wire steering assembly | Active corner control | CAN network communication |
| Drive-by-wire system | Motor torque | Electric drive and motor | Active regulation of vehicle speed | CAN network communication |
Table 1 chassis control system function table
In the aspect of communication control, the three modules of the brake-by-wire, the drive-by-wire and the steering-by-wire adopt a CAN (Controller Area Network, controller area network bus) signal communication mode. The CAN bus is a protocol widely applied to communication in the vehicle, CAN realize high-speed and high-reliability data transmission, and ensures accurate information exchange among all control units. Through the CAN bus, the three key modules CAN exchange state information and control instructions in real time, so that coordination work is realized, and the dynamic performance and response speed of the vehicle are improved.
For a wire control gear, the wire control chassis is provided with a set of DNR (Drive Neutral Reverse, forward reverse gear) three-level control and safety logic setting. The DNR system allows the driver to control the shift of gears by means of an electrical signal, thereby achieving driving, neutral and reverse of the vehicle. The system not only improves the convenience and operability of driving, but also ensures the safety of the switching process through strict safety logic setting, and prevents accidents caused by misoperation of gears.
In order to support the remote control driving function, the wire control chassis is provided with a self-grinding CAN communication remote controller. The remote controller can remotely control various functions of the bionic transportation robot through wireless signals, including braking, steering, driving and the like, and convenience is provided for unmanned and remote operation. The remote control driving function can greatly improve the working efficiency and the safety under specific environments such as narrow space working or dangerous area working.
In terms of power management, the bionic transportation robot can detect voltage and current in real time, display the state of a Battery, and optimize power output through a BMS (Battery MANAGEMENT SYSTEM). The BMS not only can protect the power battery and prevent overcharge and overdischarge, but also can monitor the health condition of the battery, predict the service life of the battery, and provide intelligent support for energy management of vehicles.
The chassis is provided with vehicle self-checking functions, including starting self-checking and driving self-checking. In the starting stage, the chassis can carry out self-checking on the gravity-point control component of the bionic transportation robot, so that all systems can work normally. When the system runs, the working state of the chassis part can be detected in real time, and driving data is fed back, so that the automatic driving system can know the running state of the bionic transportation robot in time, and corresponding measures are taken to ensure the running safety of the vehicle.
The beneficial effects of the invention are as follows:
The intelligent stable control system of the bionic transportation robot can remarkably improve the performance and safety of the bionic transportation robot in a complex environment. By implementing model predictive control and adaptive control strategies, the system can adjust its control parameters in real time to accommodate changing environmental and load conditions. The application of the multi-sensor data fusion technology enables the system to estimate the state of the robot more accurately, and the robustness of the control strategy is enhanced. In addition, by dynamically adjusting parameters of the real-time control system, the quick response and the high operation accuracy are ensured, so that the working efficiency of the robot is greatly improved, and the safety accidents caused by incorrect operation are reduced. These improvements provide a significant competitive advantage for biomimetic transport robots in industrial automation and complex task execution.
Those of ordinary skill in the art will appreciate that all or part of the steps in the various methods of the above embodiments may be implemented by a program to instruct related hardware, the program may be stored in a computer readable storage medium, and the storage medium may include: read Only Memory (ROM), random access Memory (RAM, random Access Memory), magnetic or optical disk, and the like.
The specific embodiments described herein are offered by way of example only to illustrate the spirit of the invention. The foregoing examples illustrate only a few embodiments of the invention, which are described in detail and are not to be construed as limiting the scope of the invention. It should be noted that various modifications or additions to the described embodiments or substitutions thereof can be made by one skilled in the art without departing from the spirit of the invention or exceeding the scope of the invention as defined by the accompanying claims. It will be apparent to those skilled in the art that various modifications and improvements can be made without departing from the spirit of the invention. Accordingly, the scope of protection of the present invention is to be determined by the appended claims.
Claims (10)
1. The intelligent stable control method of the bionic transportation robot is characterized by comprising the following steps of:
S1, sensing the state of the bionic transportation robot to obtain the current state quantity of the bionic transportation robot;
S2, calculating a driving moment by combining the dynamics characteristics of the bionic transportation robot, predicting future system output quantity and state quantity based on the current state quantity and the future control quantity, realizing optimal control by solving the constraint optimization problem through rolling, and adjusting control input according to control errors;
And S3, controlling moment output according to the driving moment, and driving the bionic transportation robot to complete preset movement.
2. The intelligent stability control method of a bionic transportation robot according to claim 1, wherein the current state quantity of the bionic transportation robot in step S1 includes a centroid position of the bionic transportation robot and an azimuth and inclination angle of the bionic transportation robot in a three-dimensional space, and further includes a moving speed, a rotating speed and an actual load of the bionic transportation robot on the ground.
3. The intelligent stable control method of the bionic transportation robot according to claim 1, wherein the step S2 includes, in the control process, continuously optimizing and solving, that is, in each moment, according to the state quantity error at the current moment, solving through a quadratic programming problem to obtain the optimal solution of the control quantity when the error between the system output and the reference quantity is minimum, and repeatedly performing the optimization process online.
4. The intelligent stability control method of a bionic transportation robot according to claim 1, wherein the step S2 includes correcting each sampling time by feedback, that is, correcting the predicted result by using the actual output, and then performing the next optimization process.
5. The intelligent stability control method of a bionic transportation robot according to claim 1,3 or 4, wherein the model predicted in step S2 is expressed as a linear state space model assuming a kinetic model of the bionic transportation robot:
Wherein, Is the state vector at time k,Is a control input, and A and B are a state transition matrix and a control input matrix of the system respectively;
the objective function and constraints are defined as:
Wherein, AndRespectively a reference state and a reference control input, Q and R are weight matrixes, and N is a prediction range;
Constraints of the control input are expressed as:
where u is the control input.
6. The intelligent stable control method of a bionic transportation robot according to claim 5, wherein the control error in the step S2 is controlled by PID, and the control law is expressed as:
Wherein, Is the control error of the device and the control method,，AndProportional, integral and derivative gains, respectively.
7. The intelligent stability control method of a biomimetic transport robot according to claim 6, wherein the control model of the combination of the predictive model and the PID control is expressed as:
Wherein, Is based on the main control signal calculated by the predictive control moduleIs a PID control signal that performs fine tuning.
8. An intelligent stable control system of a bionic transportation robot, which is suitable for the intelligent stable control method of the bionic transportation robot according to any one of claims 1 to 7, and is characterized by comprising a sensor unit, a control unit and an execution unit, wherein the control unit comprises a model predictive control module and a PID control module.
9. The intelligent stability control system of a biomimetic transport robot of claim 8, wherein the sensor unit comprises a load detection module and a centroid monitoring module, and further comprises a gesture monitoring module and a speed monitoring module.
10. The intelligent stability control system of a biomimetic transport robot of claim 8, wherein the execution unit utilizes hydraulic fluid as the control system for the energy transfer medium, including hydraulic pumps and proportional valves, and further including components of hydraulic motors and controllers.
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