A hydraulic multi-way valve control system adaptive asymptotic preset performance tracking control method

Abstract

The application discloses a kind of hydraulic multipath valve control system adaptive asymptotic preset performance tracking control methods, this control method is based on the asymptotic preset performance function of construction, fusion error sign integral robust method, design with adaptive hydraulic multipath valve control system controller.For the hydraulic multipath valve control problem, the application uses adaptive recursive error sign integral robust control law, realizes the compensation of parameter uncertainty, and also fuses error sign integral robust control and preset performance control, can handle system matching and mismatched interference, ensure system transient performance and steady-state performance, can reach the specified tracking performance, realize the asymptotic stability of system.

Description

Technical Field

[0001] This invention relates to the field of electromechanical servo control technology, specifically to an adaptive asymptotic preset performance tracking control method for a hydraulic multi-channel valve control system. Background Technology

[0002] Hydraulic multi-way valve control systems play a crucial role in the hydraulic transmission systems of modern construction machinery, especially in equipment requiring complex operations. When cranes, rotary drilling rigs, bulldozers, and other construction machinery perform tasks, they often need to coordinate the simultaneous operation of multiple working mechanisms to complete diverse operational requirements, such as combined actions like lifting, rotating, bulldozing, and digging. Hydraulic multi-way valve control systems precisely possess this function, efficiently controlling the combined actions of multiple actuators through the coordinated control of multiple hydraulic valves. In this system, the hydraulic multi-way valve, as the core component, typically consists of two or more hydraulic elements with the required functions, such as directional valves, check valves, and brake valves. However, hydraulic multi-way valve control systems exhibit highly nonlinear characteristics, which lead to limitations in dynamic response and steady-state accuracy. Furthermore, external load disturbances, parameter uncertainties, and system coupling effects further exacerbate the complexity of system control. To ensure that the hydraulic multi-way valve control system can achieve good control performance and minimize the impact of nonlinear systems, the control strategy of the hydraulic multi-way valve control system must have the characteristics of high precision, high response speed and strong robustness. However, traditional linear control methods are no longer able to meet the requirements of high-precision control.

[0003] To address the aforementioned issues, scholars both domestically and internationally have conducted extensive research on nonlinear control strategies for hydraulic multi-way valve control systems. Classical sliding mode control (SMD) possesses good robustness and anti-interference capabilities, effectively handling system parameter uncertainties and external disturbances. However, the inclusion of a sign function in the input can easily lead to chattering, causing mechanical wear on the system's actuators and affecting the system's stable operation. Adaptive robust control effectively handles the problems of matched and mismatched disturbances and, through adaptive adjustment of control parameters, enables the system to achieve deterministic steady-state performance. However, this method can only guarantee convergence within a sufficiently small range when both matched and mismatched disturbances coexist. Error Sign Integral Robustness (RISE) has strong robustness and continuity. Combined with backstepping-based adaptive control, its most significant advantage over adaptive robust control is that the system's tracking error asymptotically converges to zero. However, when facing matched and mismatched disturbances, its smoothness and boundedness must be determined. All of the above control methods primarily focus on improving steady-state tracking performance, with little attention paid to transient performance. Summary of the Invention

[0004] The purpose of this invention is to provide an adaptive asymptotic preset performance tracking control method for a hydraulic multi-channel valve control system. This method employs an adaptive recursive error sign integral robust control law, which integrates an adaptive law based on the desired trajectory to compensate for parameter uncertainties. It also combines error sign integral robust control and preset performance control to handle system matching and mismatch disturbances, ensuring the system's transient and steady-state performance, achieving the specified tracking performance, and realizing the system's asymptotic stability.

[0005] The technical solution to achieve the purpose of this invention is: an adaptive asymptotic preset performance tracking control method for a hydraulic multi-way valve control system, comprising the following steps:

[0006] Step 1: Establish the mathematical model of the hydraulic multi-way valve control system, then proceed to Step 2.

[0007] Step 2: Based on the mathematical model of the hydraulic multi-way valve control system, design a tracking controller with adaptive asymptotic preset performance, and proceed to Step 3.

[0008] Step 3: Using Lyapunov stability theory, prove the stability of the adaptive asymptotic preset performance tracking controller, and obtain the result that the system tracking error is limited to the specified constraints and is asymptotically stable.

[0009] Compared with the prior art, the significant advantages of this invention are: (1) It adopts an adaptive recursive error symbol integral robust control law, which integrates the adaptive law based on the desired trajectory, and realizes the compensation for parameter uncertainty; (2) It integrates error symbol integral robust control and preset performance control, which can handle system matching and mismatch disturbances, ensure the transient performance and steady-state performance of the system, achieve the specified tracking performance, realize the asymptotic stability of the system, and the simulation results verify its effectiveness. Attached Figure Description

[0010] Figure 1 This is a schematic diagram illustrating the principle of the adaptive asymptotic preset performance tracking control method for the hydraulic multi-channel valve control system of the present invention.

[0011] Figure 2 This is a simplified schematic diagram of the hydraulic multi-way valve control system of the present invention.

[0012] Figure 3 This is a graph showing the expected command curve that the system output needs to track under the action of the adaptive asymptotic preset performance tracking controller designed in this invention.

[0013] Figure 4 This is a graph showing the change of tracking error of the system over time under the action of the adaptive asymptotic preset performance tracking controller designed in this invention.

[0014] Figure 5This is a comparison curve of the tracking error and preset performance boundary of the system under the action of the adaptive asymptotic preset performance tracking controller designed in this invention and the traditional PID controller.

[0015] Figure 6 Under the action of the adaptive asymptotic preset performance tracking controller designed in this invention A graph showing how the graph changes over time.

[0016] Figure 7 Under the action of the adaptive asymptotic preset performance tracking controller designed in this invention A graph showing how the graph changes over time. Detailed Implementation

[0017] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0018] Combination Figure 1 and Figure 2 The present invention provides an adaptive asymptotic preset performance tracking control method for a hydraulic multi-channel valve control system, comprising the following steps:

[0019] Step 1: Establish a mathematical model of the hydraulic multi-way valve control system.

[0020] Step 1-1: The hydraulic multi-way valve control system is applied to large heavy-duty engineering machinery, mining, and metallurgical equipment. The electric signal controls the electro-proportional pressure reducing valve, which changes its output pressure by adjusting the voltage, thereby changing the pressure in the main valve control chamber, causing the valve core to move, controlling the piston rod on the hydraulic cylinder to move, and driving the load fixed to the piston rod of the hydraulic cylinder to move.

[0021] According to Newton's second law, the force balance equation of a hydraulic multi-way valve control system is:

[0022]

[0023] In equation (1), m represents the mass of the load, and y represents the displacement of the hydraulic cylinder piston rod. Indicates the speed of the hydraulic cylinder piston rod. Let A represent the acceleration of the hydraulic cylinder piston rod, P1 represent the oil pressure in the hydraulic cylinder inlet chamber, P2 represent the oil pressure in the hydraulic cylinder outlet chamber, B represent the total viscous damping coefficient of the hydraulic cylinder and the load, K represent the elastic load coefficient, d(t) represent the unmodeled mechanical disturbance of the system, and t represent time.

[0024] Then equation (1) can be rewritten as:

[0025]

[0026] In a hydraulic multi-way valve control system, neglecting external leakage of hydraulic fluid from the cylinder, the pressure dynamic equation is:

[0027]

[0028] In equation (3), β e V represents the effective elastic modulus of the oil. t C represents the equivalent volume of a hydraulic cylinder. t This represents the internal leakage coefficient of the hydraulic cylinder, and the pressure difference P between the oil inlet and outlet chambers on both sides of the cylinder. L =P1-P2, Q L q represents the load overcurrent, and P represents the load overcurrent. L Unmodeled interference, P represents L The first derivative.

[0029] The mathematical model of the valve core motion in a hydraulic multi-way valve control system is as follows:

[0030]

[0031] In equation (4), m v Indicates the mass of the valve core, x v Indicates the displacement of the valve core. Indicates the speed of the valve core.

[0032] A represents the acceleration of the valve core. v P represents the effective working area of ​​the main valve control chamber. i B indicates the output pressure of the pressure reducing valve. v K represents the viscous damping coefficient of the valve core. v Let d1(t) represent the valve core elasticity coefficient, d1(t) represent the unmodeled disturbance of the main valve mechanical system, and t represent time. Assume the output pressure P of the pressure reducing valve is... i In a steady state, it is directly proportional to the control input signal U, i.e., P i =k t U,k t This represents the gain coefficient of the pressure reducing valve's output pressure relative to the input voltage.

[0033] Overload current Q L The valve core displacement x of the main valve in a hydraulic multi-way valve control system v The following relationship exists:

[0034]

[0035] In equation (5), C d The main valve's flow coefficient is represented by w, the valve core area gradient by ρ, and the oil density by P. s Let sgn(·) represent the oil supply pressure, and let sgn(·) represent a function of the intermediate variable ·, defined as:

[0036]

[0037] After linearization, equation (5) is rewritten as:

[0038] Q L =k d1 x v -k d2 P L (7)

[0039] In equation (7), k d1 k represents the main valve flow gain coefficient. d2 This represents the main valve flow-pressure gain coefficient.

[0040] Step 1-2: Define state variables: Where, the intermediate variable x1 = y, the intermediate variable Intermediate variable x3 = AP L / m, intermediate variable x4 = x v intermediate variables Then equations (2), (3), (4), and (7) are transformed into state equations:

[0041]

[0042] In equation (8), Denotes the first derivative of x1. This represents the first derivative of x². This represents the first derivative of x³. This represents the first derivative of x⁴. Let x5 be the first derivative.

[0043] Ignoring the viscous damping of the main valve, select state variables. The control variable u is the displacement of the main valve core. Simplifying equation (8) yields a new system state-space equation:

[0044]

[0045] In equation (9), the intermediate variable intermediate variables Unknown system dynamics intermediate variables intermediate variables intermediate variables Unknown system dynamics

[0046] To facilitate controller design, the following assumptions are made:

[0047] Assumption 1: The system expects to track the position command x 1dIt is third-order continuously differentiable, and the system expects the position command, velocity command, and acceleration command to be bounded.

[0048] Assumption 2: The unknown dynamics of the system Δ1(t) and Δ2(t) are sufficiently smooth, and their first and second derivatives exist and are bounded, i.e.:

[0049]

[0050] In equation (10), ζ1, ζ2, ζ3 and ζ4 are all unknown positive constants.

[0051] Proceed to step 2.

[0052] Step 2: Based on the mathematical model of the hydraulic multi-way valve control system, design a tracking controller with adaptive asymptotic preset performance. The specific steps are as follows:

[0053] Step 2-1: Design a preset performance function and transform the tracking error, as follows:

[0054] Define the system tracking error e1 = x1 - x 1d x 1d The system expects to track position commands. To constrain tracking errors, a preset performance function μ(t) = (μ0 - μt) is introduced. ∞ )e -κt +μ ∞ , where μ0 and μ ∞

[0055] Both represent parameters to be designed, μ0 > μ ∞ >0, convergence rate κ>0, thus constraining the tracking error and keeping it within the performance boundary, i.e.:

[0056]

[0057] In equation (11), δ and If it represents a positive adjustable parameter, then - δ μ0 and This represents the lower limit of the tracking error and the upper limit of the overshoot. δ μ ∞ and This indicates the preset steady-state error range.

[0058] Introducing a strictly increasing function ψ(z1) that satisfies the following property:

[0059]

[0060] In equation (12), z1 represents the conversion error signal corresponding to the tracking error e1. Based on the above properties, the following nonlinear mapping is introduced:

[0061] e1(t)=μ(t)ψ(z1) (13)

[0062] The strictly increasing function ψ(z1) is defined as:

[0063]

[0064] In equation (14), λ represents an intermediate variable. Taking the inverse function of equation (14) yields:

[0065]

[0066] As long as the conversion error z1 is bounded under the action of the controller, according to the property of ψ(z1), equation (11) can be guaranteed to hold. This shows that if z1 converges asymptotically to 0, the tracking error e1 will also converge asymptotically to 0.

[0067] Differentiating equation (15) with respect to time yields:

[0068]

[0069] In equation (16), Let z1 be the first derivative. Denotes the first derivative of e1. The first derivative of μ, an intermediate variable The intermediate variable ρ is bounded and satisfies:

[0070]

[0071] Step 2-2: Based on the mathematical model of the hydraulic multi-way valve control system and the conversion error z1, design an adaptive asymptotic preset performance tracking controller, as follows:

[0072] Differentiating equation (16) with respect to time and substituting equation (9) into it, we get:

[0073]

[0074] In equation (18), The first derivative of ρ is represented by ρ. Let z1 be the second derivative. Denotes the second derivative of μ. x represents 1d The second derivative of .

[0075] Define a set of error signals as follows:

[0076]

[0077] In equation (19), z2, z3, r1, and r2 represent error signals, and k1, k2, and k3 represent positive gains. This represents the first derivative of z². Let z3 represent the first derivative, and α2 represent the virtual control law.

[0078] Substituting equation (18) into equation (19) for the error signal r1, we get:

[0079]

[0080] In equation (20), the actual value θ = [θ1, θ2] T The intermediate variable φ(x) = [-x1, -x2] T intermediate variables

[0081] The virtual control law α2 is designed as follows:

[0082]

[0083] In equation (21), k s k r1 And β1 represents positive gain, α 2a α represents the model-based compensation term. 2s1 Let α represent the linear robust term. 2s2 This represents the real-time estimate of the RISE feedback control term. z2(0) represents the initial value of z2, and dτ represents the derivative with respect to time.

[0084] Define real-time estimates The adaptive law is:

[0085]

[0086] In equation (22), express The first derivative of Γ, where Γ represents the adjustable diagonal adaptive gain matrix with constant constants. express First derivative, intermediate variable

[0087] Equation (22) can be obtained by integration by parts:

[0088]

[0089] In equation (23), express initial value, express The second derivative of .

[0090] Substituting equation (21) into equation (20), we get:

[0091]

[0092] In equation (24), the intermediate variable Intermediate variable χ = ρθ T (φ(x)-φ d (x)), the parameter estimation error of θ

[0093] Differentiating equation (24) with respect to time yields:

[0094]

[0095] In equation (25), The first derivative of r1 is represented by the intermediate variable. intermediate variables This represents the first derivative of E. Let Δ1(t) be the first derivative.

[0096] right Applying the mean value theorem, we get:

[0097]

[0098] In equation (26), the error signal vector z = [z1, z2, r1, z3, r2]. T η(·) represents a positive, globally invertible, non-decreasing function. According to equations (10) and (17), N can be obtained. d It satisfies the following properties:

[0099]

[0100] In equation (27), ζ5 and ζ6 are both unknown positive constants.

[0101] Substituting equation (9) into equation (19) for the error signal r2, we get:

[0102]

[0103] In equation (28), It represents the first derivative of α².

[0104] According to equation (28), the controller input u is designed as follows:

[0105]

[0106] In equation (29), k r2 And β2 represents positive gain, u a Represents the model-based compensation term, u s1 Represents the linear robust term, u s2 This represents the RISE feedback control term, and z3(0) represents the initial value of z3.

[0107] Substituting equation (29) into equation (28), we get:

[0108]

[0109] Differentiating equation (30) with respect to time yields:

[0110]

[0111] In equation (31), This represents the first derivative of r². Let Δ2(t) represent the first derivative.

[0112] Proceed to step 3.

[0113] Step 3: Using Lyapunov stability theory, the stability of the adaptive asymptotic preset performance tracking controller is proven, yielding results showing that the system tracking error is limited to the specified constraints and is asymptotically stable, as detailed below:

[0114] Before proving stability, the following lemma is given to facilitate subsequent analysis:

[0115] Lemma 1: The system satisfies ε = f(ε,t), where ε represents an intermediate variable. Function There exists a solution, where, Represents the set of n-dimensional real numbers. Let represent the set of real numbers greater than or equal to 0. The domain is defined as follows: Where υ represents a positive constant. Define a continuously differentiable function that satisfies the following conditions. That is, for satisfy:

[0116]

[0117] In equation (32), U1(ε) and U2(ε) represent continuous positive definite functions, and U(ε) represents a uniformly continuous positive semi-definite function. Let V(ε,t) represent the first derivative. If equation (32) is satisfied and ε(0)∈S, then the following conclusion holds:

[0118] As t→∞, U(ε(t))→0 (33)

[0119] The field S is defined as follows:

[0120]

[0121] In equation (34), Represents a positive integer.

[0122] prove:

[0123] The auxiliary function is defined as follows:

[0124]

[0125] In equation (35), L1(t), L2(t), P1(t), and P2(t) represent auxiliary functions, and N d (0) and N d and The initial value.

[0126] The Lyapunov function is defined as follows:

[0127]

[0128] In equation (36), the intermediate variable ε is an expression for time t, which can be written as ε(t). Assuming a domain D contains ε(t) = 0, the intermediate variable ε(t) is expressed as:

[0129]

[0130] for V(ε,t) satisfies:

[0131] U1(ε)≤V(ε,t)≤U2(ε) (38)

[0132] In equation (38), the intermediate variable function U1(ε)=σ1||ε|| 2 and U2(ε)=σ2||ε|| 2 It is a continuous positive definite function, with intermediate variables. and λ min (·) and λ max (·) represent the minimum and maximum eigenvalues ​​of the matrix, respectively.

[0133] Differentiating equation (36) with respect to time, and substituting equations (19), (22), (25), (31), and (35) into the equation, we get:

[0134]

[0135] In equation (39), Let V(ε,t) be the first derivative.

[0136] Define the intermediate variable Λ as:

[0137]

[0138] By adjusting the gains k1, k2, k3, k r1 and k r2If we make the symmetric matrix Λ a positive definite matrix, then equation (39) yields:

[0139]

[0140] Substituting equation (26) into equation (41), we get:

[0141]

[0142] In equation (42), the intermediate variable It is a positive number when the following conditions are met:

[0143]

[0144] In equation (43), η -1 (·) represents the inverse function of η(·).

[0145] In equation (42), U(ε) is a positive semi-definite function defined in the domain D, which satisfies the following condition:

[0146]

[0147] According to equation (38), V∈L ∞ And U∈L2. According to equation (36), the error signals z1, z2, z3, r1, r2 and the parameter estimation error values ​​are obtained. All are bounded. Based on assumption 1, the intermediate variables x1, x2, and x3 are also bounded. According to equations (19), (25), and (31), the time derivatives of the error signals z1, z2, z3, r1, and r2 are all bounded, indicating that... Since it is bounded, U(ε) is a uniformly continuous function in its domain D. Let a domain S∈D be defined as follows:

[0148]

[0149] According to Lemma 1:

[0150] As t→∞

[0151] Based on equation (15), the tracking error of the system will be limited to the constraints specified in equation (11), and the tracking error e1 tends to 0 when time approaches infinity, that is:

[0152] As t→∞, e1(t)→0 (47)

[0153] Therefore, we can conclude that by adjusting the gains k1, k2, k3, and k... r1 and k r2By selecting a sufficiently large gain coefficient to make the symmetric matrix Λ a positive definite matrix, the system can achieve a result where the tracking error asymptotically converges to 0 according to the specified constraints. A schematic diagram of the adaptive asymptotic preset performance tracking controller principle for a hydraulic multi-channel valve control system is shown below. Figure 1 As shown.

[0154] Example

[0155] To evaluate the performance of the designed controller, the physical parameters of the hydraulic multi-way valve control system in the simulation are shown in Table 1:

[0156] Table 1 System Physical Parameters

[0157] physical parameters numerical values physical parameters numerical values <![CDATA[A(m 2 )]]> <![CDATA[2×10 -4 ]]> <![CDATA[β e (Well)]]> <![CDATA[2×10 8 ]]> m(kg) 5 B(N·s / m) 40 K(N / m) 20 <![CDATA[V t (m 3 )]]> 0.04 <![CDATA[K d1 (m 2 / s)]]> 1.608 <![CDATA[K d2 (m 5 / (N·s))]]> <![CDATA[3.717×10 -12 ]]> <![CDATA[C t (m 5 / (N·s))]]> <![CDATA[7×10 -12 ]]>

[0158] Given the desired instructions of the system

[0159] The following controller is used for comparison in the simulation:

[0160] Hydraulic multi-channel valve control system adaptive asymptotic preset performance tracking controller: with gains k1 = 5, k2 = 10, k3 = 15, β1 = 1.2, β2 = 0.8, k s =400,k r1 =100, k r2 =150, take the adjustable parameter δ=0.04, μ0 = 0.01, μ ∞ =0.0015, κ=2,

[0161]

[0162] PID controller: The steps for selecting PID controller parameters are as follows: First, ignoring the nonlinear dynamics of the hydraulic multi-way valve control system, obtain a set of controller parameters through the PID parameter self-tuning function in Matlab. Then, after adding the nonlinear dynamics of the system, fine-tune the obtained self-tuning parameters to achieve the best tracking performance of the system.

[0163] The system's expected command, the tracking error of the adaptive asymptotic preset performance tracking controller, and the comparison of the tracking errors of the adaptive asymptotic preset performance tracking controller and the PID controller with the preset performance boundaries are respectively as follows: Figure 3 , Figure 4 and Figure 5 As shown. By Figure 4 It can be seen that, under the action of the adaptive asymptotic preset performance tracking controller, the position output of the hydraulic multi-way valve control system has a very high tracking accuracy to the command, and the amplitude of the steady-state tracking error is approximately 2 × 10⁻⁶. -7 m. From Figure 5A comparison of the tracking errors of the two controllers shows that the tracking error of the adaptive asymptotic preset performance tracking controller proposed in this invention is much smaller than that of the PID controller. Furthermore, the adaptive asymptotic preset performance tracking controller can ensure that the error is within the preset performance boundary, resulting in superior tracking performance. Figure 6 and Figure 7 These are the real-time estimates of θ1 and θ2, respectively. and Based on the given system physical parameters It can be seen that the adaptive method proposed in this invention can accurately estimate the system parameters in real time.

Claims

1. An adaptive asymptotic preset performance tracking control method for a hydraulic multi-way valve control system, characterized in that, Includes the following steps: Step 1: Establish the mathematical model of the hydraulic multi-way valve control system, then proceed to Step 2; Step 2: Based on the mathematical model of the hydraulic multi-way valve control system, design a tracking controller with adaptive asymptotic preset performance, as follows: Step 2-1: To facilitate controller design, design a preset performance function and transform the tracking error, as follows: Define the system tracking error e1 = x1 - x 1d x 1d The system expects to track position commands. To constrain tracking errors, a preset performance function μ(t) = (μ0 - μt) is introduced. ∞ )e -κt +μ ∞ , where μ0 and μ ∞ Both represent parameters to be designed, μ0 > μ ∞ >0, convergence rate κ>0, thus constraining the tracking error and keeping it within the performance boundary, i.e.: In equation (11), δ and If it represents a positive adjustable parameter, then - δ μ0 and This represents the lower limit of the tracking error and the upper limit of the overshoot. δ μ ∞ and Indicates the preset steady-state error range; Introducing a strictly increasing function ψ(z1) that satisfies the following property: In equation (12), z1 represents the conversion error signal corresponding to the tracking error e1. Based on the above properties, the following nonlinear mapping is introduced: e1(t)=μ(t)ψ(z1) (13) The strictly increasing function ψ(z1) is defined as: In equation (14), λ represents an intermediate variable. Taking the inverse function of equation (14) yields: As long as the conversion error z1 is bounded under the action of the controller, according to the property of ψ(z1), equation (11) can be guaranteed to hold. This shows that if z1 converges asymptotically to 0, the tracking error e1 will also converge asymptotically to 0. Differentiating equation (15) with respect to time yields: In equation (16), Let z1 be the first derivative. Denotes the first derivative of e1. The first derivative of μ is represented by the intermediate variable. The intermediate variable ρ is bounded and satisfies: Step 2-2: Based on the mathematical model of the hydraulic multi-way valve control system and the conversion error z1, design an adaptive asymptotic preset performance tracking controller, as follows: Differentiating equation (16) with respect to time and substituting equation (9) into it, we get: In equation (18), The first derivative of ρ is represented by ρ. Let z1 be the second derivative. Denotes the second derivative of μ. x represents 1d The second derivative; Define a set of error signals as follows: In equation (19), z2, z3, r1, and r2 represent error signals, and k1, k2, and k3 represent positive gains. This represents the first derivative of z². Let α3 denote the first derivative of z3, and α2 denote the virtual control law; Substituting equation (18) into equation (19) for the error signal r1, we get: In equation (20), the actual value θ = [θ1, θ2] T The intermediate variable φ(x) = [-x1, -x2] T intermediate variables The virtual control law α2 is designed as follows: In equation (21), k s k r1 And β1 represents positive gain, α 2a α represents the model-based compensation term. 2s1 Let α represent the linear robust term. 2s2 This represents the real-time estimate of the RISE feedback control term. z2(0) represents the initial value of z2, and dτ represents the derivative with respect to time; Define real-time estimates The adaptive law is: In equation (22), express The first derivative of Γ, where Γ represents the adjustable diagonal adaptive gain matrix with constant constants. express First derivative, intermediate variable Equation (22) can be obtained by integration by parts: In equation (23), express initial value, express The second derivative; Substituting equation (21) into equation (20), we get: In equation (24), the intermediate variable Intermediate variable χ = ρθ T (φ(x)-φ d (x)), the parameter estimation error of θ Differentiating equation (24) with respect to time yields: In equation (25), The first derivative of r1 is represented by the intermediate variable. intermediate variables This represents the first derivative of E. The first derivative of Δ1(t) is given by... Applying the mean value theorem, we get: In equation (26), the error signal vector z = [z1, z2, r1, z3, r2]. T η(·) denotes a positive, globally invertible, non-decreasing function; According to equations (10) and (17), N can be obtained. d It satisfies the following properties: In equation (27), ζ5 and ζ6 are both unknown positive constants; Substituting equation (9) into equation (19) for the error signal r2, we get: In equation (28), This represents the first derivative of α². According to equation (28), the controller input u is designed as follows: In equation (29), k r2 And β2 represents positive gain, u a Represents the model-based compensation term, u s1 Represents the linear robust term, u s2 This represents the RISE feedback control term, and z3(0) represents the initial value of z3; Substituting equation (29) into equation (28), we get: Differentiating equation (30) with respect to time yields: In equation (31), This represents the first derivative of r². This represents the first derivative of Δ2(t); Proceed to step 3; Step 3: Using Lyapunov stability theory, prove the stability of the adaptive asymptotic preset performance tracking controller, and obtain the result that the system tracking error is limited to the specified constraints and is asymptotically stable.

2. The adaptive asymptotic preset performance tracking control method for a hydraulic multi-channel valve control system according to claim 1, characterized in that, In step 1, a mathematical model of the hydraulic multi-way valve control system is established, as follows: Step 1-1: The hydraulic multi-way valve control system is applied to large heavy-duty engineering machinery, mining, and metallurgical equipment. The electro-proportional pressure reducing valve is controlled by an electrical signal. By adjusting the voltage, its output pressure is changed, thereby changing the pressure in the main valve control chamber, causing the valve core to move. This controls the movement of the piston rod on the hydraulic cylinder, which in turn drives the load fixed to the piston rod of the hydraulic cylinder to move. Based on the dynamic characteristics of the load, the hydraulic cylinder, and the valve core of the multi-way valve, the mathematical model of the hydraulic multi-way valve control system is derived. Steps 1-2: To facilitate controller design, define state variables and convert the derived mathematical model of the hydraulic multi-way valve control system into state-space equations.

3. The adaptive asymptotic preset performance tracking control method for a hydraulic multi-channel valve control system according to claim 2, characterized in that, In step 1-1, based on the dynamic characteristics of the load, hydraulic cylinder, and main valve spool of the multi-way valve, the mathematical model of the hydraulic multi-way valve control system is derived, as follows: According to Newton's second law, the force balance equation of a hydraulic multi-way valve control system is: In equation (1), m represents the mass of the load, and y represents the displacement of the hydraulic cylinder piston rod. Indicates the speed of the hydraulic cylinder piston rod. d(t) represents the acceleration of the hydraulic cylinder piston rod, A represents the effective working area of ​​the hydraulic cylinder piston, P1 represents the oil pressure in the hydraulic cylinder inlet chamber, P2 represents the oil pressure in the hydraulic cylinder outlet chamber, B represents the total viscous damping coefficient of the hydraulic cylinder and the load, K represents the elastic load coefficient, d(t) represents the unmodeled mechanical disturbance of the system, and t represents time. Then equation (1) can be rewritten as: In a hydraulic multi-way valve control system, neglecting external leakage of hydraulic fluid from the cylinder, the pressure dynamic equation is: In equation (3), β e V represents the effective elastic modulus of the oil. t C represents the equivalent volume of a hydraulic cylinder. t P represents the internal leakage coefficient of the hydraulic cylinder, and the pressure difference P between the oil inlet and outlet chambers on both sides of the cylinder. L =P1-P2, Q L q represents the load overcurrent, and P represents the load overcurrent. L Unmodeled interference, P represents L The first derivative; The mathematical model of the valve core motion in a hydraulic multi-way valve control system is as follows: In equation (4), m v Indicates the mass of the valve core, x v This indicates the displacement of the valve core. Indicates the speed of the valve core. A represents the acceleration of the valve core. v P represents the effective working area of ​​the main valve control chamber. i B indicates the output pressure of the pressure reducing valve. v K represents the viscous damping coefficient of the valve core. v Let d1(t) represent the valve core elasticity coefficient, d1(t) represent the unmodeled disturbance of the main valve mechanical system, and t represent time; let P be the output pressure of the pressure reducing valve. i In a steady state, it is directly proportional to the control input signal U, i.e., P i =k t U,k t This represents the gain coefficient of the pressure reducing valve's output pressure relative to the input voltage. Overload current Q L The valve core displacement x of the main valve in a hydraulic multi-way valve control system v The following relationship exists: In equation (5), C d The main valve's flow coefficient is represented by w, the valve core area gradient by ρ, and the oil density by P. s Let sgn(·) represent the oil supply pressure, and let sgn(·) represent a function of the intermediate variable ·, defined as: After linearization, equation (5) is rewritten as: Q L =k d1 x v -k d2 P L (7) In equation (7), k d1 k represents the main valve flow gain coefficient. d2 This represents the main valve flow-pressure gain coefficient.

4. The adaptive asymptotic preset performance tracking control method for a hydraulic multi-channel valve control system according to claim 3, characterized in that, In steps 1-2, state variables are defined, and the derived mathematical model of the hydraulic multi-way valve control system is transformed into state-space equations, as follows: Define state variables: Where, the intermediate variable x1 = y, the intermediate variable Intermediate variable x3 = AP L / m, intermediate variable x4 = x v intermediate variables Equations (2), (3), (4), and (7) are then transformed into state equations: In equation (8), Denotes the first derivative of x1. This represents the first derivative of x². This represents the first derivative of x³. This represents the first derivative of x⁴. This represents the first derivative of x5; Ignoring the viscous damping of the main valve, select state variables. The control variable u is the displacement of the main valve core. Simplifying equation (8) yields a new system state-space equation: In equation (9), the intermediate variable intermediate variables Unknown system dynamics intermediate variables intermediate variables intermediate variables Unknown system dynamics 5. The adaptive asymptotic preset performance tracking control method for a hydraulic multi-channel valve control system according to claim 4, characterized in that, In step 1, for the convenience of controller design, the following assumptions are made: Assumption 1: The system expects to track the position command x 1d It is third-order continuously differentiable, and the system expects bounded position, velocity, and acceleration commands. Assumption 2: The unknown dynamics of the system Δ1(t) and Δ2(t) are sufficiently smooth, and their first and second derivatives exist and are bounded, i.e.: In equation (10), ζ1, ζ2, ζ3 and ζ4 are all unknown positive constants; Proceed to step 2.

6. The adaptive asymptotic preset performance tracking control method for a hydraulic multi-channel valve control system according to claim 5, characterized in that, In step 3, the stability of the adaptive asymptotic preset performance tracking controller is proved using Lyapunov stability theory. The results show that the system tracking error is limited to the specified constraints and is asymptotically stable, as detailed below: The auxiliary function is defined as follows: In equation (35), L1(t), L2(t), P1(t), and P2(t) represent auxiliary functions, and N d (0) and N d and The initial value; The Lyapunov function is defined as follows: In equation (36), the intermediate variable ε is an expression for time t, which can be written as ε(t), specifically as follows: The stability was proven using Lyapunov stability theory, and the system tracking error was found to be constrained under specified constraints and asymptotically stable.

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