Hydraulic system intelligent control method considering full-state performance constraint

Abstract

The invention discloses a hydraulic system intelligent control method considering full-state performance constraints, and the method fuses a radial basis function neural network real-time learning technology, innovatively introduces full-state preset performance function transformation, and designs a nonlinear controller considering the full-state performance constraints. Aiming at the problem of position tracking control of the hydraulic system, the method can ensure that the full-state transient performance and the steady-state performance of the hydraulic system are converged to a preset performance index range, ensures safe and reliable operation of the hydraulic system, and can learn the unknown dynamic state of the system in real time by using the radial basis function neural network to realize high-precision motion control performance. And the problem of differential explosion in traditional backstepping control of the hydraulic system can be avoided, and the influence of measurement noise on control precision is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of electromechanical servo control, and in particular to a hydraulic system intelligent control method considering full-state performance constraints (FSPPIC). Background Art

[0002] Hydraulic systems, with their high power density, high force / torque output, and fast dynamic response, play a crucial role in applications such as robotics, heavy machinery, and high-performance load testing equipment. Hydraulic systems are typically nonlinear systems, characterized by numerous nonlinear characteristics and modeling uncertainties. These nonlinear characteristics include input nonlinearities such as hysteresis and saturation, proportional servo valve flow and pressure nonlinearities, and friction nonlinearities. Modeling uncertainties include parameter uncertainty and uncertainty nonlinearities. Parameter uncertainty primarily relates to load mass, actuator viscous friction coefficient, leakage coefficient, proportional servo valve flow gain, and hydraulic oil elastic modulus. Uncertain nonlinearities primarily include unmodeled friction dynamics, high-order system dynamics, external disturbances, and unmodeled leakage. As hydraulic systems evolve toward higher precision and higher frequency response, the impact of these nonlinear characteristics on system performance becomes increasingly significant. Furthermore, modeling uncertainty can lead to instability or degradation of the order of controllers designed based on the nominal system model. Therefore, the nonlinear characteristics and modeling uncertainty of hydraulic systems are significant factors limiting system performance. With the continuous advancement of technology in the industrial and defense sectors, controllers designed based on traditional linear theory are no longer able to meet the high-performance requirements of these systems. Therefore, it is imperative to develop more advanced nonlinear control strategies tailored to the nonlinear characteristics of hydraulic systems.

[0003] Many methods have been proposed to address the nonlinear control issues in hydraulic systems. Adaptive control methods are very effective for dealing with parameter uncertainty and can achieve asymptotic tracking steady-state performance. However, they are inadequate for uncertain nonlinearities such as external load disturbances. Excessively large uncertain nonlinearities can cause system instability. Actual hydraulic systems all have uncertain nonlinearities, so adaptive control methods cannot achieve high-precision control performance in practical applications. Classical sliding mode control, as a robust control method, can effectively handle any bounded modeling uncertainty and achieve asymptotic tracking steady-state performance. However, the discontinuous controller designed for classical sliding mode control is prone to chattering of the sliding mode surface, which degrades the tracking performance of the system. To address both parameter uncertainty and uncertain nonlinearity, adaptive robust control methods have been proposed. These methods can achieve deterministic transient and steady-state performance in the presence of both modeling uncertainties. To achieve high-precision tracking performance, the feedback gain must be increased to reduce the tracking error. However, due to measurement noise, excessively large feedback gain often results in chattering of the control input, which degrades control performance and can even cause system instability. Summary of the Invention

[0004] The purpose of the present invention is to provide an intelligent control method for a hydraulic system that takes into account all-state performance constraints. This method can not only ensure that the transient performance and steady-state performance of the hydraulic system in all states converge to a preset performance index range, thereby ensuring the safe and reliable operation of the hydraulic system, but also utilize a radial basis function neural network to learn the unknown dynamics of the system in real time, thereby achieving high-precision motion control performance, and avoiding the differential explosion problem in traditional backstepping control of the hydraulic system, thereby reducing the impact of measurement noise on control accuracy.

[0005] The technical solution for achieving the purpose of the present invention is: an intelligent control method for a hydraulic system considering all-state performance constraints, comprising the following steps:

[0006] Step 1: Establish a mathematical model of the hydraulic system and proceed to step 2.

[0007] Step 2: Based on the mathematical model of the hydraulic system, design a nonlinear controller that considers all-state performance constraints, and then proceed to step 3.

[0008] Step 3: Use Lyapunov stability theory to prove the stability of the nonlinear controller considering the full-state performance constraints, and obtain the result that the system tracking error is asymptotically stable.

[0009] Compared with the existing technology, the significant advantages of the present invention are: (1) ensuring that the transient performance and steady-state performance of the system in all states converge to the specified performance index range, ensuring the safe and reliable operation of the system; (2) using radial basis function neural network to learn the unknown dynamics of the system in real time to achieve high-precision motion control performance; (3) avoiding the differential explosion problem in traditional backstepping control of hydraulic systems, reducing the impact of measurement noise on control accuracy, and simulation results verify its effectiveness; (4) realizing nonlinear intelligent control of hydraulic systems with full-state preset performance regulation, strong anti-interference ability and high tracking performance. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] Figure 1 It is a schematic diagram of the principle of the hydraulic system intelligent control method considering all-state performance constraints of the present invention.

[0011] Figure 2 It is a schematic diagram of the hydraulic system principle of the present invention.

[0012] Figure 3 It is a curve diagram of the tracking process of the system output to the expected instruction under the action of the FSPPIC controller designed by the present invention.

[0013] Figure 4 It is a graph showing the tracking error of the system changing with time under the action of the FSPPIC controller designed by the present invention.

[0014] Figure 5 This is a comparison curve of the tracking errors of the system under the action of the FSPPIC controller designed by the present invention and the traditional PID controller.

[0015] Figure 6 This is a control input curve diagram of the system under the action of the FSPPIC controller designed by the present invention. DETAILED DESCRIPTION

[0016] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0017] Combine Figure 1 and Figure 2 , an intelligent control method for a hydraulic system considering all-state performance constraints, comprising the following steps:

[0018] Step 1: Establish a mathematical model of the hydraulic system.

[0019] Step 1-1: The hydraulic system is used for linear motion of large industrial heavy-load mechanical equipment. The load is fixedly connected to the piston rod on the hydraulic cylinder. The electro-hydraulic proportional servo valve controls the movement of the piston rod on the hydraulic cylinder, thereby driving the load to move. Based on the dynamic characteristics of the load, hydraulic cylinder, and electro-hydraulic proportional servo valve, the mathematical model of the hydraulic system is obtained as follows:

[0020] According to Newton's second law, the force balance equation of the hydraulic system is:

[0021]

[0022] In formula (1), m represents the mass of the load, y represents the displacement of the hydraulic cylinder piston rod, Indicates the speed of the hydraulic cylinder piston rod, It represents the acceleration of the hydraulic cylinder piston rod, A represents the effective working area of ​​the hydraulic cylinder piston, and the oil pressure difference P between the inlet and outlet oil chambers on both sides of the cylinder L =P1-P2, P1 represents the oil pressure in the hydraulic cylinder inlet chamber, P2 represents the oil pressure in the hydraulic cylinder outlet chamber, represents the friction force on the load, d1(t) represents the unmodeled mechanical disturbance of the system, and t represents time.

[0023] Then formula (1) can be rewritten as:

[0024]

[0025] In the hydraulic system, ignoring the leakage of oil from the cylinder, the dynamic pressure equation is:

[0026]

[0027] Formula (3), β e Indicates the effective elastic modulus of oil, C t Indicates the leakage coefficient of the hydraulic cylinder, the control volume of the oil inlet chamber V1=V 01 +Ay, the control volume of the oil outlet chamber V2=V 02 -Ay, V 01 Indicates the initial volume of the oil inlet chamber, V 02 represents the initial volume of the oil outlet cavity, Q1 represents the flow rate of the oil inlet cavity, Q2 represents the flow rate of the oil outlet cavity, q1 represents the unmodeled interference of Q1, q2 represents the unmodeled interference of Q2, Indicates P L The first derivative of .

[0028] Q1 and Q2 are respectively related to the displacement of the spool of the electro-hydraulic proportional servo valve x v There are the following relationships:

[0029]

[0030] Among them, the electro-hydraulic proportional servo valve coefficient C d represents the flow coefficient of the electro-hydraulic proportional servo valve, w0 represents the valve core area gradient of the electro-hydraulic proportional servo valve, ρ represents the oil density, P s Indicates the oil supply pressure, P rrepresents the return oil pressure, s(·) represents the function of the intermediate variable ·, and is defined as:

[0031]

[0032] Ignoring the dynamics of the electro-hydraulic proportional servo valve spool, assume that the control input u acting on the spool and the spool displacement x v Proportional relationship, that is, x v =k i u, where k i represents the voltage-spool displacement gain coefficient, so Equation (4) is rewritten as:

[0033]

[0034] Formula (6), intermediate variable k u =k q k i , intermediate variables Intermediate variables

[0035] Step 1-2: To facilitate controller design, define state variables and convert the obtained hydraulic system mathematical model into state space equations, as follows:

[0036] Define state variables: Among them, the intermediate variable x1=y, the intermediate variable Intermediate variable x3 = AP L / m, then transform Equation (2) into a state space equation:

[0037]

[0038] Formula (7), represents the first-order derivative of x1, represents the first-order derivative of x2, represents the first-order derivative of x3, the unknown dynamics of the system Δ1=d1(t) / m, and the intermediate variable F(x2)=F r (x2) / m, intermediate variable Intermediate variables Intermediate variables System unknown dynamics T stands for transpose.

[0039] To facilitate controller design, the following assumptions are made:

[0040] Assumption 1: The system is expected to track the position command x d It is second-order continuous, and the system expects that the position command, velocity command and acceleration command are all bounded.

[0041] Assumption 2: The unknown dynamics Δ1 and Δ2 of the system satisfy:

[0042]

[0043] In formula (8), δ1 and δ2 are both unknown positive constants.

[0044] Go to step 2.

[0045] Step 2: Based on the mathematical model of the hydraulic system, design a nonlinear controller that considers all-state performance constraints. The specific steps are as follows:

[0046] Step 2-1: To facilitate controller design, define the error ζ1 = z1 / Φ1(t), where the system tracking error z1 = x1-x d , x d is the position command that the system expects to track, and Φ1(t) represents the preset performance function. In order to facilitate the system state x1 to track the expected position command x as accurately as possible under the designed controller drive, d , and let z1 always satisfy |z1|<Φ1(t), and ensure that the tracking error ζ1 tends to 0, as follows:

[0047] The tracking error z1 satisfies the preset range:

[0048] -Φ1(t)<z1<Φ1(t) (9),

[0049] Formula (9), the preset performance function Φ1(t)=(Φ 10 -Φ 1∞ )e -ct +Φ 1∞ , where Φ 10 Represents a constant that is always positive, Φ 1∞ represents a constant that is always positive, c represents a constant that is always positive, and satisfies the condition Φ 10 >Φ 1∞ , e represents the natural exponential function.

[0050] Design the following nonlinear filter:

[0051]

[0052] Formula (10), filter gain τ1>0, v1 represents the virtual control of x2, and the filter error ω1 of v1=v 1f -v1,

[0053] v 1f represents the filtered signal of v1, σ(t) represents a function that is always positive and satisfies Where ν represents the integration variable, represents a constant that is always positive, represents the first-order derivative of v1, Indicates v1f The first derivative of The upper bound l1 of represents a positive constant.

[0054] Taking the derivative of the tracking error ζ1, we get:

[0055]

[0056] in, represents the first-order derivative of ζ1, error ζ2=(x2-v 1f ) / Φ2(t), where the preset performance function Φ2(t)=(Φ 20 -Φ 2∞ )e -ct +Φ 2∞ , Φ 20 Represents a constant that is always positive, Φ 2∞ represents a constant that is always positive and satisfies the condition Φ 20 >Φ 2∞ ; Represents x d The first derivative of ; represents the first-order derivative of Φ1(t).

[0057] Select Lyapunov function We can get:

[0058]

[0059] in, represents the first-order derivative of L1.

[0060] Design virtual control v1 as:

[0061]

[0062] Formula (13), gain k1>0, then

[0063]

[0064] Step 2-2, design the following nonlinear filter:

[0065]

[0066] Formula (15), filter gain τ2>0, v2 represents the virtual control of x3, and the filter error ω2 of v2=v 2f -v2,

[0067] v 2f represents the filtered signal of v2, represents the first-order derivative of v2, Indicates v 2f The first derivative of The upper bound l2 represents a positive constant.

[0068] Derivative of the error ζ2 yields:

[0069]

[0070] in, represents the first-order derivative of ζ2, error ζ3=(x3-v 2f ) / Φ3(t), where the preset performance function Φ3(t)=(Φ 30 -Φ 3∞ )e -ct +Φ 3∞ , Φ 30 Represents a constant that is always positive, Φ 3∞ represents a constant that is always positive and satisfies the condition Φ 30 >Φ 3∞ ; represents the first-order derivative of Φ2(t).

[0071] Select Lyapunov function We can get:

[0072]

[0073] in, represents the first-order derivative of L2.

[0074] Design virtual control v2 as:

[0075]

[0076] Formula (18), gain k2>0, v s represents an intermediate variable, γ1 represents a positive constant, represents the estimated value of F(x2), The specific form is:

[0077]

[0078] Formula (19), W a The estimated value of W a represents the weights of the radial basis neural network, represents the activation function of the radial basis neural network, X a Represents the input of the radial basis neural network.

[0079] The weight update law of the radial basis neural network is designed as:

[0080]

[0081] Formula (20), express The first derivative of , Γ a Represents the weight gain matrix of the radial basis neural network, and Proj represents the discontinuous mapping function.

[0082] Substituting formula (18) into formula (17), we get:

[0083]

[0084] Formula (21), the radial basis neural network weight W a The estimated error ω a Represents the approximation error of the radial basis neural network.

[0085] Step 2-3, take the derivative of ζ3 and get:

[0086]

[0087] in, represents the first-order derivative of ζ3; represents the first-order derivative of Φ3(t).

[0088] Select Lyapunov function have to:

[0089]

[0090] in, represents the first-order derivative of L3.

[0091] According to formula (23), the control input of the valve core, that is, the nonlinear controller u considering the full-state performance constraint, is:

[0092]

[0093] Formula (24), gain k3>0, u s represents an intermediate variable, and γ2 represents a positive constant.

[0094] Substituting formula (24) into formula (23) yields:

[0095]

[0096] Go to step 3.

[0097] Step 3: Lyapunov stability theory is used to prove the stability of the nonlinear controller considering the full-state performance constraints, and the result that the system tracking error is asymptotically stable is obtained, as follows:

[0098] The Lyapunov function L is defined as follows:

[0099]

[0100] represents the first derivative of L.

[0101] Derivative (26) and substitute (11), (16), (20) and (25) into it yields:

[0102]

[0103] Taking into account |Δ1|+ω a ≤γ1 and |Δ2|≤γ2, we can get the expression:

[0104]

[0105] Notice

[0106]

[0107] Substituting formula (29) into formula (28), we can get

[0108]

[0109] Formula (30), intermediate variable

[0110] Integrating both sides of equation (30) we can get:

[0111]

[0112] From Equation (31), we can see that L is bounded and Ψ is integrally bounded. It can be concluded that all signals in the system are bounded. Therefore, Ψ is uniformly continuous. According to Barbalat’s lemma, when time tends to positive infinity, the tracking error z1 tends to 0.

[0113] Therefore, it is concluded that by adjusting the gains k1, k2, k3 and the filter gains τ1, τ2, the nonlinear controller designed for the hydraulic system considering the full-state performance constraints can make the system innovatively obtain the result that the tracking error converges to 0 asymptotically. The principle diagram of the hydraulic system intelligent controller considering the full-state performance constraints is shown in the figure. Figure 1 shown.

[0114] Example

[0115] In order to evaluate the performance of the designed controller, the physical parameters of the hydraulic system in the simulation are shown in Table 1:

[0116] Table 1 System physical parameters

[0117] Physical parameters Numerical Physical parameters Numerical <![CDATA[A(m 2 )]]> <![CDATA[2×10 -4 ]]> <![CDATA[β e (Well)]]> <![CDATA[2×10 8 <!-- 8 -->]]> m(kg) 40 B (N·s / m) 80 <![CDATA[C t (m 5 / (N·s))]]> <![CDATA[7×10 -12 ]]> <![CDATA[k u (m / V)]]> <![CDATA[4×10 -8 ]]> <![CDATA[V 01 (m 3 )]]> <![CDATA[1×10 -3 ]]> <![CDATA[V 02 (m 3 )]]> <![CDATA[1×10 -3 ]]> <![CDATA[P s (MPa)]]> 7 <![CDATA[P r (MPa)]]> 0

[0118] Given a system with the expected instruction x d =0.02sin(πt)×(1-e -t )m.

[0119] The following controllers are used for comparison in the simulation:

[0120] Hydraulic system intelligent control method considering full state performance constraints (FSPPIC): take gains k1 = 150, k2 = 40, k3 = 30, Γ a =diag{90,90,90,90,90},τ1=2000,τ2=2000,Φ 10 =2,Φ 1∞ =0.02, c=0.5.

[0121] PID controller: The steps for selecting PID controller parameters are: first, ignoring the nonlinear dynamics of the hydraulic system, obtain a set of controller parameters through the PID parameter self-tuning function in Matlab, and then fine-tune the obtained self-tuning parameters after adding the nonlinear dynamics of the system to achieve the best tracking performance. The selected controller parameters are k P =1000,k I =500,k D = 1. The system's expected command, FSPPIC controller tracking error, and the tracking error comparison between FSPPIC controller and PID controller are as follows: Figure 3 、 Figure 4 and Figure 5 As shown. Figure 4 It can be seen that under the action of the FSPPIC controller, the position output of the electro-hydraulic system has a high tracking accuracy for the command, and the amplitude of the steady-state tracking error is about 3×10 -5 m. From Figure 5 The comparison of the tracking errors of the two controllers shows that the tracking error of the FSPPIC controller proposed in the present invention is much smaller than that of the PID controller, and the tracking performance is more superior. Figure 6 This is a curve diagram of the hydraulic system control input changing with time under the action of the FSPPIC controller. It can be seen from the figure that the obtained control input is a smooth and continuous signal, which is more conducive to execution in practical applications.

Claims

1. An intelligent control method for a hydraulic system considering all-state performance constraints, characterized in that: The following steps are involved: Step 1: Establish a mathematical model of the hydraulic system and proceed to step 2; Step 2: Based on the mathematical model of the hydraulic system, design a nonlinear controller that considers all-state performance constraints, and then proceed to step 3. Step 3: Use Lyapunov stability theory to prove the stability of the nonlinear controller considering the full-state performance constraints, and obtain the result that the system tracking error is asymptotically stable.

2. The intelligent control method for a hydraulic system considering all-state performance constraints according to claim 1, characterized in that: In step 1, a mathematical model of the hydraulic system is established as follows: Step 1-1: The hydraulic system is used for linear motion of large industrial heavy-load mechanical equipment. The load is fixedly connected to the piston rod on the hydraulic cylinder. The electro-hydraulic proportional servo valve controls the movement of the piston rod on the hydraulic cylinder, thereby driving the load to move. Based on the dynamic characteristics of the load, hydraulic cylinder, and electro-hydraulic proportional servo valve, the mathematical model of the hydraulic system is obtained as follows: According to Newton's second law, the force balance equation of the hydraulic system is: In formula (1), m represents the mass of the load, y represents the displacement of the hydraulic cylinder piston rod, Indicates the speed of the hydraulic cylinder piston rod, It represents the acceleration of the hydraulic cylinder piston rod, A represents the effective working area of ​​the hydraulic cylinder piston, and the oil pressure difference P between the inlet and outlet oil chambers on both sides of the cylinder L =P1-P2, P1 represents the oil pressure in the hydraulic cylinder inlet chamber, P2 represents the oil pressure in the hydraulic cylinder outlet chamber, represents the friction force on the load, d1(t) represents the unmodeled mechanical disturbance of the system, and t represents time; Then formula (1) can be rewritten as: In the hydraulic system, ignoring the leakage of oil from the cylinder, the dynamic pressure equation is: Formula (3), β e Indicates the effective elastic modulus of oil, C t Indicates the leakage coefficient of the hydraulic cylinder, the control volume of the oil inlet chamber V1=V 01 +Ay, the control volume of the oil outlet chamber V2=V 02 -Ay, V 01 Indicates the initial volume of the oil inlet chamber, V 02 represents the initial volume of the oil outlet cavity, Q1 represents the flow rate of the oil inlet cavity, Q2 represents the flow rate of the oil outlet cavity, q1 represents the unmodeled interference of Q1, q2 represents the unmodeled interference of Q2, Indicates P L The first derivative of ; Q1 and Q2 are respectively related to the displacement of the spool of the electro-hydraulic proportional servo valve x v There are the following relationships: Among them, the electro-hydraulic proportional servo valve coefficient C d represents the flow coefficient of the electro-hydraulic proportional servo valve, w0 represents the valve core area gradient of the electro-hydraulic proportional servo valve, ρ represents the oil density, P s Indicates the oil supply pressure, P r represents the return oil pressure, s(·) represents the function of the intermediate variable ·, and is defined as: Ignoring the dynamics of the electro-hydraulic proportional servo valve spool, assume that the control input u acting on the spool and the spool displacement x v Proportional relationship, that is, satisfying x v =k i u, where k i represents the voltage-spool displacement gain coefficient, so Equation (4) is rewritten as: Formula (6), intermediate variable k u =k q k i , intermediate variables Intermediate variables Step 1-2: To facilitate controller design, define state variables and convert the obtained hydraulic system mathematical model into state space equations, as follows: Define state variables: Among them, the intermediate variable x1=y, the intermediate variable Intermediate variable x3 = AP L / m, then transform Equation (2) into a state space equation: Formula (7), represents the first-order derivative of x1, represents the first-order derivative of x2, represents the first-order derivative of x3, the unknown dynamics of the system Δ1=d1(t) / m, and the intermediate variable F(x2)=F r (x2) / m, intermediate variable Intermediate variables Intermediate variables System unknown dynamics T stands for transpose.

3. The intelligent control method for a hydraulic system considering all-state performance constraints according to claim 2, characterized in that: In step 1, to facilitate controller design, the following assumptions are made: Assumption 1: The system is expected to track the position command x d It is second-order continuous, and the system expects position command, velocity command and acceleration command to be bounded; Assumption 2: The unknown dynamics Δ1 and Δ2 of the system satisfy: In formula (8), δ1 and δ2 are both unknown positive constants; Go to step 2.

4. The intelligent control method for a hydraulic system considering all-state performance constraints according to claim 3, characterized in that: In step 2, based on the mathematical model of the hydraulic system, a nonlinear controller is designed that takes into account the full-state performance constraints, as follows: Step 2-1: To facilitate controller design, define the error ζ1 = z1 / Φ1(t), where the system tracking error z1 = x1-x d , x d is the position command that the system expects to track, and Φ1(t) represents the preset performance function. In order to facilitate the system state x1 to track the expected position command x as accurately as possible under the designed controller drive, d , and let z1 always satisfy |z1|<Φ1(t), and ensure that the tracking error ζ1 tends to 0, as follows: The tracking error z1 satisfies the preset range: -Φ1(t)<z1<Φ1(t) (9), Formula (9), the preset performance function Φ1(t)=(Φ 10 -Φ 1∞ )e -ct +Φ 1∞ , where Φ 10 Represents a constant that is always positive, Φ 1∞ represents a constant that is always positive, c represents a constant that is always positive, and satisfies the condition Φ 10 >Φ 1∞ , e represents the natural exponential function; Design the following nonlinear filter: Formula (10), filter gain τ1>0, v1 represents the virtual control of x2, and the filter error ω1 of v1=v 1f -v1, v 1f represents the filtered signal of v1, σ(t) represents a function that is always positive and satisfies Where ν represents the integration variable, represents a constant that is always positive, represents the first-order derivative of v1, Indicates v 1f The first derivative of The upper bound l1 represents a positive constant; Taking the derivative of the tracking error ζ1, we get: in, represents the first-order derivative of ζ1, error ζ2=(x2-v 1f ) / Φ2(t), where the preset performance function Φ2(t)=(Φ 20 -Φ 2∞ )e -ct +Φ 2∞ , Φ 20 Represents a constant that is always positive, Φ 2∞ represents a constant that is always positive and satisfies the condition Φ 20 >Φ 2∞ ; Represents x d The first derivative of ; represents the first-order derivative of Φ1(t); Select Lyapunov function We can get: in, represents the first-order derivative of L1; Design virtual control v1 as: Formula (13), gain k1>0, then Step 2-2, design the following nonlinear filter: Formula (15), filter gain τ2>0, v2 represents the virtual control of x3, and the filter error ω2 of v2=v 2f -v2, v 2f represents the filtered signal of v2, represents the first-order derivative of v2, Indicates v 2f The first derivative of The upper bound l2 represents a positive constant; Derivative of the error ζ2 yields: in, represents the first-order derivative of ζ2, error ζ3=(x3-v 2f ) / Φ3(t), where the preset performance function Φ3(t)=(Φ 30 -Φ 3∞ )e -ct +Φ 3∞ , Φ 30 Represents a constant that is always positive, Φ 3∞ represents a constant that is always positive and satisfies the condition Φ 30 >Φ 3∞ ; represents the first-order derivative of Φ2(t); Select Lyapunov function We can get: in, represents the first-order derivative of L2; Design virtual control v2 as: Formula (18), gain k2>0, v s represents an intermediate variable, γ1 represents a positive constant, represents the estimated value of F(x2), The specific form is: Formula (19), W a The estimated value of W a represents the weights of the radial basis neural network, represents the activation function of the radial basis neural network, X a Represents the input of the radial basis neural network; The weight update law of the radial basis neural network is designed as: Formula (20), express The first derivative of , Γ a represents the weight gain matrix of the radial basis neural network, and Proj represents the discontinuous mapping function. Substituting formula (18) into formula (17), we get: Formula (21), the weight of the radial basis neural network W a The estimated error ω a represents the approximation error of the radial basis neural network; Step 2-3, take the derivative of ζ3 and get: in, represents the first-order derivative of ζ3; represents the first-order derivative of Φ3(t); Select Lyapunov function have to: in, represents the first-order derivative of L3; According to formula (23), the control input of the valve core, that is, the nonlinear controller u considering the full-state performance constraint, is: Formula (24), gain k3>0, u s represents an intermediate variable, and γ2 represents a positive constant; Substituting formula (24) into formula (23) yields: Go to step 3.

5. The intelligent control method for a hydraulic system considering all-state performance constraints according to claim 4, characterized in that: The Lyapunov stability theory described in step 3 is used to prove the stability of the nonlinear controller considering the full-state performance constraints, and the result that the system tracking error is asymptotically stable is obtained, as follows: The Lyapunov function L is defined as follows: The stability is proved by using Lyapunov stability theory, and the result that the system tracking error is asymptotically stable is obtained.

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