CN-121993455-A - Iterative learning control method and system for vehicle lifting system

CN121993455ACN 121993455 ACN121993455 ACN 121993455ACN-121993455-A

Abstract

The invention relates to an iterative learning control method and system for a vehicle lifting system, belongs to the technical field of lifting system control, and solves the problems of insufficient control precision and poor robustness in the prior art. The method comprises the steps of obtaining structural parameters of a vehicle lifting system, constructing a vehicle lifting system power model according to the structural parameters, obtaining a vehicle lifting system control model, generating an expected lifting angle track of a vehicle lifting system support frame, constructing an iterative learning control model of the vehicle lifting system, carrying out iterative control based on the iterative learning control model, realizing lifting angle control of the support frame, obtaining control quantity deviation and angle deviation by using the expected lifting angle track and the vehicle lifting system control model when each iteration is carried out, and carrying the control quantity obtained in the previous iteration, the control quantity deviation and the angle deviation obtained in the current iteration into the iterative learning control model to obtain the control quantity of the current iteration.

Inventors

HANG JIE
YIN HAIRONG
Liang Songpeng
WANG HAO
WANG LI
Xiong Dao
LIU JINGYONG

Assignees

北京机械设备研究所

Dates

Publication Date: 20260508
Application Date: 20241106

Claims (10)

1. An iterative learning control method for a vehicle lifting system, the method comprising: acquiring structural parameters of a vehicle lifting system based on a sensor network, and constructing a vehicle lifting system power model according to the structural parameters; Obtaining a vehicle lifting system control model based on the vehicle lifting system power model parameters; Generating an expected lifting angle track of the supporting frame of the vehicle lifting system through a quintic polynomial curve fitting algorithm; Constructing an iterative learning control model of the vehicle lifting system; And in each iteration, based on the measured actual lifting angle of the support frame and the oil inlet and outlet pressure of the lifting oil cylinder, obtaining control quantity deviation and angle deviation by using the expected lifting angle track and the vehicle lifting system control model, and carrying the control quantity obtained in the previous iteration and the control quantity deviation and angle deviation obtained in the current iteration into the iterative learning control model to obtain the control quantity of the current iteration.
2. The method of claim 1, wherein deriving the control amount bias and the angle bias using the desired lift angle trajectory and the vehicle lift system control model based on the measured actual lift angle of the support frame, the lift cylinder inlet and outlet pressure, comprises: Acquiring an expected lifting angle corresponding to the actual lifting angle from the expected lifting angle track; Inputting the expected lifting angle into a vehicle lifting system control model to obtain an expected control amount corresponding to the expected lifting angle; The pressure information of the oil inlet and outlet of the lifting oil cylinder is brought into a control model of a vehicle lifting system to obtain actual control quantity; Obtaining a control amount deviation based on the desired control amount and the actual control amount; Based on the desired lift angle and the actual lift angle to angle deviation.
3. The method according to claim 2, wherein the iterative learning control model is represented by the formula (1): Wherein u k+1 (t) is the control quantity of k+1 iterations, k p and k d are the proportional learning gain and the differential learning gain respectively, and alpha and beta are the error and the compensation gain of control respectively; the change rate of the lift angle deviation is the k iteration, and Deltau k (t) is the control deviation amount of the k iteration.
4. The method of claim 1, wherein the vehicle lift system power model is as shown in equation (2): wherein J is the rotational inertia of the support frame, and q is the lifting angle of the support frame; Lifting the angular velocity for the support frame; Lifting angular acceleration for a support frame, p A 、f B being inlet and outlet pressure of an oil cylinder, fv being viscous friction coefficient, A f being coulomb friction coefficient; At the angle of The normal force of the friction surface is the included angle between the piston rod and the support frame, L 3 is the distance from the hinge point of the support frame to the action point of the lifting cylinder, L 4 is the distance from the hinge point of the support frame to the mass center of the support frame, g is gravity acceleration, m is the mass of the support frame, beta 0 is the initial included angle between the support frame and the horizontal plane, and A A 、A B is the action area of a rodless cavity and a rod-containing cavity of the lifting cylinder; Is a dynamic disturbance.
5. The method of claim 1, wherein the desired lift angle trajectory is as shown in equation (3): q(t)＝q 0 +a 1 (t-t 0 ) 2 +a 2 (t-t 0 ) 2 +a 3 (t-t 0 ) 3 +a 4 (t-t 0 ) 4 +a 5 (t-t 0 ) 5 (3); Where q (t) is the desired lift angle at time t, q 0 is the initial lift angle, a 1 、a 2 、a 3 、a 4 、a 5 is the polynomial coefficient, and t 0 is the initial time point.
6. The method of claim 5, wherein the step of determining the position of the probe is performed, The polynomial coefficient of the desired lift angle trajectory is shown in the calculation formula (4): Wherein v 0 is the initial angular velocity of the support frame, T is the total time of the lifting action, h is the expected total stroke displacement of the lifting action, and v 1 is the expected angular velocity in the total time T of the lifting action.
7. The method according to claim 4, wherein the vehicle lifting system control model is shown in a calculation formula (5); Wherein u k (t) is the control amount at time t.
8. A method according to claim 3, wherein the control gain of the iterative learning control model takes the value: The value of the proportional learning gain is 5, and the value of the differential learning gain is 10; the compensation gain value of the iterative learning control model is as follows: The error compensation gain is 100, and the compensation gain is controlled to be 0.2.
9. The method of claim 1, wherein the sensor network comprises an acceleration sensor, a displacement sensor, a pressure sensor, a flow sensor, a tilt sensor, and a distance sensor.
10. An iterative learning control system for a vehicle lift system, the system comprising: the power model construction module is used for acquiring structural parameters of the vehicle lifting system based on the sensor network, and constructing a power model of the vehicle lifting system according to the structural parameters; the control model construction module is used for obtaining a control model of the vehicle lifting system based on the power model parameters of the vehicle lifting system; The expected lifting angle track module is used for generating an expected lifting angle track of the supporting frame of the vehicle lifting system through a quintic polynomial curve fitting algorithm; The iterative learning control model construction module is used for constructing an iterative learning control model of the vehicle lifting system; The iteration control module is used for receiving the calculated lifting angle instruction of the upper computer, calculating the control quantity deviation and the angle deviation, carrying out iteration control on the iteration learning control model based on the control quantity, the control quantity deviation and the angle deviation obtained in the previous iteration to obtain the control quantity of each iteration, and controlling the electrohydraulic proportional valve in the vehicle lifting system based on the control quantity to realize the lifting angle control of the support frame.

Description

Iterative learning control method and system for vehicle lifting system Technical Field The invention relates to the technical field of control of lifting systems, in particular to an iterative learning control method and system of a vehicle lifting system. Background Accurate control of the special vehicle lifting system is important to ensuring operation safety and improving operation efficiency. With the development of technology, higher requirements are put on the operation precision and response speed of the lifting system. Currently, many vehicle lift systems employ conventional control methods, such as PID control, that are capable of achieving, to some extent, the basic control requirements of the lift operation. However, these conventional methods tend to be difficult to provide adequate control accuracy and robustness in the face of complex dynamic environments and system internal uncertainties. The existing lifting system has the following problems and disadvantages that firstly, the traditional control method is difficult to adapt to the rapid change of the working environment and the dynamic change of system parameters, secondly, the existing system cannot always keep enough control precision when processing complex interference and nonlinear factors, and finally, the control strategy of the existing system lacks the full utilization of real-time data and the dynamic adjustment capability of system parameters, so that the control effect is deviated from the expected value. Disclosure of Invention In view of the above analysis, the embodiment of the invention aims to provide an iterative learning control method and system for a vehicle lifting system, which are used for solving the problems that the existing control precision is insufficient, the robustness is poor and the control effect cannot reach the expected value. In one aspect, an embodiment of the present invention provides a vehicle lifting system iterative learning control method, including: acquiring structural parameters of a vehicle lifting system based on a sensor network, and constructing a vehicle lifting system power model according to the structural parameters; Obtaining a vehicle lifting system control model based on the vehicle lifting system power model parameters; Generating an expected lifting angle track of the supporting frame of the vehicle lifting system through a quintic polynomial curve fitting algorithm; Constructing an iterative learning control model of the vehicle lifting system; And in each iteration, based on the measured actual lifting angle of the support frame and the oil inlet and outlet pressure of the lifting oil cylinder, obtaining control quantity deviation and angle deviation by using the expected lifting angle track and the vehicle lifting system control model, and carrying the control quantity obtained in the previous iteration and the control quantity deviation and angle deviation obtained in the current iteration into the iterative learning control model to obtain the control quantity of the current iteration. As a further improvement of the application, based on the measured actual lifting angle of the support frame and the oil inlet and outlet pressure of the lifting oil cylinder, the control quantity deviation and the angle deviation are obtained by utilizing the expected lifting angle track and the vehicle lifting system control model, and the application comprises the following steps: Acquiring an expected lifting angle corresponding to the actual lifting angle from the expected lifting angle track; Inputting the expected lifting angle into a vehicle lifting system control model to obtain an expected control amount corresponding to the expected lifting angle; The pressure information of the oil inlet and outlet of the lifting oil cylinder is brought into a control model of a vehicle lifting system to obtain actual control quantity; Obtaining a control amount deviation based on the desired control amount and the actual control amount; Based on the desired lift angle and the actual lift angle to angle deviation. As a further improvement of the present application, the iterative learning control model is as shown in the calculation formula (1): Wherein u k+1 (t) is the control quantity of k+1 iterations, k p and k d are the proportional learning gain and the differential learning gain respectively, and alpha and beta are the error and the compensation gain of control respectively; the change rate of the lift angle deviation is the k iteration, and Deltau k (t) is the control deviation amount of the k iteration. As a further improvement of the present application, the vehicle lifting system power model is as shown in the calculation formula (2): wherein J is the rotational inertia of the support frame, and q is the lifting angle of the support frame; Lifting the angular velocity for the support frame; Lifting angular acceleration for a support frame, p A、pB being inlet and outlet pressure of an