Self-adaptive model predictive pressure control method and system for pump control electro-hydraulic actuator

Abstract

The invention provides a self-adaptive model predictive pressure control method and system for a pump control electro-hydraulic actuator. The method comprises the steps that a load elastic coefficient estimated value of a hydraulic system at the moment k is determined according to a recursive least square method with genetic factors; updating a system matrix and an input matrix of a state space model of the hydraulic system according to the estimated value of the load elastic coefficient and the volume of the rodless cavity in the hydraulic system; according to the updated system matrix, the input matrix, the hydraulic cylinder piston rod reference displacement of the hydraulic system, the piston reference speed of the hydraulic system and the reference speed of the permanent magnet synchronous motor, calculating the reference displacement of the permanent magnet synchronous motor; the reference torque of the permanent magnet synchronous motor is determined according to the reference pressure of a hydraulic cylinder rod cavity of the hydraulic system and the constant displacement pump constraint conditions of the permanent magnet synchronous motor and the hydraulic system; and driving the permanent magnet synchronous motor according to the reference torque. According to the method, dynamic performance attenuation caused by model mismatching can be avoided, the response speed can be increased through torque driving, and it is ensured that the direct-drive pump-controlled electro-hydraulic actuator is excellent in performance under different working conditions.

Description

Technical Field

[0001] The present application relates to the technical field of electro-hydraulic servo control, and in particular to an adaptive model predictive pressure control method and system for a pump-controlled electro-hydraulic actuator. Background Art

[0002] Standalone electro-hydraulic actuators are a promising technology that uses variable speed motors and bidirectional servo pumps as the main control elements to regulate the movement of hydraulic cylinders. Compared with traditional valve control systems, these pump-controlled actuators minimize throttling losses by eliminating proportional valves; compared with electromechanical actuators, hydraulic transmission still maintains the advantages of high power / weight ratio, no backlash characteristics and collision absorption ability. Standalone electro-hydraulic actuators can provide higher power-to-weight ratio, better maintainability and stronger robustness. However, the electro-hydraulic servo system is a typical nonlinear system with many nonlinear characteristics and model uncertainties. As the industry has higher and higher requirements for the control performance of electro-hydraulic servo systems, the traditional PID classical control theory has been difficult to meet the needs. Therefore, it is urgent to study more advanced nonlinear control theories for the nonlinear characteristics in electro-hydraulic servo systems. Moreover, in the pump control system, the pressure of the hydraulic circuit, the speed of the pump and motor, and the torque provided by the permanent magnet synchronous motor are all limited by the electromechanical hardware. Compared with the valve control system, the dynamic response is slow, resulting in the dynamic performance attenuation of the electro-hydraulic actuator during use.

[0003] Therefore, there is an urgent need for a control method for regulating an electro-hydraulic actuator, which can avoid the dynamic performance degradation of the electro-hydraulic actuator during use. Summary of the invention

[0004] In order to solve the above technical problems, the present application provides an adaptive model predictive pressure control method and system for a pump-controlled electro-hydraulic actuator to avoid dynamic performance degradation of the electro-hydraulic actuator during use.

[0005] The first aspect of the present application provides an adaptive model prediction pressure control method for a pump-controlled electro-hydraulic actuator, which is applied to an adaptive model prediction pressure control system of a pump-controlled electro-hydraulic actuator. The control system of the voltage actuator includes a permanent magnet synchronous motor and a hydraulic system. The output end of the permanent magnet synchronous motor is connected to the input end of the hydraulic system. The adaptive model prediction pressure control method includes: determining an estimated value of a load elasticity coefficient of the hydraulic system at time k according to a recursive least squares method with genetic factors. Estimated value based on load elasticity coefficient and the rodless chamber volume V1 in the hydraulic system to update the system matrix A and input matrix B of the state space model of the hydraulic system; according to the updated system matrix A, input matrix B, and the reference displacement of the hydraulic cylinder piston rod of the hydraulic system Piston reference speed for hydraulic systems Reference speed ω of permanent magnet synchronous motor * , the reference pressure of the rod chamber of the hydraulic cylinder of the hydraulic system The reference torque of the permanent magnet synchronous motor is determined by the constraints of the permanent magnet synchronous motor and the fixed displacement pump of the hydraulic system. According to the reference torque Drives permanent magnet synchronous motors.

[0006] In some implementations, the estimated value of the load elasticity coefficient of the hydraulic system at time k is determined according to a recursive least squares method with genetic factors. Including: According to the displacement x of the hydraulic cylinder piston rod at time k+2 p(k+2) , hydraulic cylinder piston rod displacement x at time k+1 p(k+1) , hydraulic cylinder piston rod displacement x at time k p(k) , the rodless chamber p of the hydraulic cylinder at time k 1(k) At time k, the hydraulic cylinder has a rod cavity p 2(k) Determine the output vector y of the hydraulic system at time k (k) , the input vector of the hydraulic system The unknown parameter vector θ of the state space model of the hydraulic system is (k) ; Among them, the output vector y of the hydraulic system (k) , the input vector of the hydraulic system The unknown parameter vector θ of the state space model of the hydraulic system (k) satisfy:

[0007] Where, m is the load mass, A a A is the effective area of ​​the piston in the rodless cavity of the hydraulic cylinder, b b is the effective area of ​​the piston in the rod chamber of the hydraulic cylinder, v is the viscous damping coefficient, T s is the sampling period, k s is the load elastic coefficient; according to the output vector y of the hydraulic system (k) , the input vector of the hydraulic system The unknown parameter vector θ of the state space model of the hydraulic system is (k) Determine the estimated value of the load spring coefficient at time k

[0008] In some implementations, according to the output vector y of the hydraulic system (k) , the input vector of the hydraulic system The unknown parameter vector θ of the state space model of the hydraulic system is (k) Determine the estimated value of the load spring coefficient at time k Including: using recursive least squares method with genetic factors, according to the output vector y of the hydraulic system (k) , the input vector of the hydraulic system The unknown parameter vector θ of the state space model of the hydraulic system (k) Determine the observed values ​​of the unknown parameter vector According to the observed values ​​of the unknown parameter vector Sampling period T s and the viscous damping coefficient b v Determine the load spring factor estimate

[0009] In some implementations, the load spring coefficient estimate is determined Previously, the method also includes: determining a state space model of the hydraulic system; the state space model is x is the state variable of the state space model, is the first-order derivative of x, u is the input vector, u=T e , T e is the permanent magnet synchronous motor torque;

[0010]

[0011] C is the output matrix of the state-space model, D is the direct transfer matrix of the state-space model, β e is the effective elastic modulus, V1 is the volume of the rodless chamber of the hydraulic cylinder, C i is the leakage coefficient of the hydraulic cylinder, D p is the pump displacement, J is the moment of inertia, B m is the viscous friction coefficient.

[0012] In some implementations, determining the state space model of the hydraulic system includes: determining the state space model of the hydraulic system based on the permanent magnet synchronous motor motion equation, the hydraulic cylinder force balance equation of the hydraulic system, the quantitative pump flow equation of the hydraulic system, and the load torque of the quantitative pump.

[0013] In some implementations, according to the updated system matrix A, the input matrix B, and the reference displacement of the hydraulic cylinder piston rod of the hydraulic system Piston reference speed for hydraulic systems Reference speed ω of permanent magnet synchronous motor * , the reference pressure of the rod chamber of the hydraulic cylinder of the hydraulic system The reference torque of the permanent magnet synchronous motor is determined by the constraints of the permanent magnet synchronous motor and the fixed displacement pump of the hydraulic system. The method comprises: determining an incremental state space model of the hydraulic system according to an updated system matrix A and an input matrix B; wherein the incremental state space model is:

[0014]

[0015] A ois the system matrix of the incremental state space model, B o is the input matrix of the incremental state space model, C o is the output matrix of the incremental state space model, y k is the output variable of the incremental state space model at time k, z k is the output variable of the incremental state space model at time k, z k+1 is the output variable of the incremental state space model at time k+1, Δu k is the input variable of the incremental state space model; according to the updated system matrix A of the incremental state space model o 、Input matrix B o , output matrix C o , prediction time domain N p and control time domain N c Determine the differential state matrix F and the differential input matrix G of the hydraulic system; determine the relationship between the differential state matrix F, the differential input matrix G, the differential torque increment ΔU and the torque reference increment Y at the next moment; according to the relationship, the differential torque increment ΔU, the reference displacement of the hydraulic cylinder piston rod Piston reference speed for hydraulic systems Permanent magnet synchronous motor reference speed ω * , the reference pressure of the rod chamber of the hydraulic cylinder of the hydraulic system The reference torque of the permanent magnet synchronous motor is determined by the constraints of the permanent magnet synchronous motor and the fixed displacement pump of the hydraulic system.

[0016] In some implementations, according to the relationship, the torque increment ΔU after differentiation, the reference displacement of the hydraulic cylinder piston rod Piston reference speed for hydraulic systems Permanent magnet synchronous motor reference speed ω * , the reference pressure of the rod chamber of the hydraulic cylinder of the hydraulic system The reference torque of the permanent magnet synchronous motor is determined by the constraints of the permanent magnet synchronous motor and the fixed displacement pump of the hydraulic system. The method includes: determining the cost function J1 of the hydraulic system according to the relational expression and the differential torque increment ΔU; wherein the cost function J1 satisfies the constraint conditions of the permanent magnet synchronous motor and the quantitative pump of the hydraulic system, and the constraint conditions are:

[0017]

[0018] ω max is the maximum limit of the mechanical angular velocity of the permanent magnet synchronous motor and the quantitative pump of the hydraulic system, ω(k) is the mechanical angular velocity of the permanent magnet synchronous motor and the quantitative pump of the hydraulic system at time k, Δω(k+1) is the difference between the mechanical angular velocity at time k+1 and time k, T emaxis the maximum torque limit of the permanent magnet synchronous motor, T e (k) is the torque of the permanent magnet synchronous motor at time k, ΔT e (k+1) is the difference between the torque at time k+1 and at time k, r tmax = ΔT e (k+1) maximum limit; minimize the control cost function J1 to obtain the reference torque of the permanent magnet synchronous motor

[0019] In some implementations, the control cost function J1 is minimized to obtain the reference torque of the permanent magnet synchronous motor Including: minimizing the control cost function J1 to determine the optimal differential torque increment ΔU; according to the optimal differential torque increment ΔU, determining the optimal input variable Δu of the incremental state space model k ;in,

[0020] In some implementations, the relationship is Y=Fx k +GΔU;

[0021] in,

[0022]

[0023] C0=[OI], O is an empty matrix; B d =BT s ,

[0024] is the system matrix A after parameter update, and I is the unit vector matrix.

[0025] The first aspect of the present application provides an adaptive model predictive pressure control method for a pump-controlled electro-hydraulic actuator. The pressure control adopts an adaptive model predictive control (AMPC) based on the torque output of a permanent magnet synchronous motor, which can improve the pressure control accuracy and response speed, and the design process of the recursive least squares method with a forgetting factor. The online parameter identification adopts a recursive least squares method with a forgetting factor (FFRLS) to estimate the unknown load elastic coefficient, and the volume parameters of the system are updated in real time. The parameter identification results and the cavity volume parameters are used to update the model parameters, which can avoid the dynamic performance attenuation caused by model mismatch, and the torque drive can improve the response speed.

[0026] The second aspect of the present application provides an adaptive model prediction pressure control system for a pump-controlled electro-hydraulic actuator, which adopts the method provided in the first aspect, and the control system includes: a first determination module, which is configured to determine the load elastic coefficient estimate of the hydraulic system at time k according to a recursive least squares method with genetic factors The update module is configured to estimate the value based on the load elasticity factor and the rodless chamber volume V1 in the hydraulic system to update the system matrix A and the input matrix B of the state space model of the hydraulic system; the second determination module is configured to determine the reference displacement of the hydraulic cylinder piston rod of the hydraulic system according to the updated system matrix A, the input matrix B, and the reference displacement of the hydraulic cylinder piston rod of the hydraulic system Piston reference speed for hydraulic systems Reference speed ω of permanent magnet synchronous motor * , the reference pressure of the rod chamber of the hydraulic cylinder of the hydraulic system The reference torque of the permanent magnet synchronous motor is determined by the constraints of the permanent magnet synchronous motor and the fixed displacement pump of the hydraulic system. The drive module is configured to Drives permanent magnet synchronous motors.

[0027] The adaptive model predictive pressure control system of the pump-controlled electro-hydraulic actuator provided in the second aspect of the present application adopts the adaptive model predictive pressure control method of the pump-controlled electro-hydraulic actuator provided in the first aspect. Therefore, its beneficial technical effects can be referred to the first aspect and will not be repeated here. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] In order to more clearly illustrate the technical solution of the present application, the drawings required for use in the embodiments are briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0029] Figure 1 It is a structural schematic diagram of an adaptive model predictive pressure control system of a pump-controlled electro-hydraulic actuator provided in an embodiment of the present application;

[0030] Figure 2 It is a flow chart of an adaptive model predictive pressure control method for a pump-controlled electro-hydraulic actuator provided in an embodiment of the present application;

[0031] Figure 3 is a schematic diagram of a permanent magnet synchronous motor provided by an embodiment of the present application in rotation states in different quadrants;

[0032] Figure 4 It is a general control block diagram of an adaptive model predictive pressure control system of a pump-controlled electro-hydraulic actuator provided in an embodiment of the present application;

[0033] Figure 5 It is a structural block diagram of an adaptive model predictive pressure control system of a pump-controlled electro-hydraulic actuator provided in an embodiment of the present application.

[0034] Graphic marking:

[0035] 1- elastic load, 2- hydraulic cylinder, 3- overflow valve, 4- hydraulically controlled one-way valve, 5- quantitative pump, 6- permanent magnet synchronous motor, 7- accumulator;

[0036] 100 - control system; 101 - first determination module; 102 - update module; 103 - second determination module; 104 - drive module. DETAILED DESCRIPTION

[0037] The technical solutions in the embodiments of the present application will be described clearly below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, rather than all of the embodiments. Based on the embodiments of the present application, other embodiments obtained by ordinary technicians in this field without making creative work all belong to the protection scope of the present application.

[0038] In the following, the terms "first", "second", etc. are used for descriptive purposes only and are not to be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first", "second", etc. may explicitly or implicitly include one or more of the features. In the description of this application, unless otherwise specified, "plurality" means two or more.

[0039] In addition, in the present application, directional terms such as "upper", "lower", "inner" and "outer" are defined relative to the orientation of the components schematically placed in the drawings. It should be understood that these directional terms are relative concepts. They are used for relative description and clarification, and they can change accordingly according to the changes in the orientation of the components placed in the drawings.

[0040] The application of permanent magnet synchronous motor (PMSM) drives spans across different industrial fields, from electromechanical actuators to electro-hydraulic transmissions. Compared with electromechanical actuators, electro-hydraulic drive mechanisms have significant advantages in high power density, reliability, and robustness. Specifically, electro-hydraulic actuators are the first choice for heavy machinery because they can exert large forces and absorb shocks and collisions. However, the energy efficiency of fluid transmission in traditional electro-hydraulic systems is affected by the throttling losses generated in the fluid control valve. Therefore, pump-controlled hydraulic actuators with smaller throttling losses and higher compactness have been widely studied. Pump-controlled direct-drive servo systems have problems such as slow response rate and insufficient robustness, which hinder their promotion in civilian industries. Pump-controlled direct-drive actuators used in servo stamping and plastic forming CNC machine tools have control model mismatch due to the difficulty in determining the elastic coefficient of the stamping parts and the change of the cavity volume during the motion process, which significantly reduces the dynamic servo performance of the system.

[0041] At present, most pump-controlled direct drive solutions still use variable speed strategies to control system pressure. For pump control system pressure control, the speed is usually combined with pressure feedback to control the system pressure, but it is difficult to achieve high-frequency response and no overshoot requirements at the same time. Traditional control methods (such as Proportion Integral Differential, PID) are difficult to achieve high-precision control for electro-hydraulic servo systems with parameter uncertainty. The accuracy of model predictive control (MPC) depends on the accurate system model of the controlled object, but the model parameters are difficult to obtain in the actual system.

[0042] In order to improve the control accuracy and response speed of the pump-controlled direct-drive electro-hydraulic actuator and avoid dynamic performance degradation caused by model mismatch, the embodiment of the present application adopts adaptive model predictive pressure control (AMPC) of the motor torque output, which can improve the control accuracy and response speed compared with the traditional PID control; compared with the variable speed control, the series control loop is reduced, which can improve the response speed; compared with the traditional model predictive control, the recursive least squares method with forgetting factor (FFRLS) is used to online identify the unknown load elastic coefficient, update the volume parameters of the system in real time, and use the parameter identification results and cavity volume parameters to update the model parameters, which can avoid dynamic performance degradation caused by model mismatch.

[0043] Figure 1 It is a structural schematic diagram of an adaptive model predictive pressure control system of a pump-controlled electro-hydraulic actuator provided in an embodiment of the present application.

[0044] like Figure 1 As shown, the electro-hydraulic actuator mainly uses a variable speed motor and a bidirectional servo pump as the main control elements to adjust the movement of the hydraulic cylinder. The system is a closed pump-controlled direct drive system, including an elastic load 1, an asymmetric hydraulic cylinder 2, a relief valve 3, a hydraulically controlled one-way valve 4, a bidirectional metering pump 5, a permanent magnet synchronous motor 6 and an accumulator 7. The relief valve 3 plays a role in safety protection in the system. The two ends of the bidirectional metering pump 5 are directly connected to the two chambers of the hydraulic cylinder 2. The permanent magnet synchronous motor 6 drives the metering pump 5 to control the flow and pressure of the hydraulic cylinder by adjusting the torque and speed. The asymmetric flow is supplemented by the accumulator 7 and the hydraulically controlled one-way valve 4.

[0045] Figure 2 It is a flow chart of an adaptive model predictive pressure control method for a pump-controlled electro-hydraulic actuator provided in an embodiment of the present application.

[0046] like Figure 2As shown, the method is applied to an adaptive model predictive pressure control system of a pump-controlled electro-hydraulic actuator, the control system includes a permanent magnet synchronous motor and a hydraulic system, the output end of the permanent magnet synchronous motor is connected to the input end of the hydraulic system, wherein the hydraulic system may include the remaining components mentioned above except the permanent magnet synchronous motor 6. The adaptive model predictive pressure control method of a pump-controlled electro-hydraulic actuator provided in the embodiment of the present application can be implemented by the following steps S1 to S5.

[0047] Step S1: Determine the state space model of the hydraulic system.

[0048] In step S1, the state space model of the hydraulic system can be determined according to the motion equation of the permanent magnet synchronous motor, the hydraulic cylinder force balance equation of the hydraulic system, the flow equation of the quantitative pump and the load torque of the quantitative pump.

[0049] 1. Permanent magnet synchronous motor model

[0050] The motion equation of permanent magnet synchronous motor is formula (1):

[0051]

[0052] Where: J is the moment of inertia, ω is the rotor electrical angular velocity, B m is the viscous friction coefficient, T e Motor torque; T L Load torque, t is time, θ is the electrical angular displacement of the rotor.

[0053] 2. Hydraulic subsystem model

[0054] Figure 3 It is a schematic diagram of a permanent magnet synchronous motor provided in an embodiment of the present application in rotation states in different quadrants.

[0055] like Figure 3 As shown, the present invention takes the operation of the permanent magnet synchronous motor and the quantitative pump in quadrants I and III as the research object. Except for the change of the effective area of ​​the hydraulic cylinder, the dynamics of the motion quadrants II and IV are equivalent.

[0056] Mathematical Model of Asymmetric Hydraulic Cylinder

[0057] The hydraulic cylinder force balance equation is expressed as formula (2):

[0058]

[0059] Where: m is the load mass, A a A is the effective area of ​​the piston in the rodless cavity of the hydraulic cylinder, b b is the effective area of ​​the piston in the rod chamber of the hydraulic cylinder, v is the viscous damping coefficient, k sis the load elastic coefficient, F f is the nonlinear load and its disturbance, x p is the displacement of the hydraulic cylinder piston rod, is the hydraulic cylinder piston rod velocity (derivative of piston rod displacement), is the acceleration of the hydraulic cylinder piston rod (the second-order derivative of the piston rod displacement), p1 is the pressure in the hydraulic cylinder rodless chamber, and p2 is the pressure in the hydraulic cylinder rod chamber.

[0060] Ignoring the external leakage of the hydraulic cylinder, the pressure dynamics of the two chambers of the hydraulic cylinder can be modeled as formula (3) to formula (4):

[0061]

[0062] Where: Q1, Q2 are the oil flow rates entering the rodless chamber of the hydraulic cylinder and flowing out of the rod chamber of the hydraulic cylinder, C i is the leakage coefficient of the hydraulic cylinder, V1 and V2 are the volumes of the rodless and rod chambers of the hydraulic cylinder respectively, and V 01 、V 02 are the initial volume of the rodless and rod chambers of the hydraulic cylinder, β e is the effective elastic modulus, is the derivative of the rodless chamber pressure of the hydraulic cylinder, It is the derivative of the rod chamber pressure of the hydraulic cylinder.

[0063] Metering pump flow Q P The equation is formula (5):

[0064] Q p =D p ω(5)

[0065] Where: D p is the displacement of the metering pump.

[0066] Ignoring the transmission efficiency of the metering pump, the load torque T of the metering pump L Formula (6):

[0067] T L =D p p L (6)

[0068] Where: p L is the pressure difference between the two ends of the hydraulic cylinder, p L =p1-p2.

[0069] Assume that the rod cavity pressure p2 = 0, define the state variable From equations (1) to (6), the state space equations of the system are:

[0070]

[0071] The state space model of the hydraulic system is sorted out from formula (7), which is approximately expressed as formula (8):

[0072]

[0073] Where: u is the input vector, u = T e , A is the system matrix of the hydraulic system state space, B is the input matrix of the hydraulic system state space, C is the output matrix of the hydraulic system state space, and D is the direct transfer matrix of the state space model.

[0074]

[0075] The system discrete state space equation obtained by forward Euler discretization of formula (8) is approximately expressed as formula (9):

[0076]

[0077] Where: x k is the state variable at time k, x k+1 is the state variable at time k+1, y k is the output variable at time k, u k is the input vector T at time k e , I is the unit vector matrix.

[0078] Algorithm design part

[0079] Figure 4 It is a general control block diagram of an adaptive model predictive pressure control system of a pump-controlled electro-hydraulic actuator provided in an embodiment of the present application.

[0080] Combination Figure 1 and Figure 4 As shown, the adaptive model predictive pressure control method of the pump-controlled electro-hydraulic actuator provided in the embodiment of the present application mainly includes the online parameter identification design of the recursive least squares method with forgetting factor (FFRLS) and the adaptive model predictive torque output control (AMPC) design of the hydraulic system. Among them, Figure 4 The Q shown in includes Q1 and Q2.

[0081] On-line parameter identification design of recursive least squares method with forgetting factor (FFRLS)

[0082] Step S2: Determine the estimated value of the load elasticity coefficient of the hydraulic system at time k using the recursive least squares method with a forgetting factor

[0083] In the process of multi-parameter identification, the identification equation often faces the problem of lack of rank, which will cause the identification result to be non-unique. In view of this lack of rank problem, considering that the load elastic coefficient is more important than the influence of the change of the hydraulic cylinder area and the viscous damping coefficient, the embodiment of the present application adopts the method of fixing other parameters and only identifying the load elastic coefficient to ensure that the identification equation is full rank. The hydraulic cylinder force balance equation formula (2) is selected as the identification model. The first-order and second-order forward Euler formula (10) is used for discretization.

[0084]

[0085] Wherein, step S2 may include the following steps S21 and S22.

[0086] Step S21: According to the displacement x of the hydraulic cylinder piston rod at time k+2 p(k+2) , hydraulic cylinder piston rod displacement x at time k+1 p(k+1) , hydraulic cylinder piston rod displacement x at time k p(k) , the rodless chamber p of the hydraulic cylinder at time k 1(k) At time k, the hydraulic cylinder has a rod cavity p 2(k) Determine the output vector y of the hydraulic system at time k (k) , the input vector of the hydraulic system The unknown parameter vector θ of the state space model of the hydraulic system is (k) .

[0087] The discretized formula is rewritten into the matrix form of the identification model (12), and the collected x is used through formula (11) p(k+2) 、x p(k+1) 、x p(k) 、p 1(k) 、p 2(k) , calculate y at time k (k) , θ (k) .

[0088]

[0089] Step S22: According to the output vector y of the hydraulic system (k) , the input vector of the hydraulic system The unknown parameter vector θ of the state space model of the hydraulic system is (k) Determine the estimated value of the load spring coefficient at time k

[0090] Among them, step S22 can be implemented by the following steps S221 and step S222.

[0091] Step S221: Using the recursive least squares method, according to the output vector y of the hydraulic system(k) , the input vector of the hydraulic system The unknown parameter vector θ of the state space model of the hydraulic system (k) Determine the observed values ​​of the unknown parameter vector

[0092] Use FFRLS formula (12) and formula (13) to calculate the k-time

[0093]

[0094] Where: e (k) is the prediction error, y (k) is the system output vector, is the input vector, θ (k) is the unknown parameter vector of the model, K (k) is the weight factor, P (k) is the positive definite covariance matrix at time k, P (k-1) is the positive definite covariance matrix at time k-1, λ is the forgetting factor (0<λ<1), and T is the transpose.

[0095] Step S222: Based on the observed value of the unknown vector Sampling period T s , viscous damping coefficient b v Determine the load spring factor estimate

[0096] Use formula (14) to calculate the estimated value of the load spring coefficient

[0097]

[0098] Step S3: Estimated value based on load elastic coefficient And the rodless chamber volume V1 in the hydraulic system update the system matrix A and input matrix B of the discrete state space equation of the hydraulic system.

[0099] The estimated value of the load spring coefficient and the rodless chamber volume V1 of the hydraulic cylinder are used to update the system state space model equation (8) used by AMPC.

[0100] Design of Adaptive Model Predictive Torque Control (AMPC) for Hydraulic System

[0101] Step S4: Based on the updated system matrix A, input matrix B, and the reference displacement of the hydraulic cylinder piston rod of the hydraulic system Piston reference speed for hydraulic systems Reference speed ω of permanent magnet synchronous motor * , the reference pressure of the rod chamber of the hydraulic cylinder of the hydraulic system The reference torque of the permanent magnet synchronous motor is determined by the constraints of the permanent magnet synchronous motor and the fixed displacement pump of the hydraulic system. Among them, the permanent magnet synchronous motor is controlled by an adaptive model predictive pressure control system.

[0102] AMPC is generally in discrete time form. Let the sampling period be T s , a discrete-time state-space model is established using first-order forward Euler. The incremental state equation of the state-space model is derived as:

[0103] Δx k+1 =A d Δx k +B d Δu k (15)

[0104] y k+1 =y k +C d A d Δx k +C d B d Δu k (16)

[0105] Δx k =x k -x k-1 (17)

[0106] Δu k =u k -u k-1 (18)

[0107] Where: B d =BT s , C d =C. is the system matrix A after parameter update. k-1 is the state variable at time k-1, x k is the state variable at time k, u k is the input variable at time k, u k-1 is the input variable at time k-1, Δx k+1 is the difference between the state variables at time k+1 and time k, Δx k is the difference between the state variables at time k and time k-1, Δu k is the difference between the input variable at time k and time k-1, y k is the output variable of the incremental state space model at time k, y k+1is the output variable at time k+1. Compared with conventional MPC which uses a constant input state matrix A, AMPC updates the load elasticity coefficient k in the state matrix in real time. s and cavity volume V1, improving the parameter robustness of MPC.

[0108] Among them, step S4 can be implemented by the following steps S41 to S44.

[0109] Step S41: Determine the incremental state space model of the hydraulic system according to the updated system matrix A and the input matrix B.

[0110] Its incremental state space model can be expressed as:

[0111]

[0112] Where: A o is the system matrix of the incremental state space model, B o is the input matrix of the incremental state space model, C o is the output matrix of the incremental state space model, z k is the state variable of the incremental state space model, Δu k are the input variables of the incremental state space model, for The transpose of .

[0113] C0=[OI]

[0114] , O is an empty matrix.

[0115] Step S42: Based on the updated system matrix A of the incremental state space model o 、Input matrix B o , output matrix C o , prediction time domain N p and control time domain N c Determine the differential state matrix F and the differential input matrix G of the hydraulic system;

[0116] Step S43: Determine the relationship between the differential state matrix F, the differential input matrix G, the differential torque increment ΔU and the torque reference increment Y at the next moment.

[0117] Through the prediction system in the prediction time domain N p The optimal output torque vector is calculated based on the dynamic characteristics of the internal torque vector. Therefore, we can derive the relationship:

[0118] Y=Fx k +GΔU(20)

[0119] Where: Y represents the torque reference increment matrix at the next moment, F represents the state matrix after differentiation, G represents the input matrix after differentiation, and ΔU represents the torque increment after differentiation.

[0120]

[0121]

[0122] Step S44: According to the relationship, the differential torque increment ΔU, and the reference displacement of the hydraulic cylinder piston rod Piston reference speed Permanent magnet synchronous motor reference speed ω * , Reference pressure of the rod chamber of the hydraulic cylinder The reference torque of the permanent magnet synchronous motor is determined by the constraints of the permanent magnet synchronous motor and the fixed displacement pump of the hydraulic system.

[0123] Specifically, step S44 can be implemented by the following steps S441 and S442.

[0124] Step S441: Determine the cost function J1 of the hydraulic system according to the relational expression and the differential torque increment ΔU.

[0125] In the optimal control design, weight coefficients are set for different variables and corresponding factors are penalized. The cost function of model predictive control can be expressed as:

[0126] J1=(R m -Y) T Q(R m -Y)+ΔU T RΔU (21)

[0127] Where: R m Y is the reference vector, Q is the output weighting matrix, and R is the control weighting matrix, where R m Including hydraulic cylinder piston rod reference displacement Piston reference speed Permanent magnet synchronous motor reference speed ω * .

[0128] Q = diag(Q0,Q0,L,Q0);

[0129]

[0130] Where: Q0 is the identity matrix of the reference output weights, is the control weighting coefficient of the hydraulic cylinder piston position, is the control weighting coefficient of the hydraulic cylinder piston speed, is the control weighting coefficient of the controlled pressure of the piston chamber, K ωis the control weighting coefficient of the mechanical angular velocity of the permanent magnet synchronous motor, R0 is the unit of the control input weight, It is the control weighting coefficient of the permanent magnet synchronous motor output torque.

[0131] Step S442: Minimize the control cost function J1 to obtain the reference torque of the permanent magnet synchronous motor

[0132] Among them, step S442 can be implemented by the following steps S442a and step S442b.

[0133] Step S442a: Minimize the control cost function J1 to determine the optimal differential torque increment ΔU;

[0134] Step S442b: Determine the optimal input variable Δu of the incremental state space model according to the optimal differential torque increment ΔU k ;in,

[0135] The given signal of the present invention is: piston position reference signal Piston speed reference value and the motor speed reference ω * and the reference pressure of the hydraulic cylinder rod chamber The hydraulic cylinder has a rod chamber pressure reference signal As a step signal, and ω * It is designed as a pulse signal to provide excitation for state feedback and test the dynamic characteristics of the closed-loop system.

[0136] The advantages of the MPC scheme include the ability to apply control constraints to algorithm development, which represent the physical limitations of the engineering application, and to optimize control performance and actuator energy efficiency through the optimal control algorithm. In the pump control system, the speed of the pump and permanent magnet synchronous motor in the hydraulic circuit and the torque provided by the permanent magnet synchronous motor are limited by the electromechanical hardware. In addition, some pump suppliers recommend that the rotational impact of the pump should be limited to avoid component failure and shortened life. Therefore, in this working state variable, the cost function J1 permanent magnet synchronous motor and hydraulic system quantitative pump constraint, control input and control rate are defined as:

[0137]

[0138] Where: max is the maximum limit of the mechanical angular velocity of the permanent magnet synchronous motor and the quantitative pump of the hydraulic system, ω(k) is the mechanical angular velocity of the permanent magnet synchronous motor and the quantitative pump of the hydraulic system at time k, T emax is the maximum torque limit of the permanent magnet synchronous motor, T e(k) is the torque of the permanent magnet synchronous motor at time k, ΔT e (k+1) is the difference between the torque at time k+1 and at time k, r tmax = ΔT e (k+1) maximum limit.

[0139] Step S5: Based on the reference torque Drives permanent magnet synchronous motors.

[0140] The adaptive model predictive pressure control method of the direct-drive pump-controlled electro-hydraulic actuator provided in the embodiment of the present application adopts the adaptive model predictive control (AMPC) based on the torque output of the permanent magnet synchronous motor for pressure control, which can improve the pressure control accuracy and response speed, and the design process of the recursive least squares method with forgetting factor. The online parameter identification adopts the recursive least squares method with forgetting factor (FFRLS) to estimate the unknown load elastic coefficient, and the volume parameters of the system are updated in real time. The parameter identification results and the cavity volume parameters are used to update the model parameters, which can avoid the dynamic performance degradation caused by model mismatch.

[0141] This control method, based on the adaptive model predictive pressure control method (AMPC) of the permanent magnet synchronous motor torque output and the control architecture built with the forgetting factor recursive least squares method (FFRLS) as the core, is one of the key protection contents. This covers the detailed layout of each functional module, the organic connection between them, and the rigorous execution logic. From the signal acquisition module, data processing and analysis module, control instruction generation module to the execution drive module, each link works closely together to form a complete and efficient control closed loop.

[0142] Implementation methods and processes of innovative methods: The above core innovative methods, namely the specific implementation steps and detailed processes of AMPC and FFRLS, are also key protection objects. This includes the entire process from the initial system initialization settings, to the data acquisition variables and acquisition types during operation, to the selection and construction of the system model when using AMPC for pressure control, prediction steps, controller parameter design, calculation and output of the control quantity, and the initial parameter setting, forgetting factor selection, and specific implementation of the iterative algorithm when using FFRLS for online parameter identification. These implementation methods and processes are the optimal solutions obtained through a large amount of theoretical derivation and repeated simulation experiments. They have extremely high technical content and innovation, ensuring the excellent performance of direct-drive pump-controlled electro-hydraulic actuators under different working conditions.

[0143] Corresponding to the above-mentioned embodiment of the adaptive model predictive pressure control method of the pump-controlled electro-hydraulic actuator, the present application also provides an embodiment of an adaptive model predictive pressure control system of the pump-controlled electro-hydraulic actuator.

[0144] Figure 5It is a structural block diagram of an adaptive model predictive pressure control system of a pump-controlled electro-hydraulic actuator provided in an embodiment of the present application.

[0145] like Figure 5 As shown, the adaptive model predictive pressure control system 100 of the pump-controlled electro-hydraulic actuator includes a first determination module 101 , an update module 102 , a second determination module 103 and a drive module 104 .

[0146] The first determination module 101 is configured to determine the load elastic coefficient estimate of the hydraulic system at time k according to the recursive least square method with genetic factors. That is, the first determining module 101 is used to execute the above step S2.

[0147] The updating module 102 is configured to estimate the value of the load elasticity coefficient The system matrix A and the input matrix B of the state space model of the hydraulic system are updated by using the rodless chamber volume V1 in the hydraulic system. That is, the updating module 102 is used to execute the above step S3.

[0148] The second determination module 103 is configured to determine the hydraulic cylinder piston rod reference displacement of the hydraulic system according to the updated system matrix A, the input matrix B, and the reference displacement of the hydraulic cylinder piston rod of the hydraulic system. Piston reference speed for hydraulic systems Reference speed ω of permanent magnet synchronous motor * , the reference pressure of the rod chamber of the hydraulic cylinder of the hydraulic system The reference torque of the permanent magnet synchronous motor is determined by the constraints of the permanent magnet synchronous motor and the fixed displacement pump of the hydraulic system. That is, the second determining module 103 is used to execute the above step S4.

[0149] The driving module 104 is configured to Driving the permanent magnet synchronous motor. That is, the driving module 104 is used to perform the above step S5.

[0150] The present invention applies model predictive control to the control of permanent magnet synchronous motors and hydraulic systems, making full use of the characteristics of model predictive control, which is flexible in design, quick in response, and applicable to multiple control targets, and designs constraints that must be met for the operation of permanent magnet synchronous motors and pumps. The pressure control uses adaptive model predictive control (AMPC) of the torque output of the permanent magnet synchronous motor to improve control accuracy and response speed.

[0151] The pump-controlled direct-drive actuator used in servo stamping and plastic forming CNC machine tools has a control model mismatch due to the difficulty in determining the elastic coefficient of the stamping parts and the change of the cavity volume during the motion process, which significantly reduces the dynamic servo performance of the system. Online parameter identification uses the recursive least squares method with a forgetting factor to identify the unknown load elastic coefficient online, and uses the parameter identification results and cavity volume parameters to update the model parameters, which can provide an accurate system model for AMPC and improve the parameter robustness of AMPC.

[0152] It should be noted that those skilled in the art will easily think of other embodiments of the present application after considering the specification and practicing the application disclosed herein. The present application is intended to cover any variation, use or adaptation of the present application, which follows the general principles of the present application and includes common knowledge or customary technical means in the art that are not disclosed in the present application.

[0153] It should be understood that the present application is not limited to the precise construction that has been described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof, the true scope being indicated by the present application.

Claims

1. An adaptive model predictive pressure control method for a pump-controlled electro-hydraulic actuator, characterized in that: An adaptive model predictive pressure control system applied to a pump-controlled electro-hydraulic actuator, the control system comprising a permanent magnet synchronous motor and a hydraulic system, the output end of the permanent magnet synchronous motor is connected to the input end of the hydraulic system, and the adaptive model predictive pressure control method comprises: Determine the estimated value of the load elasticity coefficient of the hydraulic system at time k based on the recursive least square method with genetic factors According to the load elastic coefficient estimated value and the rodless chamber volume V1 in the hydraulic system to update the system matrix A and the input matrix B of the state space model of the hydraulic system; According to the updated system matrix A, the input matrix B, and the reference displacement of the hydraulic cylinder piston rod of the hydraulic system The piston reference speed of the hydraulic system The reference speed ω of the permanent magnet synchronous motor * , the hydraulic cylinder of the hydraulic system has a rod chamber reference pressure The reference torque of the permanent magnet synchronous motor is determined by the constraints of the permanent magnet synchronous motor and the quantitative pump of the hydraulic system. According to the reference torque The permanent magnet synchronous motor is driven.

2. The adaptive model predictive pressure control method for a pump-controlled electro-hydraulic actuator according to claim 1, characterized in that: The recursive least square method with genetic factors is used to determine the estimated value of the load elastic coefficient of the hydraulic system at time k. include: According to the displacement x of the hydraulic cylinder piston rod at time k+2 p(k+2) , hydraulic cylinder piston rod displacement x at time k+1 p(k+1) , hydraulic cylinder piston rod displacement x at time k p(k) , the rodless chamber p of the hydraulic cylinder at time k 1(k) At time k, the hydraulic cylinder has a rod cavity p 2(k) Determine the output vector y of the hydraulic system at time k (k) , the input vector of the hydraulic system The unknown parameter vector θ of the state space model of the hydraulic system is (k) ; Wherein, the output vector y of the hydraulic system (k) , the input vector of the hydraulic system The unknown parameter vector θ of the state space model of the hydraulic system is (k) satisfy: Where, m is the load mass, A a A is the effective area of ​​the piston in the rodless cavity of the hydraulic cylinder, b b is the effective area of ​​the piston in the rod chamber of the hydraulic cylinder, v is the viscous damping coefficient, T s is the sampling period, k s is the load elastic coefficient; According to the output vector y of the hydraulic system (k) , the input vector of the hydraulic system The unknown parameter vector θ of the state space model of the hydraulic system is (k) Determine the estimated value of the load spring coefficient at time k 3. The adaptive model predictive pressure control method for a pump-controlled electro-hydraulic actuator according to claim 2, characterized in that: The output vector y of the hydraulic system (k) , the input vector of the hydraulic system The unknown parameter vector θ of the state space model of the hydraulic system is (k) Determine the estimated value of the load spring coefficient at time k include: Using the recursive least square method with genetic factors, according to the output vector y of the hydraulic system (k) , the input vector of the hydraulic system The unknown parameter vector θ of the state space model of the hydraulic system is (k) Determine the observed value of the unknown parameter vector According to the observed value of the unknown parameter vector The sampling period T s and the viscous damping coefficient b v Determine the estimated value of the load spring coefficient 4. The adaptive model predictive pressure control method for a pump-controlled electro-hydraulic actuator according to claim 3, characterized in that: Determine the estimated value of the load spring coefficient Previously, the method also included: determining a state-space model of the hydraulic system; The state space model is x is the state variable of the state space model, is the first-order derivative of x, u is the input vector, u=T e , T e is the permanent magnet synchronous motor torque; C is the output matrix of the state-space model, D is the direct transfer matrix of the state-space model, β e is the effective elastic modulus, V1 is the volume of the rodless chamber of the hydraulic cylinder, C i is the leakage coefficient of the hydraulic cylinder, D p is the displacement of the fixed displacement pump, J is the moment of inertia, B m is the viscous friction coefficient.

5. The adaptive model predictive pressure control method for a pump-controlled electro-hydraulic actuator according to claim 4, characterized in that: Determining a state space model of the hydraulic system includes: The state space model of the hydraulic system is determined according to the motion equation of the permanent magnet synchronous motor, the hydraulic cylinder force balance equation of the hydraulic system, the quantitative pump flow equation of the hydraulic system and the load torque of the quantitative pump.

6. The adaptive model predictive pressure control method for a pump-controlled electro-hydraulic actuator according to claim 5, characterized in that: The updated system matrix A, the input matrix B, and the reference displacement of the hydraulic cylinder piston rod of the hydraulic system The piston reference speed of the hydraulic system The reference speed ω of the permanent magnet synchronous motor * , the hydraulic cylinder of the hydraulic system has a rod chamber reference pressure The reference torque of the permanent magnet synchronous motor is determined by the constraints of the permanent magnet synchronous motor and the quantitative pump of the hydraulic system. include: The incremental state space model of the hydraulic system is determined according to the updated system matrix A and the input matrix B; wherein the incremental state space model is: A o is the system matrix of the incremental state space model, B o is the input matrix of the incremental state space model, C o is the output matrix of the incremental state space model, y k is the output variable of the incremental state space model at time k, z k is the output variable of the incremental state space model at time k, z k+1 is the output variable of the incremental state space model at time k+1, Δu k are the input variables of the incremental state space model; According to the updated system matrix A of the incremental state space model o 、Input matrix B o , output matrix C o , prediction time domain N p and control time domain N c Determining a differential state matrix F and a differential input matrix G of the hydraulic system; Determine a relationship between the differential state matrix F, the differential input matrix G, the differential torque increment ΔU and the torque reference increment Y at the next moment; According to the relationship, the differential torque increment ΔU, and the hydraulic cylinder piston rod reference displacement The piston reference speed of the hydraulic system The permanent magnet synchronous motor reference speed ω * , the hydraulic cylinder of the hydraulic system has a rod chamber reference pressure The reference torque of the permanent magnet synchronous motor is determined by the constraints of the permanent magnet synchronous motor and the quantitative pump of the hydraulic system.

7. The adaptive model predictive pressure control method for a pump-controlled electro-hydraulic actuator according to claim 6, characterized in that: According to the relationship, the differential torque increment ΔU, the hydraulic cylinder piston rod reference displacement The piston reference speed of the hydraulic system The permanent magnet synchronous motor reference speed ω * , the hydraulic cylinder of the hydraulic system has a rod chamber reference pressure The reference torque of the permanent magnet synchronous motor is determined by the constraints of the permanent magnet synchronous motor and the quantitative pump of the hydraulic system. include: The cost function J1 of the hydraulic system is determined according to the relationship and the differential torque increment ΔU; wherein the cost function J1 satisfies the constraint conditions of the permanent magnet synchronous motor and the quantitative pump of the hydraulic system, and the constraint conditions are: ω max is the maximum limit of the mechanical angular velocity of the permanent magnet synchronous motor and the quantitative pump of the hydraulic system, ω(k) is the mechanical angular velocity of the permanent magnet synchronous motor and the quantitative pump of the hydraulic system at time k, T emax is the maximum torque limit of the permanent magnet synchronous motor, T e (k) is the torque of the permanent magnet synchronous motor at time k, ΔT e (k+1) is the difference between the torque at time k+1 and time k, r tmax = ΔT e (k+1) maximum limit; Control the cost function J1 to minimize and obtain the reference torque of the permanent magnet synchronous motor 8. The adaptive model predictive pressure control method for a pump-controlled electro-hydraulic actuator according to claim 7, characterized in that: The control minimizes the cost function J1 to obtain the reference torque of the permanent magnet synchronous motor include: Controlling the cost function J1 to be minimized to determine the optimal differential torque increment ΔU; According to the optimal differential torque increment ΔU, determine the optimal input variable Δu of the incremental state space model k ;in, 9. The adaptive model predictive pressure control method for a pump-controlled electro-hydraulic actuator according to claim 8, characterized in that: The relationship is Y = Fx k +GΔU; in, C0=[OI], O is an empty matrix; B d =BT s , C d =C, is the system matrix A after parameter update, and I is the unit vector matrix.

10. An adaptive model predictive pressure control system for a pump-controlled electro-hydraulic actuator, using the method according to any one of claims 1 to 9, characterized in that: include: The first determination module is configured to determine the load elastic coefficient estimate of the hydraulic system at time k according to the recursive least square method with genetic factors An updating module is configured to estimate a value according to the load elasticity coefficient and the rodless chamber volume V1 in the hydraulic system to update the system matrix A and the input matrix B of the state space model of the hydraulic system; The second determination module is configured to determine the hydraulic cylinder piston rod reference displacement of the hydraulic system according to the updated system matrix A, the input matrix B, and the reference displacement of the hydraulic cylinder piston rod of the hydraulic system. The piston reference speed of the hydraulic system Reference speed ω of permanent magnet synchronous motor * , the hydraulic cylinder of the hydraulic system has a rod chamber reference pressure The reference torque of the permanent magnet synchronous motor is determined by the constraints of the permanent magnet synchronous motor and the quantitative pump of the hydraulic system. A driving module is configured to The permanent magnet synchronous motor is driven.

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