CN117519133B - Unmanned cotton picker track tracking control method - Google Patents
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Abstract
The invention discloses an unmanned cotton picker track tracking control method based on total disturbance instant observation and model prediction, which comprises the following steps of calculating a distance error and a heading error of an unmanned cotton picker according to a target running track of the unmanned cotton picker to obtain a pre-aiming point coordinate; establishing an error kinematic model of the vehicle, introducing distance error disturbance and heading error disturbance through the error kinematic model of the vehicle, and establishing a total disturbance observer so as to estimate the total disturbance of the system of the unmanned cotton picker; the model predictive controller calculates feedback control quantity according to the distance error, the heading error and the total disturbance of the system; the TD differential tracker calculates feedforward control quantity according to the coordinate of the pre-aiming point; and adding the feedback control quantity and the feedforward control quantity to obtain a total control quantity, multiplying the total control quantity by the gain of the controller to obtain a target steering wheel angle, and sending the target steering wheel angle to a steering wheel angle controller for closed-loop control. The invention has better control comprehensive performance.
Description
Technical Field
The invention relates to the technical field of unmanned, in particular to an unmanned cotton picker track tracking control method based on total disturbance instant observation and model prediction.
Background
Cotton is widely planted worldwide as one of the most important commercial crops in the world. However, the large-area cotton harvesting work often requires higher labor cost, and a driver with insufficient experience is unreasonable in speed and direction control, and can also cause the problems of cotton picking head blockage, cotton picking of a cotton picker and the like. Therefore, the unmanned cotton picker capable of accurately tracking the target picking travel track has great potential for improving the quality and efficiency of cotton picking operation.
For this reason, miao Zhonghua et al at Shanghai university developed a study on the automatic alignment auxiliary driving control technique of cotton picker by using a proportional-integral-derivative (PID) control algorithm of speed parameter adaptation. However, since the state of the vehicle varies with time and the road condition has uncertainty, it is difficult for the PID controller to maintain optimal control quality under different conditions and it is not generalized to the tracking control of the picker steering. For this reason, model-based trajectory tracking control algorithms with better adaptability and control accuracy have been widely studied. Algorithms such as a pretightening method, model predictive control, adaptive control and the like are widely studied in the field of traditional passenger vehicles. The result of the pure tracking method adopted by students to design the track tracking controller shows that the algorithm has better tracking effect on paths with smaller curvatures, and the tracking precision is reduced on road sections with larger curvatures because the pre-aiming point passes through part of points. The track tracking controller based on model prediction can be designed by using the dynamic model of the vehicle, and experimental conclusion proves that the method has better control effect than the traditional control algorithm. However, because the operation pavement condition of the cotton picker and the structure thereof are complex, if a too complex prediction model is used, the calculation force of the calculation unit is insufficient, and when the control based on a simple model is simply adopted, the deviation between the model and a real object is easy to cause the deterioration of the control quality.
In summary, a control algorithm with better anti-interference performance and higher control effect is needed for the track tracking control problem of the unmanned cotton picker.
Disclosure of Invention
The invention aims at solving the technical defects existing in the prior art and provides an unmanned cotton picker track tracking control method based on total disturbance instant observation and model prediction.
The technical scheme adopted for realizing the purpose of the invention is as follows:
An unmanned cotton picker track tracking control method based on total disturbance instant observation and model prediction comprises the following steps:
step 1, calculating a distance error e d and a heading error e θ of the unmanned cotton picker according to a target running track of the unmanned cotton picker, and acquiring a pre-aiming point coordinate (X p,Yp);
Step 2, simplifying the unmanned cotton picker into a bicycle with 2 degrees of freedom, obtaining a bicycle kinematic model, and establishing an error kinematic model of the vehicle;
step 3, introducing distance error disturbance and heading error disturbance through an error kinematic model of the vehicle, and establishing a total disturbance observer so as to estimate the total disturbance of the system of the unmanned cotton picker, wherein the total disturbance of the system comprises the total disturbance of the distance error And heading error total disturbance/>
Step 4, the model predictive controller calculates feedback control quantity u FB according to the distance error e d, the heading error e θ obtained in the step 1 and the total disturbance of the system obtained in the step 3;
step 5, the TD differential tracker calculates a feedforward control quantity u FF according to the pre-aiming point coordinate obtained in the step 1;
And 6, adding the feedback control quantity u FB obtained in the step 4 and the feedforward control quantity u FF obtained in the step 5 to obtain a total control quantity u, multiplying the total control quantity u by the controller gain 1/b0 to obtain a target steering wheel angle delta wr, and sending the target steering wheel angle delta wr to a steering wheel angle controller for closed-loop control.
In the above technical solution, in the step 1, a point closest to the current actual position on the target track is found by using a dichotomy, and a distance error e d and a heading error e θ of the unmanned cotton picker are calculated, and meanwhile, the pre-aiming distance is taken as a radius, and an intersection point between the pre-aiming distance and the target track in the vehicle advancing direction is found as a pre-aiming point.
In the above technical solution, in the step 2, the bicycle kinematic model is:
wherein v is the vehicle speed, L is the cotton picker wheelbase, delta is the tire rotation angle, (x, y) is the vehicle coordinate, and theta is the vehicle course angle;
error kinematic model of vehicle:
Wherein θ r is the heading angle of the target track point, and u is the total control quantity.
In the above technical solution, in the step 3, the total disturbance observer is:
Wherein: And/> Estimated values of distance error and heading error, respectively,/>And/>The estimated value of the total disturbance of the distance error and the heading error is respectively, beta 1、β2、β3、β4 is the gain of the observer, and u FB and u FF are the feedback control quantity and the feedforward control quantity of the controller respectively.
In the above technical solution, in the step 4, the prediction model in the model prediction controller is:
Wherein: U＝uFB；
representing the sum of the known disturbance and the unknown disturbance.
In the above technical solution, the cost function of the model predictive controller during iterative solution is: Gain coefficient matrices in which Q and R are cost functions, respectively, are denoted/> R= [ R ], where q 1、q2, R are gain coefficients of the cost function set by the simulation tuning, q 1 reflects the weight of e d, q 2 reflects the weight of e θ, and R reflects the weight of u FB.
In the above-described technical solution, the feedforward control amount u FF＝Lκp, where k p represents the pretighted spot curvature,Wherein/>And/>Representing the first derivative of the pretightening point coordinate X p、Yp with respect to time,/>Representing the second derivative of the pretightening point coordinate X p、Yp with respect to time.
In the above technical solution, the TD differential tracker in step 5 is:
Wherein fhan is the fastest control integrated function, h 0 is the filter factor, h is the step size, r is the fast factor, v is the first derivative of the input signal X p,x1 is the filtered X p or Y p,x2 is X p Or the first derivative of Y p/>
Compared with the prior art, the invention has the beneficial effects that:
1. According to the invention, a distance error disturbance and a heading error disturbance are introduced through an error kinematic model, and a total disturbance observer is designed, so that the total disturbance of the system of the unmanned cotton picker is estimated. The method mainly comprises a distance error disturbance observer and a course error disturbance observer, and the disturbance is introduced into a model predictive controller, so that the disturbance immunity of the model predictive controller to the disturbance caused by changeable road conditions is improved.
2. The model predictive controller calculates the feedback control quantity according to the distance error, the heading error and the total disturbance of the system. The vehicle error kinematic model combining the distance error disturbance and the course error disturbance is used as a prediction model to calculate the state quantity in a period of time in the future, and the optimal feedback control quantity u FB can be solved on line by using a generalized residual error minimum algorithm through the constructed cost function because the problem is converted into a quadratic programming solution problem at the moment. The method integrally considers the distance error, the heading error and the disturbance algorithm of the vehicle, avoids the problem of non-uniform control effect of the distance error and the heading error in layered control, improves the tracking precision of the control algorithm in a large curvature section of the vehicle, and solves the problem that the cotton picker is difficult to accurately re-walk when re-entering the ridge after turning around.
3. The invention calculates a feedforward control quantity according to the coordinate of the pre-aiming point through a TD differential tracker, wherein the feedforward control quantity is represented as u FF＝Lκp, and kappa p represents the curvature of the pre-aiming point. Obtaining the differential signal by the fastest discrete TD differential tracker can better avoid noise effects. Then, the feedforward control quantity is introduced into the model predictive controller, so that the robustness and the response speed of the control system can be improved.
In conclusion, the unmanned cotton picker track tracking control algorithm based on total disturbance instant observation and model prediction provided by the invention has better control comprehensive performance, and solves the problem of poor alignment accuracy after the cotton picker is tracked by a large-curvature road section track under the condition of complex roads.
Drawings
Fig. 1 is a diagram showing the overall structure of the control algorithm of the present invention.
Detailed Description
The present invention will be described in further detail with reference to specific examples. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
An unmanned cotton picker track tracking control method based on total disturbance instant observation and model prediction comprises the following steps:
Step 1, finding a point closest to the current actual position on a target track by using a dichotomy according to the target running track of the unmanned cotton picker, calculating a distance error e d and a heading error e θ of the unmanned cotton picker, simultaneously taking a positioning center as a circle center and a pre-aiming distance as a radius, finding an intersection point between the vehicle advancing direction and the target track as a pre-aiming point, and then obtaining a coordinate (X p,Yp) of the pre-aiming point.
Step2, simplifying the unmanned cotton picker into a bicycle with 2 degrees of freedom, and obtaining a bicycle kinematics model:
Wherein v is the vehicle speed, L is the cotton picker wheelbase, delta is the tire rotation angle, (x, y) is the vehicle coordinates, and theta is the vehicle course angle.
Then, in order to simplify the controller design, the error kinematic model of the vehicle is obtained by combining the target track point information:
Wherein θ r is the heading angle of the target track point.
And 3, designing a total disturbance observer through an error kinematic model of the vehicle and introducing distance error disturbance and heading error disturbance, so as to estimate the total disturbance of the system of the unmanned cotton picker. The total disturbance observer mainly comprises a distance error disturbance observer and a course error disturbance observer, and the disturbance is introduced into the model predictive controller so as to improve the disturbance resistance and the control precision of the model predictive controller.
The total disturbance observer is:
Wherein: And/> Estimated values of distance error and heading error, respectively,/>And/>The estimated value of the total disturbance of the distance error and the heading error is respectively, beta 1、β2、β3、β4 is the gain of the observer, and u FB and u FF are the feedback control quantity and the feedforward control quantity of the controller respectively.
And 4, calculating a feedback control quantity u FB by the model predictive controller according to the distance error, the heading error and the total disturbance of the system. The model prediction controller uses a vehicle error kinematic model combining distance error disturbance and heading error disturbance as a prediction model to calculate state quantity in a future period, and the model prediction controller converts the problem into a quadratic programming solution problem through a constructed cost function, so that the optimal feedback control quantity u FB can be solved on line by using a generalized residual error minimum algorithm, and accurate tracking control of the vehicle can be realized.
The prediction model in the model prediction controller is as follows:
Wherein: U=u FB, namely:
Wherein: Representing the sum of the known disturbance and the unknown disturbance, L is the picker wheelbase.
The cost function of the model predictive controller in iterative solution is as follows: Gain coefficient matrices in which Q and R are cost functions, respectively, are denoted/> R= [ R ], where q 1、q2, R are gain coefficients of the cost function set by the simulation tuning, q 1 reflects the weight of e d, q 2 reflects the weight of e θ, and R reflects the weight of u FB. The cost function is a certain weight coefficient, and the state quantity e d、eθ and the control quantity u FB are unified into the cost function, so that the model predictive controller is guided to make an optimal decision at each control moment, and the optimal control target of the system is realized.
In step 5, the td differential tracker calculates a feedforward control amount according to the pre-aiming point coordinates, where the feedforward control amount is denoted as u FF＝Lκp, and k p represents the curvature of the pre-aiming point.Wherein/>And/>Representing the first derivative of the pretightening point coordinate X p、Yp with respect to time,/>Representing the second derivative of the pretightening point coordinate X p、Yp with respect to time. The TD differential tracker comprises:
Wherein fhan is the fastest control integrated function, h 0 is the filter factor, h is the step size, r is the fast factor, v is the first derivative of the input signal X p,x1 is the filtered X p or Y p,x2 is X p Or the first derivative of Y p/>I.e./>, eliminating noise effects can be obtained directly by the above TD differential tracker
And 6, adding the feedback control quantity u FB obtained in the step 4 and the feedforward control quantity u FF obtained in the step 5 to obtain a total control quantity u, multiplying the total control quantity u by the controller gain 1/b0 set by simulation parameter adjustment to obtain a target steering wheel angle delta wr, and sending the target steering wheel angle delta wr to a steering wheel angle controller to perform closed-loop control. Thereby improving the adaptability of the algorithm to uncertainty such as vehicle state change, road condition change and the like.
The foregoing is merely a preferred embodiment of the present invention and it should be noted that modifications and adaptations to those skilled in the art may be made without departing from the principles of the present invention, which are intended to be comprehended within the scope of the present invention.
Claims (9)
1. The unmanned cotton picker track tracking control method based on total disturbance instant observation and model prediction is characterized by comprising the following steps of:
Step 1, calculating a distance error e d and a heading error e θ of the unmanned cotton picker according to a target running track of the unmanned cotton picker, and acquiring a pre-aiming point coordinate (X p,Yp);
Step 2, simplifying the unmanned cotton picker into a bicycle with 2 degrees of freedom, obtaining a bicycle kinematic model, and establishing an error kinematic model of the vehicle;
step 3, introducing distance error disturbance and heading error disturbance through an error kinematic model of the vehicle, and establishing a total disturbance observer so as to estimate the total disturbance of the system of the unmanned cotton picker, wherein the total disturbance of the system comprises the total disturbance of the distance error And heading error total disturbance/>In the step 3, the total disturbance observer is:
Wherein: And/> Estimated values of distance error and heading error, respectively,/>And/>The estimated value of total disturbance of the distance error and the course error is respectively, beta 1、β2、β3、β4 is the gain of an observer, u FB and u FF are respectively the feedback control quantity and the feedforward control quantity of the controller, and v is the vehicle speed;
Step 4, the model predictive controller calculates feedback control quantity u FB according to the distance error e d, the heading error e θ obtained in the step 1 and the total disturbance of the system obtained in the step 3;
step 5, the TD differential tracker calculates a feedforward control quantity u FF according to the pre-aiming point coordinate obtained in the step 1;
And 6, adding the feedback control quantity u FB obtained in the step 4 and the feedforward control quantity u FF obtained in the step 5 to obtain a total control quantity u, multiplying the total control quantity u by the controller gain 1/b0 to obtain a target steering wheel angle delta wr, and sending the target steering wheel angle delta wr to a steering wheel angle controller for closed-loop control.
2. The unmanned cotton picker track tracking control method based on total disturbance instant observation and model prediction according to claim 1, wherein in the step 1, a point closest to a current actual position on a target track is found by using a dichotomy, a distance error e d and a heading error e θ of the unmanned cotton picker are calculated, and meanwhile, a positioning center is taken as a circle center, a pre-aiming distance is taken as a radius, and an intersection point between the vehicle advancing direction and the target track is found as a pre-aiming point.
3. The unmanned cotton picker trajectory tracking control method based on total disturbance instant observation and model prediction according to claim 1, wherein in the step 2, the bicycle kinematics model is:
Wherein v is the vehicle speed, L is the cotton picker wheelbase, delta is the tire rotation angle, (x, y) is the vehicle coordinates, and theta is the vehicle course angle.
4. The unmanned cotton picker trajectory tracking control method based on total disturbance instant observation and model prediction according to claim 1, wherein in the step 2, an error kinematic model of the vehicle:
wherein, theta r is the heading angle of the target track point, u is the total control quantity, theta is the heading angle of the vehicle, v is the vehicle speed, and L is the axle distance of the cotton picker.
5. The unmanned cotton picker trajectory tracking control method based on total disturbance instant observation and model prediction according to claim 1, wherein in the step 4, the prediction model in the model prediction controller is:
Wherein: U＝uFB；
Representing the sum of known disturbance and unknown disturbance, v is the vehicle speed, L is the axle base of the cotton picker, u FB is the feedback control quantity of the controller, and theta r is the heading angle of the target track point.
6. The unmanned cotton picker trajectory tracking control method based on total disturbance instant observation and model prediction according to claim 5, wherein the cost function of the model prediction controller in iterative solution is: Gain coefficient matrices in which Q and R are cost functions, respectively, expressed as Wherein q 1、q2 and R are gain coefficients of a cost function set by simulation tuning, q 1 reflects the weight of e d, q 2 reflects the weight of e θ, R reflects the weight of u FB, u FB is a feedback control amount of the controller, and R is a gain coefficient matrix of the cost function.
7. The unmanned cotton picker trajectory tracking control method based on total disturbance point-of-care observation and model prediction according to claim 1, wherein the feedforward control quantity u FF＝Lκp, wherein κ p represents the pretightening point curvature.
8. The unmanned cotton picker trajectory tracking control method based on total disturbance instant observation and model prediction according to claim 7,Wherein/>And/>Representing the first derivative of the pretightening point coordinate X p、Yp with respect to time,/>Representing the second derivative of the pretightening point coordinate X p、Yp with respect to time, κ p represents the pretightening point curvature.
9. The unmanned cotton picker trajectory tracking control method based on total disturbance instant observation and model prediction as claimed in claim 1, wherein the TD differential tracker in step 5 is:
Wherein fhan is the fastest control integrated function, h 0 is the filter factor, h is the step size, r is the fast factor, v is the first derivative of the input signal X p,x1 is the filtered X p or Y p,x2 is X p Or the first derivative of Y p/>
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