CN108052002A - A kind of intelligent automobile automatic tracking method of improved fuzzy - Google Patents

Abstract

The invention discloses an improved fuzzy PID intelligent car automatic tracking method. The method of the present invention uses the sensor of the smart car to measure the position of the car on the road and the distance from the road. When the smart car is in the middle of the road, the sensor detects that the deviation e at this time is zero, and when the smart car deviates farther from the lane, the deviation e at this time is also greater. When the speed of the smart car deviating from the lane is zero, the rate of change ec of the deviation detected by the sensor at this time is zero. Similarly, when the speed at which the smart car deviates from the lane is greater, the deviation change rate ec at this time is also greater. When the deviation e and the rate of change ec of the deviation are calculated, the final PID value can be calculated by combining with the fuzzy PID algorithm. Compared with common fuzzy PID algorithm, the present invention can reflect road information more accurately and in real time. Make smart car driving smoother and more precise.

Description

An Improved Fuzzy PID Automatic Tracking Method for Intelligent Vehicles

technical field

The invention relates to the technical field of automatic driving of smart cars, in particular to a method for automatic identification of roads and automatic tracking of smart cars, and proposes an improved fuzzy PID automatic tracking method for smart cars.

Background technique

The previous automatic tracking technology of smart cars often used the classic digital PID algorithm. With the increase of road complexity, fuzzy PID method is often used to detect the road, so as to judge the complexity of the road and the size of the deviation of the smart car from the road, and input the signal to the single chip microcomputer through the control algorithm. Therefore, the control system outputs the value of the corner, controls the corner size of the steering gear, and thus controls the automatic tracking of the smart car.

The structure of the PID regulator is simple, and it is easy for engineers and technicians to understand and master. The parameters of PID are easy to adjust, and engineers and technicians have accumulated rich experience in the long-term application of smart cars, and PID control does not require an accurate mathematical model of the controlled object. It has a good control effect on most of the controlled objects. Especially in the process of smart car control, because it is difficult to establish an accurate mathematical model of the control object, it takes a lot of money to identify the model by using modern control theory to analyze and synthesize, so people often use PID regulators and adjust parameters online based on experience.

In process control, the PID controller controlled by the proportional (P), integral (I) and differential (D) of the deviation is the most widely used automatic controller. Its schematic diagram is shown in Figure 1.

PID algorithm can be divided into position type PID and incremental type PID according to its specific realization. In order to reduce the amount of calculation and obtain stable results, we use incremental PID, so the following mainly introduces incremental PID.

In the formula:

e(t) is the system deviation, e(t)=r(t)-c(t);

Kp is the proportional coefficient; Ti is the integral time constant; Td is the differential time constant

In actual calculation, it is necessary to discretize the above formula, replace the differential with the difference, and replace the integral with the sum formula

as follows:

In the formula, K——sampling times, K=0,1,2…; r(k)——given value for the kth time;

c(k)——the actual output value of the kth time; u(k)——the output control value of the kth time;

e(k)—kth deviation; e(k-1)—k-1 deviation;

K _p —proportional coefficient; T _I —integral time constant;

T _D - differential time constant; T - sampling period.

The above formula can be written as:

Among them, Ki=Kp*T/Ti, Kd=Kp*Td/T

Can continue to deduce:

In the formula, Δe(k)=e(k)-e(k-1);

It can be seen from this that if the computer control system adopts a constant sampling period T, once Kp, Ki, and Kd are determined, the control quantity can be obtained only by using the deviation values of the three measurements before and after.

The increase of the proportional coefficient makes the action of the system sensitive, the speed is accelerated, and the steady-state error is reduced. If Kp is too large, the number of oscillations increases and the adjustment time increases. When Kp is too large, the system tends to be unstable. If Kp is too small, the system will slow down. Kp can choose a negative number, which is mainly determined by the characteristics of the actuator, sensor and control object. If the sign of Kc is improperly selected, the object state (pv value) will be farther and farther away from the state of the control target (sv value), and if such a situation occurs, the sign of Kp must be reversed.

Integral action will reduce the stability of the system, small Ti (strong integral action) will make the system unstable, but it can eliminate the steady-state error and improve the control accuracy of the system.

The differential action can improve the dynamic characteristics. When Td is too large, the overshoot will be larger and the adjustment time will be shorter. If Td is too small, the overshoot will be larger and the adjustment time will be longer. Only when Td is appropriate can the overshoot be small and the adjustment time shortened. Therefore, for the automatic tracking system of smart cars, the digital PID system can efficiently control the driving on simple roads, but it is easy to control the delay and rush out of the runway on complex roads.

A control method using the basic idea and theory of fuzzy mathematics . In the traditional control field, the accuracy of the dynamic mode of the control system is the most important key to the quality of the control. The more detailed the system dynamic information, the more accurate the control can be achieved. However, for complex systems, due to too many variables, it is often difficult to describe the dynamics of the system correctly, so engineers use various methods to simplify the dynamics of the system to achieve the purpose of control, but it is not ideal. So try to deal with these control problems with fuzzy control.

Fuzzy controller includes four parts: (1) Fuzzy. The main function is to select the input quantity of the fuzzy controller and convert it into a fuzzy quantity recognizable by the system, which specifically includes the following three steps: first, process the input quantity to meet the requirements of fuzzy control; second, process the input quantity Carry out scale transformation; thirdly, determine the fuzzy language value and corresponding membership function of each input quantity. (2) Rule base. Build a fuzzy rule base based on the experience of human experts. The fuzzy rule base contains many control rules, which is a key step from the actual control experience to the fuzzy controller. (3) Fuzzy reasoning. It mainly realizes reasoning and decision-making based on knowledge. (4) Defuzzification. The main function is to convert the control quantity obtained by inference into control output.

Contents of the invention

The technical problem to be solved by the present invention is: the complexity brought by the fuzzy PID, and the inaccuracy brought by the fuzzy PID which cannot fully reflect the road information.

In view of the above actual situation, the present invention proposes an improved fuzzy PID automatic tracking method for smart cars, a method that uses deviation, and the rate of change of deviation, and fuzzy PID to control the automatic driving of unmanned vehicles. The method in this paper More accurate and real-time response to road information than ordinary fuzzy PID algorithm. Make smart car driving smoother and more precise.

The method of the present invention uses the sensor of the smart car to measure the position of the car on the road and the distance from the road. When the smart car is in the middle of the road, the sensor detects that the deviation e at this time is zero, and when the smart car deviates farther from the lane, the deviation e at this time is also greater. When the speed of the smart car deviating from the lane is zero, the rate of change ec of the deviation detected by the sensor at this time is zero. Similarly, the greater the speed at which the smart car deviates from the lane, the greater the deviation change rate ec at this time. When the deviation e and the rate of change ec of the deviation are calculated, the final PID value can be calculated by combining with the fuzzy PID algorithm. Due to the hysteresis of integral action, we often use fuzzy PD to control the driving of smart cars.

The concrete implementation of the present invention comprises the following steps:

Step (1): Detect the deviation e of each smart car sensor sampling through the sensor, and calculate the change rate ec of the deviation through the deviation e.

specific:

1-1. Obtain the deviation e1 of the smart car's deviation from the track through sensor signal collection.

1-2. After one acquisition period ends, the second signal acquisition is performed to obtain the deviation e2.

1-3. Calculate the deviation change rate ec1=e2-e1 at this time.

1-4. In the same way, the deviation e and deviation change rate ec of each subsequent sampling period can be obtained.

Step (2): Use fuzzy control to calculate the parameters of fuzzy PID. Since the integral link has hysteresis, only the value of fuzzy PD is required. Since the biggest innovation of this paper lies in the algorithm that combines the deviation and the rate of change of the deviation with fuzzy PD, the algorithm of fuzzy PID is briefly described as follows. The schematic diagram of fuzzy control is shown in Figure 2.

specific:

2-1. Fuzzy processing, converting the precise quantity of deviation e into fuzzy quantity, and transforming the input quantity into its respective domain of discourse, and expressing it with the corresponding fuzzy set language value.

2-2. Use the knowledge base, that is, the database and the fuzzy control rule base to write the membership function, and the rules of the fuzzy controller are established based on experience.

2-3. Carry out fuzzy reasoning, complete fuzzy reasoning according to the input fuzzy quantity and knowledge base, and solve the fuzzy relationship equation, so as to obtain the dog function part of the fuzzy control quantity.

2-4. Carry out clear processing, the output obtained through fuzzy decision-making is fuzzy quantity, which must be converted into precise quantity through clearing in order to control, and finally use the principle of weighted average to calculate the parameters fuzzy P and fuzzy D.

Step (3): Use the deviation e and deviation change rate ec obtained in step (1) and the fuzzy P and fuzzy D values obtained in step (2) to perform algorithm fusion to obtain a more accurate tracking algorithm. Since the deviation e represents The current situation of the vehicle deviating from the road, the rate of change ec of the deviation represents the speed of the current vehicle deviating from the road, so this algorithm improves the real-time performance of the ordinary fuzzy PID, reflects the current road conditions, and makes up for the fuzzy PID complexity and inaccuracies. The flowchart of this method is shown in Fig.

specific:

3-1. Step (1) Obtain the deviation e and the rate of change ec of the deviation.

3-2. Step (2) to obtain blur P and blur D.

3-3. Algorithm fusion to obtain the final control value Value=e*(P+p)/a+ec*(D+d)/b. Among them, Value is the output value obtained by the control system, where e*(P+p) reflects the influence of the current vehicle deviation on the control system, and ec*(D+d) reflects the impact of the current vehicle deviation change rate on the control system Influenced by the effect, this algorithm more represents the current value of the vehicle offset, making up for the inaccuracy of fuzzy PID, where a and b are the coefficients of P and D, representing the weight of the deviation and the deviation change rate, a and b The size of the car can only be given a specific value after experience debugging and the performance of the car. p and d represent the minimum basic P and D values.

Beneficial effects of the present invention:

The method of the present invention only needs to use the sensor information to collect the current deviation value of the vehicle and calculate the value of the change rate of the deviation. And use the fuzzy PID to solve the fuzzy P and fuzzy D values. The values obtained by the two are fused by algorithm to calculate the output value of the control system. This method greatly improves the inaccuracy of ordinary fuzzy PID for road segmentation, and greatly improves the control sensitivity of smart cars. Reduced the complexity of parameter tuning for fuzzy PID.

Description of drawings

Figure 1 is a schematic diagram of the PID controller.

Figure 2 is a schematic diagram of fuzzy control.

Figure 3 is a flowchart of the method.

Detailed ways

The present invention will be described in detail below in combination with specific embodiments.

The implementation process of an improved fuzzy PID intelligent vehicle automatic tracking method proposed by the present invention is shown in FIG. 3 . The method of the present invention comprises the following steps:

Step (1): Detect the deviation e of each smart car sensor sampling through the sensor, and calculate the change rate ec of the deviation through the deviation e. Through sensor signal collection, the deviation e1 of the smart car's deviation from the track is obtained. When a collection cycle ends, the second signal collection is performed to obtain the deviation e2. Then the deviation change rate ec1=e2-e1 at this time can be calculated. Similarly, the deviation e and deviation change rate ec of each subsequent sampling period can be obtained.

Step (2): Use fuzzy control to calculate the parameters of fuzzy PID. Since the integral link has hysteresis, only the value of fuzzy PD is required. Since the biggest innovation of this paper lies in the algorithm that combines the deviation and the rate of change of the deviation with fuzzy PD, the algorithm of fuzzy PID is briefly described as follows. The schematic diagram of fuzzy control is shown in Figure 2. Fuzzy processing, transforming the precise quantity of deviation e into fuzzy quantity, and transforming the input quantity into their respective domains of discourse, and expressing it with the corresponding fuzzy set language value. The membership function is written with the knowledge base, that is, the database and the fuzzy control rule base, and the rules of the fuzzy controller are established based on experience. Carry out fuzzy reasoning, complete the fuzzy reasoning according to the input fuzzy quantity and knowledge base, and solve the fuzzy relationship equation, so as to obtain the dog function part of the fuzzy control quantity. Carry out clear processing, the output obtained through fuzzy decision-making is a fuzzy quantity, which must be converted into a precise quantity through clearing for control, and finally use the principle of weighted average to calculate the parameters fuzzy P, and fuzzy D.

Step (3): Use the deviation e obtained in step (1) and the rate of change ec of the deviation and the fuzzy P obtained in step (2) to perform algorithm fusion with the value of fuzzy D to obtain a more accurate tracking algorithm. Since the deviation e It represents the current situation that the car deviates from the road, and the rate of change ec of the deviation represents the speed at which the current car deviates from the road. Therefore, this algorithm improves the real-time performance of ordinary fuzzy PID, reflects the current road conditions, and makes up for the fuzzy Complexity and inaccuracy of PID. The flow chart of this method is shown in Figure 3. The deviation e and the rate of change ec of the deviation are obtained through step (1). Obtain fuzzy P and fuzzy D through step (2). Algorithm fusion results in the final control value Value=e*(P+p)/a+ec*(D+d)/b. Among them, Value is the output value obtained by the control system, where e*(P+p) reflects the influence of the deviation of the current car on the function of the control system, and ec*(D+d) reflects the effect of the change rate of the deviation of the current car on the control system Influenced by the effect, this algorithm more represents the current value of the vehicle offset, making up for the inaccuracy of fuzzy PID, where a and b are the coefficients of P and D, representing the weight of the deviation and the deviation change rate, a and b The size of the car can only be given a specific value after experience debugging and the performance of the car. p and d represent the minimum basic P and D values.

After completing the above steps, the intelligent car automatic tracking side of an improved fuzzy PID of the present invention has just been completed. This method greatly improves the inaccuracy of ordinary fuzzy PID for road segmentation, and greatly improves the control sensitivity of smart cars. Reduced the complexity of parameter tuning for fuzzy PID.

Claims (1)

1. an intelligent car automatic tracking method of an improved fuzzy PID, is characterized in that comprising the steps: Step (1): Detect the deviation e of each smart car sensor sampling through the sensor, and calculate the rate of change ec of the deviation through the deviation e; specific: 1-1. Obtain the deviation e1 of the smart car's deviation from the track through sensor signal collection; 1-2. When a collection cycle ends, the second signal collection is performed to obtain the deviation e2; 1-3. Calculate the deviation change rate ec1=e2-e1 at this time; 1-4. In the same way, the deviation e and deviation change rate ec of each subsequent sampling period can be obtained; Step (2): Use fuzzy control to calculate the parameters of fuzzy PID; because the integral link has a hysteresis effect, only the value of fuzzy PD is required; since the biggest innovation of this paper lies in the algorithm that combines the deviation and the change rate of the deviation with fuzzy PD , so the algorithm of fuzzy PID is briefly described as follows: 2-1. Fuzzy processing, converting the precise quantity of the deviation e into a fuzzy quantity, performing scale transformation on the input quantity, transforming it into its respective domain, and expressing it with the corresponding fuzzy set language value; 2-2. Use the knowledge base, that is, the database and the fuzzy control rule base to write the membership function, and the rules of the fuzzy controller are established based on experience; 2-3. Carry out fuzzy reasoning, complete fuzzy reasoning according to the input fuzzy quantity and knowledge base, and solve the fuzzy relational equation, so as to obtain the dog function part of the fuzzy control quantity; 2-4. Carry out clear processing, the output obtained through fuzzy decision-making is a fuzzy quantity, which must be converted into a precise quantity through clearing for control, and finally use the principle of weighted average to calculate the parameters fuzzy P and fuzzy D; Step (3): use the deviation e obtained in step (1) and the rate of change ec of the deviation and the values of fuzzy P and fuzzy D obtained in step (2) to perform algorithmic fusion to obtain a more accurate tracking algorithm; specific: 3-1. Step (1) obtains the deviation e and the rate of change ec of the deviation; 3-2. Step (2) obtains fuzzy P and fuzzy D; 3-3. Algorithm fusion to get the final control value Value=e*(P+p)/a+ec*(D+d)/b; where Value is the output value obtained by the control system, where e*(P+p ) reflects the influence of the current deviation of the vehicle on the function of the control system, and ec*(D+d) reflects the influence of the change rate of the current deviation of the vehicle on the function of the control system. This algorithm is more representative of the current value of the deviation of the vehicle, making up for In order to avoid the inaccuracy of fuzzy PID, a and b are the coefficients of P and D, which represent the weight of the deviation and the deviation change rate. The size of a and b can only be given a specific value after empirical debugging and the performance of the car; p and d represent the minimum basic P and D values.