Pilot operated multi-way valve control system position control method considering output state constraints

Abstract

The application discloses a pilot multi-way valve control system position control method considering output state constraints, which is based on an output state constraint function constructed, combines a backstepping control thought, and designs a nonlinear position control controller with instruction filtering, state and disturbance observation. For the position control problem of the pilot multi-way valve system, the application can not only rely on the feedback of the system output state signal, construct an extended state observer and a state constraint nonlinear conversion function to obtain unmeasured states of the system, realize output state constraint and disturbance compensation of the system, but also avoid the differential explosion problem in the traditional nonlinear control method through the instruction filter, reduce the influence of the closed-loop control overshoot on the system, and realize high-precision position tracking performance.

Description

Technical Field

[0001] This invention relates to the field of electro-hydraulic control technology, and more specifically to a position control method (SCRC) for a pilot-operated multi-way valve control system that takes into account output state constraints. Background Technology

[0002] Pilot-operated multi-way valves, with their strong environmental adaptability, fewer pipeline connections, and high reliability, play an important role in engineering machinery, agricultural machinery, and industrial hydraulics. A pilot-operated multi-way valve control system can be simply described as an electro-hydraulic valve control system composed of a power source, a multi-way valve, a hydraulic actuator, and related accessories. The system contains various characteristic elements such as flow nonlinearity, friction nonlinearity, hysteresis between the pilot and main valves, input nonlinearity, and hydraulic dynamics, as well as system parameter and nonlinear uncertainties. Parameter uncertainties include uncertainties in the load mass equivalent to the actuator end, the system leakage coefficient, the valve's relevant gain coefficient, and the effective elastic modulus of the hydraulic oil. Nonlinear uncertainties include unmodeled high-order valve core dynamics and external disturbances. With the development of industrial modernization, higher requirements for the safety, accuracy, and response of pilot-operated multi-way valve control systems have been placed on their control performance, leading to the gradual prominence of latent nonlinear characteristics within the system. At the same time, to control costs, it is often impossible to equip products with multiple sensors, resulting in the unmeasurability of some system states. Therefore, linear control methods designed based on the traditional control theory framework can no longer meet the ever-increasing system performance requirements. It is necessary to study better nonlinear control methods for the characteristics and uncertainties of pilot-operated multi-channel valve control systems.

[0003] Numerous research findings have been published on the nonlinear control problems of electro-hydraulic valve control systems, such as pilot-operated multi-channel valve control systems. Adaptive control, through the rational design of the system's adaptive law, can obtain the system's true parameters and solve the problem of internal parameter uncertainties. However, this method still mainly relies on high-gain feedback strategies for nonlinear problems such as external disturbances, and its slow convergence speed and susceptibility to instability make it difficult to promote the application of adaptive control in practical industrial settings. Traditional sliding mode control can effectively handle the system's inherent parameter and nonlinear uncertainties, but the calculated system control input has discontinuities, easily leading to chattering problems in the motion system. Robust control based on the traditional backstepping framework can attribute the system's inherent uncertainties to system disturbances and, through the design of linear and nonlinear robust feedback terms, mitigate the interference and nonlinearity. Determinism is suppressed, but system uncertainty increases the difficulty of designing the virtual controller in the backstepping method. Strongly nonlinear and high-order systems are prone to differential explosion problems. Active disturbance rejection control can estimate and compensate for matched and unmatched disturbances in high-order systems by estimating system modeling errors and external disturbances, thus improving the robustness of the system. However, active disturbance rejection technology does not constrain the system state. When the motion command approaches the stroke of the controlled actuator, it is still prone to internal shocks in the actuator due to slight overshoot, which affects the service life of the system. Although traditional state constraint control can constrain the position and velocity states of the system, its control effect depends on the feedback of the full state information of the system, which places high demands on the sensors. Summary of the Invention

[0004] The purpose of this invention is to provide a position control method for a pilot-driven multi-channel valve control system that has state constraint capability, system state observation capability, and strong robustness. It can not only achieve constraint on the output state and compensation for unknown disturbances based solely on the output state feedback, but also avoid the differential explosion problem in traditional backstepping control through the command filter, thereby achieving high-precision tracking performance.

[0005] The technical solution to achieve the objective of this invention is: a position control method for a pilot-operated multi-way valve control system considering output state constraints, comprising the following steps:

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

[0007] Step 2: Based on the mathematical model of the pilot-operated multi-channel valve control system, design a nonlinear position controller that considers output state constraints, and proceed to Step 3.

[0008] Step 3: Apply Lyapunov stability theory to prove the stability of the nonlinear position controller considering output state constraints, and obtain the result that the system tracking error is stable.

[0009] Compared with the prior art, the significant advantages of this invention are: (1) It achieves the constraint of system state x1 based solely on the feedback of system state x1, estimates and obtains the unmeasurable system states x2 and x3 as well as the unknown dynamics of the system, and reduces the system's dependence on sensors; (2) By using the instruction filter, it simplifies the differentiation process of the virtual controller, avoids the differential explosion problem in traditional backstepping control, achieves high-precision tracking performance, and the simulation results verify its effectiveness. Attached Figure Description

[0010] Figure 1 This is a schematic diagram illustrating the principle of the pilot-operated multi-channel valve control system position control method that considers output state constraints according to the present invention.

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

[0012] Figure 3 This is a graph showing the tracking process of the system's actual movement position relative to the system's desired tracking position command under the action of the SCRC controller designed in this invention.

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

[0014] Figure 5 This is a comparison curve of the tracking error of the system under the action of the SCRC controller designed in this invention and the VFPID controller commonly used in industry.

[0015] Figure 6 This is a system state observation curve diagram of the SCRC controller designed in this invention.

[0016] Figure 7 This is an interference observation curve of the SCRC controller designed in this invention.

[0017] Figure 8 This is a comparison diagram of the system output state constraints under the action of the SCRC controller designed in this invention and the VFPID controller commonly used in industry.

[0018] Figure 9 This is a graph showing the input voltage signal of the pilot-operated multi-way valve in the system under the action of the SCRC controller designed in this invention. Detailed Implementation

[0019] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0020] Combination Figure 1 and Figure 2The position control method for a pilot-operated multi-way valve control system considering output state constraints, as described in this invention, includes the following steps:

[0021] Step 1: Establish a mathematical model of the pilot-operated multi-way valve control system.

[0022] In step 1-1, the pilot-operated multi-way valve control system is applied to the linear motion of large equipment driven by the hydraulic system. The load is fixedly connected to the piston rod on the hydraulic cylinder. The pilot-operated multi-way 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, the hydraulic cylinder, and the pilot-operated multi-way valve, the mathematical model of the pilot-operated multi-way valve control system is obtained.

[0023] According to Newton's second law, the force balance equation of a pilot-operated multi-channel valve control system is:

[0024]

[0025] In equation (1), m represents the weight of the load fixed to the piston rod of the hydraulic cylinder, and y represents the displacement of the piston rod of the hydraulic cylinder. This indicates the speed of the hydraulic cylinder piston rod. The value P represents the acceleration of the hydraulic cylinder piston rod, A represents the effective working area of ​​the hydraulic cylinder piston, and P represents the oil pressure difference between the inlet and outlet oil chambers on both sides of the cylinder. L =P1-P2, where P1 represents the oil pressure in the inlet chamber of the hydraulic cylinder, P2 represents the oil pressure in the outlet chamber of the hydraulic cylinder, B represents the viscous damping coefficient of the hydraulic cylinder, f(t) represents the external unknown disturbance and the internal unmodeled disturbance of the system, and t represents time.

[0026] Then equation (1) can be rewritten as:

[0027]

[0028] In a pilot-operated multi-way valve-controlled system, considering the system's oil characteristics and neglecting external oil leakage, the pressure dynamic equation is:

[0029]

[0030] In equation (3), β e V represents the effective elastic modulus of the hydraulic oil used in the system. t Indicates the control volume of the hydraulic cylinder. P represents L The first derivative, C t Q represents the leakage coefficient within the hydraulic cylinder. L The load flow rate of the hydraulic cylinder is represented by q(t), and the unmodeled disturbance is represented by q(t).

[0031] A pilot-operated multi-way valve consists of a pilot valve and a main valve. Ignoring the higher-order spool dynamics of the pilot and main valves, we assume that the input voltage signal u acting on the pilot-operated multi-way valve and the output force F acting on the spool of the main valve are the output oil pressure from the pilot valve and the output pressure F from the pilot valve. RF Proportional relationship, output force F RF With the displacement x of the main valve core v A proportional relationship, that is, satisfying:

[0032]

[0033] In equation (4), k f k represents the gain coefficient of the input voltage signal relative to the output force of the pilot valve. y The main valve spool displacement is represented by the gain coefficient relative to the pilot valve output force. Therefore, the main valve flow rate Q of the pilot-operated multi-way valve is... L The input voltage signal u of the pilot-operated multi-way valve has the following relationship:

[0034]

[0035] In equation (5), the flow coefficient k of the pilot-operated multi-way valve main valve is... q =k f k y u represents the input voltage signal of the pilot-operated multi-way valve, P s The system's oil supply pressure is represented by sign(·), which is a function of the intermediate variable · and is defined as follows:

[0036]

[0037] In steps 1-2, to facilitate controller design, state variables are defined: Wherein, system state x1 = y, system state System status Equation (2) is then transformed into a state-space equation:

[0038]

[0039] In equation (7), Denotes the first derivative of x1. This represents the first derivative of x². This represents the first derivative of x³, where the system dynamics are unknown. The first derivative of f(t) is represented by the intermediate variable. intermediate variables intermediate variables

[0040] In steps 1-3, reasonable assumptions are made about the system to facilitate controller design, as follows:

[0041] Assumption 1: The system expects to track the position command x d It is second-order continuous, and the system expects to track bounded position, velocity, and acceleration commands.

[0042] Assumption 2: The unknown dynamics of the system D(t) satisfy:

[0043]

[0044] In equation (8), δ1, δ2, and δ3 are all unknown positive constants. Let D(t) represent the first derivative of D(t), H(t) be an unknown continuous function, f(t) represent the unknown external disturbances and the unmodeled internal disturbances of the system, and t represent time.

[0045] Assumption 3: Based on the force balance equation characterized by equation (1) and the constraints imposed by Assumption 1 on the system's desired tracking position command, P is derived. L If f1, f2, and f3 are Lipschitz functions of system states x2 and x3, then according to the definitions of intermediate variables f1, f2, and f3, we can further conclude that f1 is a Lipschitz function of system states x2 and x3 in the actual scope, f2 is a Lipschitz function of system state x2 in the global scope, and f3 is a Lipschitz function of system state x3 in the global scope.

[0046] Assumption 4: The system state x1 = y is completely measurable.

[0047] Proceed to step 2.

[0048] Step 2: Based on the mathematical model of the pilot-operated multi-channel valve control system, design a nonlinear position controller that considers output state constraints, as follows:

[0049] In step 2-1, to obtain the unmeasurable system states x2 and x3 and the unknown system dynamics D(t), an extended state observer is constructed to estimate the system states and unknown dynamics, as follows:

[0050] Take the extended state variable x e =D(t), then based on assumption 2, in, Let H(t) denote the first derivative of D(t), and let H(t) denote the unknown continuous function.

[0051] Combining equation (7), the following extended state observer is designed:

[0052]

[0053] In equation (9), the extended state observer variable Indicates system state xi The observed estimates, extended state observer variables Represents the extended state variable x e The observed estimates, express The first derivative, express The first derivative, ω represents the gain of the extended state observer, and intermediate variables. This indicates the intermediate variable f i The observed estimates, with subscripts i = 1, 2, 3;

[0054] Combining equations (7) and (9), the dynamic error of the extended state observer is:

[0055]

[0056] In equation (10), and Both represent the observation estimation error of the extended state observer of the system state. intermediate variables This indicates the intermediate variable f i The observation estimation error, Subscript i = 1, 2, 3.

[0057] Define intermediate variables: Among them, intermediate variables intermediate variables intermediate variables intermediate variables The dynamic error of the extended state observer characterized by equation (10) is further rewritten as:

[0058]

[0059] In equation (11), Let A represent the first derivative of the intermediate variable ε. ESO B ESO With C ESO Both are matrices characterizing the dynamic error of the extended state observer, defined as:

[0060]

[0061]

[0062] In step 2-2, the error z1 is defined as ζ1 - α0, where the intermediate variable ζ1 is the state variable obtained by transforming the system state x1 through a nonlinear transformation function, and α0 represents the virtual controller of ζ1, as detailed below:

[0063] To simplify the controller design process, the following instruction filter is designed:

[0064]

[0065] In equation (14), α 1f This represents the filter value of α1, where α1 represents the virtual controller of x2. Represents α 1f The first derivative, filter gain τ1 > 0, filter error e1 = α1 1f -α1, η1(t) represent functions that are always positive and satisfy... Where ν represents the integration variable, c c1 Let η represent any constant. 1max Denotes a constant that is always positive. The upper bound l1>0, This represents the estimated value of l1.

[0066] The update law for:

[0067]

[0068] In equation (15), σ1 represents positive gain.

[0069] Design the following nonlinear transformation function:

[0070]

[0071] In equation (16), ρ1 represents the boundary value designed for system state x1. ρ1 is a positive constant and satisfies:

[0072]

[0073] In equation (17), This indicates that the system expects the tracking position command x. d The upper realm, This indicates that the system expects the tracking position command x. d The lower bound.

[0074] Design the virtual controller α0 as follows:

[0075]

[0076] Differentiating with respect to z1, we get:

[0077]

[0078] In equation (19), Denotes the first derivative of ζ1. Denotes the first derivative of α0. x represents d First derivative, intermediate variable error intermediate variables

[0079] The virtual controller α1 is designed as follows:

[0080]

[0081] In equation (20), the gain k1 > 0.

[0082] Substituting equation (20) into equation (19), we get:

[0083]

[0084] In steps 2-3, the error is defined. error in, α represents the observed estimate of the system state x2. 1f This represents the filter value of α1, where α1 represents the virtual controller of x2. α represents the observed estimate of the system state x3. 2f Let α2 represent the filter value of x3, and let x3 represent the virtual controller. To simplify the controller design process, the following instruction filter is designed:

[0085]

[0086] In equation (22), the filter gain τ2 > 0. Represents α 2f The first derivative of α2, the filtering error e2=α 2f -α², η²(t) represent functions that are always positive and satisfy the following conditions: Where ν represents the integration variable, c c2 Let η represent any constant. 2max Denotes a constant that is always positive. The upper bound l2 > 0, This represents the estimated value of l2.

[0087] The update law for:

[0088]

[0089] In equation (23), σ2 represents a positive gain;

[0090] Differentiating with respect to z2, we get:

[0091]

[0092] The virtual controller α2 is designed as follows:

[0093]

[0094] In equation (25), the gain k2 > 0, α 2a The model compensation term for virtual controller α2, α 2s1 Let α represent the linear robust term of the virtual controller α2. 2s2 The nonlinear robustness term of the virtual controller α2.

[0095] Substituting equation (25) into equation (24), we get:

[0096]

[0097] Differentiating with respect to z3, we get:

[0098]

[0099] The input voltage signal u of the pilot-operated multi-way valve is designed as follows:

[0100]

[0101] In equation (28), the gain k3 > 0.

[0102] Substituting equation (28) into equation (27), we get:

[0103]

[0104] Proceed to step 3.

[0105] Step 3: Apply Lyapunov stability theory to prove the stability of the nonlinear position controller considering output state constraints, and obtain the result that the system tracking error is stable, as follows:

[0106] The Lyapunov function is defined as follows:

[0107]

[0108] In equation (30), the error z1 = ζ1 - α0, the error error intermediate variables intermediate variables σ1 and σ2 represent positive gains, and the intermediate variable ε = [ε1, ε2, ε3, ε4]. T intermediate variables intermediate variables intermediate variables intermediate variables intermediate variable ε TLet P be the transpose of the intermediate variable ε, and let P be a positive definite matrix. The filtering error is e1 = α. 1f -α1, Filtering error e2=α 2f -α2.

[0109] Differentiating equation (30) and substituting it into equations (11), (15), (21), (23), (26), and (29), we obtain:

[0110]

[0111] In equation (31), |·| represents the absolute value of the intermediate variable ·.

[0112] According to equation (12), we obtain A. ESO Given a Hurwitz matrix, we can find a positive definite matrix P that satisfies:

[0113] PA ESO +A ESO T P = -I 4×4 (32)

[0114] In equation (32), the intermediate variable A ESO T Representing matrix A ESO The transpose of the matrix, intermediate variable I 4×4 This represents a fourth-order identity matrix.

[0115] Based on assumptions 2 and 3, we can conclude that:

[0116]

[0117] In equation (33), the intermediate variable This indicates the intermediate variable f i The observation estimation error is represented by constants L1, L2, L3, L4, L5, which represent Lipschitz constants with subscripts i = 1, 2, 3.

[0118] In addition, it is noted that:

[0119]

[0120] Then we have:

[0121]

[0122] Substituting equations (32), (33), and (35) into equation (31), equation (31) is further rewritten as:

[0123]

[0124] In equation (36), the intermediate variable |μ1| max The upper bound of the absolute value of μ1, an intermediate variable. constant λ max (P) represents the largest eigenvalue of matrix P, |u| max Denotes the upper bound of the absolute value of u.

[0125] intermediate variable C N =max{L1|u| max / ω 2 L2|u| max / ω 2 ,L3|u| max / ω 2 ,L4 / ω 2 L5 / ω 2};

[0126] Define the intermediate variables χ and Λ as follows:

[0127] χ=[z1,z2,z3,ε1,ε2,ε3,ε4,e1,e2] (37)

[0128]

[0129] In equation (38), the intermediate variables Λ1 and Λ2 are respectively:

[0130]

[0131] By adjusting the gains k1, k2, k3, τ1, and τ2, the matrix Λ can be made positive definite, and thus:

[0132]

[0133] In equation (40), the intermediate variable χ T This represents the transpose of the intermediate variable χ, where M = χ. T Λχ, intermediate variable ν1(t)=η1(t)+0.5c1, intermediate variable ν2(t)=η2(t)+0.5c1.

[0134] Integrating both sides of equation (40), we have:

[0135]

[0136] In equation (41), V(0) is a constant and ν represents the integral variable.

[0137] Considering that c1 is a constant, equation (41) can be further written as:

[0138]

[0139] In equation (42), η 1max and η 2max It represents a constant that is always positive.

[0140] From equation (42), we can conclude that V is bounded and the integral of M is bounded, and thus we can conclude that all states of the system are bounded.

[0141] Therefore, we can conclude that by adjusting the gains k1, k2, k3, ω and the filter gains τ1, τ2, the first nonlinear position controller considering output state constraints designed for a pilot-operated multi-channel valve control system can innovatively achieve the results of bounded output state error and constrained input state. A schematic diagram of the position controller principle for a pilot-operated multi-channel valve control system considering output state constraints is shown below. Figure 1 As shown.

[0142] Example

[0143] To evaluate the performance of the designed controller, the physical parameters of the pilot-operated multi-channel valve control system in the simulation are shown in Table 1:

[0144] Table 1 System Physical Parameters

[0145] parameter numerical values physical parameters numerical values <![CDATA[(m 2 )]]> <![CDATA[2×10 -4 ]]> <![CDATA[β e (Well)]]> 2×10 (kg) 40 B(N·s / m) 80 (N·s)) <![CDATA[7×10 -12 ]]> <![CDATA[K q (m / V)]]> 9.25×1 <![CDATA[(m 3 )]]> <![CDATA[1×10 -3 ]]> <![CDATA[P s (MPa)]]> 7

[0146] Given the system's desired tracking position command is

[0147] The following controller is used for comparison in the simulation:

[0148] Position controller for pilot-operated multi-way valve control system (SCRC) considering output state constraints: with gain k1 = 250, k2 = 100, k3 = 50, σ1 = 1, σ2 = 1, τ1 = 3000, τ2 = 3000, and ρ1 = 0.52.

[0149] VFPID Controller: The steps for selecting VFPID controller parameters are as follows: First, ignoring the nonlinear dynamics of the pilot-operated multi-channel valve control system, a set of controller parameters is obtained by adjusting the VFPID parameters to achieve optimal tracking performance. The selected controller parameter is k. P =700,k I =400,k D =100, k velocity =0.85.

[0150] The following data are presented: the tracking process curve of the system's actual motion position to the system's desired tracking position command under the action of the SCRC controller; the tracking error of the system under the action of the SCRC controller; a comparison of the tracking errors of the SCRC controller and the VFPID controller; the system state observation of the SCRC controller; the interference observation of the SCRC controller; and the state constraints of the SCRC controller. Figure 3 , Figure 4 , Figure 5 , Figure 6 , Figure 7 and Figure 8 As shown. By Figure 3 and Figure 4 It can be seen that, under the action of the SCRC controller, the position output of the pilot-operated multi-way valve system has a very high tracking accuracy to the command, with an average tracking error of approximately 2.74 × 10⁻⁶. -6 m. From Figure 5 It can be seen that the SCRC controller proposed in this invention has superior tracking performance compared with the VFPID controller commonly used in industry. Figure 6 This demonstrates the SCRC controller's observations of unknown system states, from... Figure 6 As can be seen from this, the obtained observations are relatively accurate. Figure 7 The observations of the SCRC controller regarding unknown system disturbances are shown. Figure 8 This demonstrates the state constraint capabilities of the SCRC controller compared to the commonly used VFPID controller in industry. Figure 9 The input voltage signal of the pilot-operated multi-way valve is calculated by the SCRC controller. As can be seen from the figure, the obtained input voltage signal is a low-frequency continuous signal, which is more conducive to execution in practical applications.

Claims

1. A pilot operated multi-way valve control system position control method considering output state constraints, characterized by, Includes the following steps: Step 1: Establish the mathematical model of the pilot-operated multi-way valve control system, then proceed to Step 2; Step 2: Based on the mathematical model of the pilot-operated multi-channel valve control system, design a nonlinear position controller that considers output state constraints, as follows: Step 2-1: To obtain the unmeasurable system states x2 and x3, as well as the unknown system dynamics D(t), an extended state observer is constructed to estimate the system states and unknown dynamics; specifically as follows: Taking the extended state variable x e = D(t), then based on assumption 2, we have, where, denotes the first derivative of D(t), and H(t) denotes an unknown continuous function; Combining equation (7), the following extended state observer is designed: In formula (9), the augmented state observer variable represents an observation estimate of the system state x i , the augmented state observer variable represents an observation estimate of the augmented state variable x e , represents a first derivative of , represents a first derivative of , ω represents a gain of the augmented state observer, and an intermediate variable represents an observation estimate of the intermediate variable f i , with the subscript i = 1, 2, 3; Combining equations (7) and (9), the dynamic error of the extended state observer is: In formula (10), with denote the observation estimation error of the extended state observer for the system state, the intermediate variable denotes the observation estimation error of the intermediate variable f i , the index i = 1, 2, 3; Define the intermediate variables: where the intermediate variables The intermediate variables The intermediate variables The intermediate variables The dynamic error of the extended state observer represented by equation (10) is further rewritten as: In formula (11), denotes the first derivative of the intermediate variable ε, the intermediate variable A ESO , B ESO and C ESO are all matrices representing the dynamic error of the expansion state observer, defined as: Step 2-2, define error z1 = ζ1 - α0, where the intermediate variable ζ1 is a state variable obtained by converting the system state x1 through a nonlinear conversion function, and α0 represents the virtual controller of ζ1, to ensure that the system state x1 can accurately track the system desired tracking position instruction x d and realize the state constraint of x1, it is necessary to ensure that the error z1 is bounded, as follows: To simplify the controller design process, the following instruction filter is designed: In formula (14), α 1f represents a filtered value of α1, which represents a virtual controller of x2, represents a first derivative of α 1f , a filtered gain τ1>0, and a filtered error e1 of α1 = α 1f -α1, η1(t) represents a function that is always positive and satisfies where v represents an integral variable, c c1 represents an arbitrary constant, and η 1max represents a constant that is always positive, an upper bound l1>0 of represents an estimated value of l1; The update law for: In equation (15), σ1 represents a positive gain; Design the following nonlinear transformation function: In equation (16), ρ1 represents the boundary value designed for system state x1. ρ1 is a positive constant and satisfies: In equation (17), This indicates that the system expects the tracking position command x. d The upper realm, x d This indicates that the system expects the tracking position command x. d The lower bound; Design the virtual controller α0 as follows: Differentiating with respect to z1, we get: In equation (19), Denotes the first derivative of ζ1. Denotes the first derivative of α0. x represents d First derivative, intermediate variable error intermediate variables The virtual controller α1 is designed as follows: In equation (20), the gain k1 > 0; Substituting equation (20) into equation (19), we get: Steps 2-3: Define the error error in, α represents the observed estimate of the system state x2. 1f This represents the filter value of α1, where α1 represents the virtual controller of x2. α represents the observed estimate of the system state x3. 2f Let α2 represent the filter value of x3, and let α2 represent the virtual controller of x3. To ensure that the tracking error z1 is bounded, the error z2 must be bounded. To ensure that the error z2 is bounded, the error z3 must be bounded, as detailed below: To simplify the controller design process, the following instruction filter is designed: In equation (22), the filter gain τ2 > 0. Represents α 2f The first derivative of α2, the filtering error e2=α 2f -α², η²(t) represent functions that are always positive and satisfy the following conditions: Where ν represents the integration variable, c c2 Let η represent any constant. 2max Denotes a constant that is always positive. The upper bound l2 > 0, This represents the estimated value of l2; The update law for: In equation (23), σ2 represents a positive gain; Differentiating with respect to z2, we get: The virtual controller α2 is designed as follows: In formula (25), the gain k2>0, α 2a represents a model compensation term of the virtual controller α2, α 2s1 represents a linear robustness term of the virtual controller α2, α 2s2 represents a nonlinear robustness term of the virtual controller α2. Substituting equation (25) into equation (24), we get: Differentiating with respect to z3, we get: The input voltage signal u of the pilot-operated multi-way valve is designed as follows: In equation (28), the gain k3 > 0; Substituting equation (28) into equation (27), we get: Proceed to step 3; Step 3: Apply Lyapunov stability theory to prove the stability of the nonlinear position controller considering output state constraints, and obtain the result that the system tracking error is stable.

2. The position control method for a pilot-operated multi-channel valve control system considering output state constraints according to claim 1, characterized in that, In step 1, a mathematical model of the pilot-operated multi-channel valve control system is established, as follows: Step 1-1: The pilot-operated multi-way valve control system is applied to the linear motion of large equipment driven by the hydraulic system. The load is fixed to the piston rod on the hydraulic cylinder. The pilot-operated multi-way 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, the hydraulic cylinder and the pilot-operated multi-way valve, the mathematical model of the pilot-operated multi-way valve control system is obtained. Steps 1-2: To facilitate controller design, define state variables and convert the mathematical model of the pilot-operated multi-way valve control system into state-space equations. Steps 1-3: To facilitate controller design, make reasonable assumptions about the system.

3. The position control method for a pilot-operated multi-channel valve control system considering output state constraints according to claim 2, characterized in that, In step 1-1, the pilot-operated multi-way valve control system is applied to the linear motion of large equipment driven by a hydraulic system. The load is fixedly connected to the piston rod on the hydraulic cylinder. The pilot-operated multi-way valve controls the movement of the piston rod on the hydraulic cylinder, thereby driving the load. Based on the dynamic characteristics of the load, the hydraulic cylinder, and the pilot-operated multi-way valve, the mathematical model of the pilot-operated multi-way valve control system is obtained, as follows: According to Newton's second law, the force balance equation of a pilot-operated multi-channel valve control system is: In equation (1), m represents the weight of the load fixed to the piston rod of the hydraulic cylinder, and y represents the displacement of the piston rod of the hydraulic cylinder. This indicates the speed of the hydraulic cylinder piston rod. The value P represents the acceleration of the hydraulic cylinder piston rod, A represents the effective working area of ​​the hydraulic cylinder piston, and P represents the oil pressure difference between the inlet and outlet oil chambers on both sides of the cylinder. L =P1-P2, where P1 represents the oil pressure in the inlet chamber of the hydraulic cylinder, P2 represents the oil pressure in the outlet chamber of the hydraulic cylinder, B represents the viscous damping coefficient of the hydraulic cylinder, f(t) represents the external unknown disturbance and the internal unmodeled disturbance of the system, and t represents time; Then equation (1) can be rewritten as: In a pilot-operated multi-way valve-controlled system, considering the system's oil characteristics and neglecting external oil leakage, the pressure dynamic equation is: In equation (3), β e V represents the effective elastic modulus of the hydraulic oil used in the system. t Indicates the control volume of the hydraulic cylinder. P represents L The first derivative, C t Q represents the leakage coefficient within the hydraulic cylinder. L The load flow rate of the hydraulic cylinder is represented by q(t), and the unmodeled disturbance is represented by q(t). The pilot type multi-way valve includes a pilot valve and a main valve, ignoring the high-order spool dynamics of the pilot valve and the main valve, assuming that the input voltage signal u acting on the pilot type multi-way valve and the output oil pressure pressure of the pilot valve acting on the output force F of the main valve spool RF proportional relationship, the output force F RF proportional relationship with the main valve spool displacement x v proportional relationship, that is, In formula (4), k f represents the input voltage signal relative to the pilot valve output force gain coefficient, k y represents the main valve spool displacement relative to the pilot valve output force gain coefficient, then the main valve flow rate Q L has the following relationship with the input voltage signal u of the pilot operated multi-way valve: In formula (5), k is a flow coefficient of the main valve of the pilot-type multi-way valve q = k f k y u represents an input voltage signal of the pilot-type multi-way valve, P s represents a supply oil pressure of the system, and sign(·) represents a function of an intermediate variable ·, which is defined as:

4. The position control method for a pilot-operated multi-channel valve control system considering output state constraints according to claim 3, characterized in that, In steps 1-2, to facilitate controller design, state variables are defined, and the mathematical model of the pilot-operated multi-way valve control system is transformed into state-space equations, as follows: Define state variables: Wherein, system state x1 = y, system state System status Equation (2) is then transformed into a state-space equation: In equation (7), Denotes the first derivative of x1. This represents the first derivative of x². This represents the first derivative of x³, where the system dynamics are unknown. The first derivative of f(t) is represented by the intermediate variable. intermediate variables intermediate variables 5. The position control method for a pilot-operated multi-channel valve control system considering output state constraints according to claim 4, characterized in that, In steps 1-3, reasonable assumptions are made about the system to facilitate controller design, as follows: Assumption 1: The system is expected to track position command x d is second order continuous, and the system is expected to track position command, velocity command, and acceleration command are all bounded; Assumption 2: The unknown dynamics of the system D(t) satisfy: In equation (8), δ1, δ2, and δ3 are all unknown positive constants. Let D(t) represent the first derivative of D(t), H(t) be an unknown continuous function, f(t) represent the unknown external disturbances and the unmodeled internal disturbances of the system, and t represent time. Assumption 3: The force balance equation characterized by equation (1) is consistent with the system expected tracking position command x d With the restriction made, it is derived that P L is a Lipschitz function with respect to system states x2 and x3, then according to the definition of the intermediate variables f1, f2, f3, it is further derived that f1 is a Lipschitz function with respect to system states x2 and x3 in the actual range, f2 is a Lipschitz function with respect to system state x2 in the global range, and f3 is a Lipschitz function with respect to system state x3 in the global range; Assumption 4: The system state x1 = y is completely measurable; Proceed to step 2.

6. The position control method for a pilot-operated multi-channel valve control system considering output state constraints according to claim 1, characterized in that, In step 3, the stability of the nonlinear position controller considering output state constraints is proved using Lyapunov stability theory, and the results of the system tracking error stabilization are obtained, as follows: The Lyapunov function V is defined as follows: In equation (30), the error z1 = ζ1 - α0, the error error intermediate variables intermediate variables σ1 and σ2 represent positive gains, and the intermediate variable ε = [ε1, ε2, ε3, ε4]. T intermediate variables intermediate variables intermediate variables intermediate variables intermediate variable ε T Let P be the transpose of the intermediate variable ε, and let P be a positive definite matrix. The filtering error is e1 = α. 1f -α1, Filtering error e2=α 2f -α2; The stability was proven using Lyapunov stability theory, and the result showed that the system tracking error was stable.

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