Improved LuGre friction model-based self-adaptive robust control method for electric actuating mechanism

Abstract

The invention discloses a self-adaptive robust control method for an electric actuating mechanism based on an improved LuGre friction model, and the method comprises the steps: building a model architecture of the electric actuating mechanism, and carrying out the compensation of the nonlinear friction in a system through employing the improved LuGre friction model; designing double observers to estimate the non-measurable mane average deformation in the improved LuGre friction model; identifying parameters of the improved LuGre friction model based on a particle swarm optimization algorithm to obtain a mathematical model of the electric actuating mechanism; and according to the electric actuating mechanism model, a self-adaptive sliding mode control strategy is adopted, a nonlinear sliding mode surface switching function is used, and an electric actuating mechanism self-adaptive robust controller based on the improved LuGre friction model is designed through a backstepping method. According to the invention, the influence of non-linear friction on the control system of the electric actuating mechanism is reduced, the robustness of the system is enhanced, and the control performance of the electric actuating mechanism is improved.

Description

Technical Field

[0001] The invention relates to the technical field of electric valves, and in particular to an adaptive robust control method for an electric actuator based on an improved LuGre friction model. Background Art

[0002] With the development of industrial production automation, electric actuators are being used more and more widely, and various control fields have higher and higher requirements on the response speed, control accuracy, and anti-interference performance of electric actuator control systems. Researching and optimizing electric actuator control systems has important theoretical significance and practical value for improving system control accuracy and stability.

[0003] As an important component of the intelligent valve electric actuator, the control system has the goal of accurately controlling the valve position. The existing control method of the intelligent valve electric actuator takes into account the impact of temperature changes on the electric actuator control system, by establishing a three-loop control system based on the current loop, speed loop, and position loop; combining the use of MRAS with a variable gain coefficient to identify the system moment of inertia online, using the moment of inertia as the input of the observer to observe the system load torque online, and adjusting the system controller parameters based on the observation results; monitoring the change of the rotor time constant through the temperature detection circuit, and based on the relationship between the rotor time constant and the electromagnetic torque, temperature compensation is performed for the error caused by temperature in the load torque observation of the control system.

[0004] Although the electric actuator disclosed in the prior art has temperature compensation for the error caused by temperature on the load torque observation of the control system. However, nonlinear friction is the main interference source of the control system in the electric actuator. It is time-varying and often causes problems such as steady-state error, limit cycle and low-speed creep in the system. If friction is not controlled, the control performance of the electric actuator will be greatly reduced. In the prior art of electric actuator control algorithm, the influence of nonlinear friction on control performance has not been fully considered.

[0005] Therefore, how to provide an adaptive robust control method for an electric actuator that reduces the influence of nonlinear friction on the control system of the electric actuator is a technical problem that needs to be solved urgently by those skilled in the art. Summary of the invention

[0006] In view of the above research status and existing problems, the present invention adopts a compensation method based on an improved LuGre friction model to reduce the influence of nonlinear friction on the control system of the electric actuator and improve the control performance of the electric actuator.

[0007] The present invention first provides an adaptive robust control method for an electric actuator based on an improved LuGre friction model, comprising the following steps:

[0008] S1: Establishing an electric actuator model framework, including an electric actuator execution model and an improved LuGre friction model; the electric actuator execution model receives a target input of the electric actuator system and obtains a dynamic response of the electric actuator, and the improved LuGre friction model changes the dynamic response through friction;

[0009] S2: constructing a system state observer and a bristle deformation observer, which are used to respectively estimate the system state and bristle deformation of the electric actuator in real time; using the real-time estimation results based on the particle swarm optimization algorithm to identify the model parameters of the improved LuGre friction model, and obtaining the mathematical model of the electric actuator;

[0010] S3: According to the mathematical model of the electric actuator, an adaptive sliding mode control strategy is adopted, combined with a nonlinear sliding mode surface switching function, and an adaptive robust controller for the electric actuator based on the improved LuGre friction model is obtained through backstepping design.

[0011] Preferably, the electric actuator model architecture is:

[0012]

[0013] Among them, the output shaft model of the permanent magnet synchronous motor is:

[0014] T f is the LuGre friction torque; J 1 is the equivalent moment of inertia on the motor output shaft; B is the viscous damping coefficient; T e is the driving torque, i.e. the electromagnetic torque of the motor; i is the reduction ratio of the gear transmission reduction mechanism; M is the output torque of the gear reduction mechanism; M 1 is the resistance torque, i.e. the output torque of the motor shaft; θ is the output angle of the gear transmission reduction mechanism, is the speed of the output shaft of the gear transmission reduction mechanism; θ 1 is the mechanical angle of the motor output shaft, is the speed of the motor output shaft; T e is the driving torque, that is, the electromagnetic torque of the motor.

[0015] Preferably, the improved LuGre friction model is:

[0016]

[0017] Where, T f is the LuGre friction torque; σ 0 is the friction stiffness coefficient; σ 1 is the friction damping coefficient; B 0is the viscous friction coefficient; θ is the output angle of the gear transmission reduction mechanism; z is the average displacement change between the bristle contact surfaces; is the parameter of the Stribeck effect; θ s is the stribeck speed; T s is the maximum static friction; T c is the Coulomb friction force.

[0018] Preferably, the step of constructing a system state observer comprises:

[0019] A Kalman filter or a Luenberger observer is constructed using the target input of the electric actuator system and the measured output of the electric actuator system, and the state of the electric actuator system is estimated in real time through the error between the target input and the measured output, wherein the state includes the movement speed and / or displacement of the electric actuator.

[0020] Preferably, the steps of constructing a bristle deformation observer include:

[0021] A method for constructing a sliding mode observer or a dynamic observer using the target input of the electric actuator system and the measured output of the electric actuator system estimates the deformation of the bristle in real time through the error between the target input and the measured output.

[0022] Preferably, the step of identifying the model parameters of the improved LuGre friction model comprises:

[0023] Initialization: randomly generate a specified number of particles, each particle represents a set of parameters of the improved LuGre friction model;

[0024] Fitness evaluation: evaluating the fitness of the particle according to the error between the state estimate output by the model and the measured output of the electric actuator system;

[0025] Update particle position: Update the particle position and velocity according to the particle's own historical best position and the global best position;

[0026] Iteration: Repeat the fitness evaluation and position update process until the stopping condition is met, then stop the iteration and output the parameters of the improved LuGre friction model.

[0027] Preferably, the adaptive sliding mode control strategy comprises the following steps:

[0028] S31: Select a sliding surface S that is compatible with the electric actuator system:

[0029] S = e + λ∫edt;

[0030] Where, e is the tracking error and λ is a positive gain;

[0031] S32: Design of sliding surface using nonlinear switching function:

[0032]

[0033] Among them, η is a design parameter, α and β are parameters related to system characteristics;

[0034] S33: Design of control law based on sliding mode control principle:

[0035]

[0036] Where K is the control gain, is an estimate of the control input.

[0037] Preferably, the steps of obtaining an adaptive robust controller for an electric actuator based on an improved LuGre friction model by backstepping design include:

[0038] S34: Introducing virtual control input u 1 , used to control the state of the system:

[0039] u 1 =-K 1 ·SF friction ;

[0040] Among them, K 1 is the control gain corresponding to the virtual control input;

[0041] S35: Design an adaptive parameter update law based on the system status:

[0042]

[0043] Among them, γ is the adaptive gain, which is used to guide the convergence of parameters.

[0044] Compared with the prior art, it has the following beneficial effects:

[0045] The present invention proposes a novel adaptive robust control method for electric actuators based on an improved LuGre friction model. By using an adaptive sliding mode control strategy and backstepping method, combined with the improved LuGre friction model, an electric actuator controller with strong robustness and adaptability to uncertainty can be designed. The control algorithm can effectively compensate for the nonlinear friction in the electric actuator, improve the convergence speed and tracking accuracy of the system, and enhance the robustness of the system. This method can effectively improve the control accuracy and stability of the electric actuator under complex working conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention, and for ordinary technicians in this field, other drawings can be obtained based on the provided drawings without creative work.

[0047] Figure 1 A control block diagram of an electric actuator provided in an embodiment of the present invention;

[0048] Figure 2 A flow chart of an adaptive robust control method for an electric actuator provided by an embodiment of the present invention;

[0049] Figure 3 A dual closed-loop control diagram of position and current of an electric actuator provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0050] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0051] like Figure 1-2 As shown, the adaptive robust control method of an electric actuator based on an improved LuGre friction model disclosed in an embodiment of the present invention comprises the following steps:

[0052] S1: Establish the electric actuator model architecture, including the electric actuator execution model and the improved LuGre friction model; the electric actuator execution model receives the target input of the electric actuator system and obtains the dynamic response of the electric actuator, and the improved LuGre friction model changes the dynamic response through friction; the improved LuGre friction model is used to compensate for the nonlinear friction in the system; S2: Construct the system state observer and the bristle deformation observer, which are used to perform real-time estimation of the electric actuator system state and the bristle deformation respectively; based on the particle swarm optimization algorithm, the model parameters of the improved LuGre friction model are identified using the real-time estimation results to obtain the mathematical model of the electric actuator; S3: According to the mathematical model of the electric actuator, an adaptive sliding mode control strategy is adopted, combined with the nonlinear sliding surface switching function, and the electric actuator adaptive robust controller based on the improved LuGre friction model is obtained through the backstepping method.

[0053] In one embodiment, the electric actuator execution model includes a drive motor model and an electric actuator transmission system model. The drive motor is a permanent magnet synchronous motor. In a two-phase rotating dq coordinate system, the mathematical model of the motor is:

[0054]

[0055] In the formula, u d ,u q is the d,q axis voltage;

[0056] i d ,i q is the d,q axis current;

[0057] ψ d , ψ q is the d,q axis magnetic flux;

[0058] L d ,L q is the d,q axis inductance;

[0059] R s is the phase resistance;

[0060] ω r is the rotor angular velocity;

[0061] ψ f is a permanent magnetic chain;

[0062] T e is the electromagnetic torque;

[0063] p is the number of motor pole pairs.

[0064] The output shaft model of permanent magnet synchronous motor is:

[0065]

[0066] In the formula, J 1 is the equivalent moment of inertia on the motor output shaft, kg·m 2 ;

[0067] B is the viscous damping coefficient;

[0068] M 1 is the resistance torque, i.e. the output torque of the motor shaft, N·m;

[0069] T e is the driving torque, i.e. the electromagnetic torque of the motor, N·m;

[0070] K is the torsional stiffness of the motor shaft, N·m / rad;

[0071] θ 1 is the mechanical angle of the motor output shaft.

[0072] In this embodiment, the electric actuator transmission system model adopts a gear transmission reduction mechanism model. Due to the high stiffness of the gear reduction mechanism, it is usually regarded as a first-order linear transmission system:

[0073]

[0074] Where θ is the output angle of the gear transmission reduction mechanism, °:

[0075] i is the reduction ratio of the gear transmission reduction mechanism;

[0076] M is the output torque of the gear reduction mechanism, N·m.

[0077] In one embodiment, the electric actuator model architecture is:

[0078]

[0079] Among them, the output shaft model of the permanent magnet synchronous motor is:

[0080] T f is the LuGre friction torque; J 1 is the equivalent moment of inertia on the motor output shaft; B is the viscous damping coefficient; T e is the driving torque, i.e. the electromagnetic torque of the motor; i is the reduction ratio of the gear transmission reduction mechanism; M is the output torque of the gear reduction mechanism; M 1 is the resistance torque, i.e. the output torque of the motor shaft; θ is the output angle of the gear transmission reduction mechanism; θ 1 is the mechanical angle of the motor output shaft; T e is the driving torque, that is, the electromagnetic torque of the motor.

[0081] In one embodiment, the improved LuGre friction model is:

[0082]

[0083] Where, T f is the LuGre friction torque; σ 0 is the friction stiffness coefficient; σ 1 is the friction damping coefficient; B 0 is the viscous friction coefficient; θ is the output angle of the gear transmission reduction mechanism; z is the average displacement change between the bristle contact surfaces; is the parameter of the Stribeck effect; θ s is the stribeck speed; T s is the maximum static friction; T c is the Coulomb friction force.

[0084] It should be noted that in the electric actuator model architecture constructed in this embodiment, the relationship between the drive motor model, the electric actuator transmission system model and the improved LuGre friction model is as follows:

[0085] The input voltage (u) of the drive motor affects the current (i) and torque (T) transmitted to the actuator. The actuator responds to the input torque (T) and load torque (T e ) produces an angle or position change that affects the angular velocity (ω).

[0086] The friction model uses the friction force (T f ) affects the dynamic response of the actuator, and thus affects the position and speed output of the actuator.

[0087] Through the coupling of the above models, the input-output relationship of the entire system can be established. Usually, numerical simulation is required to analyze the dynamic behavior and stability of the system.

[0088] like Figure 1 As shown, the whole system is a closed-loop control system, which realizes precise control of the mechanical position through sensor feedback and controller instructions.

[0089] Position input is the target input of the control system, usually refers to the input signal, telling the system where to move to or what state to achieve.

[0090] The position output is the actual output part of the control system, which represents the response of the system to the input signal, such as moving to a specified position.

[0091] The controller is the brain of the system, receiving position input signals, processing them, and issuing commands to control the motor.

[0092] The motor is an actuator that drives the mechanical device according to the instructions of the controller to achieve movement or adjustment of position.

[0093] Mechanical transmission device is a device that converts the rotational motion of an electric motor into the required linear motion or other forms of motion, such as gears, belts, etc.

[0094] The power drive provides the required power to ensure that the electric motor can work properly and drive the mechanical transmission device.

[0095] Position sensors are used to detect the actual position or status of the system and feed this information back to the controller so that the controller can make adjustments to ensure accurate control of the system.

[0096] like Figure 3 As shown, the entire system is a highly integrated motor control system that achieves precise and efficient control of the motor through various control algorithms and sensor feedback.

[0097] Figure 3 In the control algorithm, the adaptive robust controller can adapt to changes in system parameters and resist external interference. The PI current controller, that is, the proportional-integral controller, is used for current control to maintain the stable operation of the motor by adjusting the current. "Space" refers to the coordinate transformation space in motor control. The PARK transformation is used to transform the current and voltage of the motor from a synchronous rotating coordinate system to a stationary coordinate system to simplify the control algorithm. The PARK transformation and the CLARK transformation are used to convert the expression of current and voltage in motor control to simplify the control strategy. Vector control is a motor control strategy that achieves efficient and precise control by controlling the flux and torque of the motor. The inverter is a key component in the motor control system, which converts direct current into alternating current to drive the motor.

[0098] The motor is the power source of the entire system, while the mechanical transmission system converts the motor's rotational motion into other forms of mechanical motion. qref and u q are the variables in the control algorithm, where i qref represents the reference value of the current, and u q represents the voltage associated with this current. d and u b It is a voltage variable related to motor control.

[0099] In motor control, modulation usually refers to PWM (pulse width modulation) technology, which is used to control the voltage waveform output by the inverter. Speed ​​and angle are important parameters of the motor's operating status, which are usually measured by encoders. The encoder provides the actual speed and angle information of the motor, which is crucial for the closed-loop control system to ensure that the motor runs according to the predetermined parameters.

[0100] The control method of this embodiment is a current-position double closed loop, and the motor adopts i d =0 vector control strategy, the inner loop is the current loop, and the outer loop is the adaptive robust control method proposed in this embodiment.

[0101] In one embodiment, the steps of constructing a system state observer include:

[0102] A Kalman filter or a Luenberger observer is constructed using the target input of the electric actuator system and the measured output of the electric actuator system. The state of the electric actuator system is estimated in real time through the error between the target input and the measured output. The state includes the movement speed and / or displacement of the electric actuator, that is, the output of the electric actuator system.

[0103] In specific execution, the steps performed by the state observer include prediction step and correction step:

[0104] Prediction step: According to the electric actuator execution model and the target input of the electric actuator system, the state estimation of the predicted system dynamic response is obtained.

[0105] Correction step: Use the actual output data of the electric actuator system measured by the sensor to correct the predicted state estimate to reduce the estimation error.

[0106] In one embodiment, the steps of constructing a bristle deformation observer include:

[0107] The method of constructing a sliding mode observer or a dynamic observer by using the target input of the electric actuator system and the measured output of the electric actuator system, and the deformation of the bristle is estimated in real time through the error between the target input and the measured output.

[0108] In one embodiment, the step of identifying model parameters of the improved LuGre friction model includes:

[0109] S21: Initialization: randomly generate a specified number of particles, each particle represents a set of parameters of the improved LuGre friction model, such as friction stiffness coefficient, friction damping coefficient, viscous friction coefficient, etc.;

[0110] S22: Fitness evaluation: The fitness of the particle is evaluated based on the error between the state estimate output by the model and the measured output of the electric actuator system, such as the mean square error;

[0111] S23 updates particle position: updates the particle position and velocity according to the particle's own historical best position and the global best position;

[0112] S24: Iteration: Repeat the fitness evaluation and position update process until the stopping condition is met, then stop the iteration, such as reaching the maximum number of iterations or error convergence, and output the parameters of the improved LuGre friction model.

[0113] In this embodiment, the following steps are also included:

[0114] S25: Conduct simulation experiments on the designed dual observer and parameter identification algorithm:

[0115] The actual system data is used for testing to verify the accuracy of the observer and the effectiveness of parameter identification.

[0116] Analyze the impact of different parameters on the model prediction performance.

[0117] S26: Result analysis:

[0118] Compare the observer's estimated results with the true value to evaluate the accuracy of the estimate.

[0119] Analyze the convergence speed and effect of particle swarm optimization algorithm in parameter identification.

[0120] The application effects of dual observer design and particle swarm optimization in the improved LuGre friction model are directly obtained.

[0121] Through this method, the unmeasurable average deformation of the bristles can be effectively estimated, and the accurate identification of the model parameters can be achieved through the optimization algorithm.

[0122] In one embodiment, the adaptive sliding mode control strategy includes the following steps:

[0123] S31: Select a sliding surface S that is compatible with the electric actuator system:

[0124] S = e + λ∫edt;

[0125] Where, e is the tracking error and λ is a positive gain;

[0126] S32: Design of sliding surface using nonlinear switching function:

[0127]

[0128] Among them, η is a design parameter, α and β are parameters related to system characteristics;

[0129] S33: Design of control law based on sliding mode control principle:

[0130]

[0131] Where K is the control gain, is an estimate of the control input, F friction is the friction torque.

[0132] It should be noted that F friction It is described by the improved LuGre friction model. The friction torque is an important disturbance factor in the electric actuator control system, has nonlinear characteristics, and will affect the dynamic response and control performance of the system.

[0133] Control Input Including the control quantity of the motor. The estimation of the control input of the control rate is related to the following parameters:

[0134] Electromagnetic torque of the motor: that is, the driving torque generated by the motor, which is usually determined by the input signal of the control system and affects the output of the motor.

[0135] Improved friction torque in LuGre friction model: LuGre friction model is used to compensate for the influence of friction, and the friction torque affects the dynamic response of the system. This friction torque is closely related to the nonlinear friction phenomenon in the system and is the focus of compensation of the adaptive sliding mode control strategy.

[0136] System state estimation: The control input is also related to the state of the electric actuator, such as the position and speed of the motor, which are estimated in real time by the observer to help determine the current system behavior. Specifically, the system state observer and the bristle deformation observer provide real-time estimates of the system state, which in turn affects the calculation of the control input.

[0137] Interference and disturbance: The control input not only depends on the known model, but also needs to consider disturbance and uncertainty factors, which may come from the friction of the system, load changes, etc. Especially when the friction model is complex, the output of the disturbance observer will affect the control input.

[0138] In this embodiment, the steps of obtaining an adaptive robust controller of an electric actuator based on an improved LuGre friction model by backstepping design include:

[0139] S34: Introducing virtual control input u 1 , used to control the state of the system:

[0140] u 1 =-K 1 ·SF friction ;

[0141] Among them, K 1 is the control gain corresponding to the virtual control input;

[0142] S35: Design an adaptive parameter update law based on the system status:

[0143]

[0144] Among them, γ is the adaptive gain, which is used to guide the convergence of parameters.

[0145] In this embodiment, after S35, the following steps are also included:

[0146] S36: Stability Analysis:

[0147] By constructing the Lyapunov function (V), the stability of the system is analyzed to ensure that the system error can converge to zero under the action of the control law and the adaptive law.

[0148]

[0149] By analyzing the derivative of (V), we can prove the asymptotic stability of the system.

[0150] In this embodiment, after the controller design is completed, a simulation tool (such as MATLAB / Simulink) is used to perform system simulation to verify the performance of the controller.

[0151] Test control effectiveness under different operating conditions, including disturbances, model uncertainties, etc.

[0152] Comparing the performance of the electric actuator adaptive robust control method with that of the traditional controller, the electric actuator adaptive robust control method proposed in the embodiment of the present invention can effectively compensate for the nonlinear friction in the electric actuator, thereby enhancing the robustness of the system.

[0153] The above is a detailed introduction to the adaptive robust control method of an electric actuator based on an improved LuGre friction model provided by the present invention. Specific examples are used in this article to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea; at the same time, for general technical personnel in this field, according to the idea of ​​the present invention, there will be changes in the specific implementation method and application scope. In summary, the content of this specification should not be understood as a limitation on the present invention.

[0154] In this article, relational terms such as first and second, etc. are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "comprise", "include" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the statement "comprise a ..." do not exclude the presence of other identical elements in the process, method, article or device including the elements.

Claims

1. An adaptive robust control method for electric actuator based on improved LuGre friction model, characterized in that: The steps include: S1: Establishing an electric actuator model framework, including an electric actuator execution model and an improved LuGre friction model; the electric actuator execution model receives a target input of the electric actuator system and obtains a dynamic response of the electric actuator, and the improved LuGre friction model changes the dynamic response through friction; S2: constructing a system state observer and a bristle deformation observer, which are used to respectively estimate the system state and bristle deformation of the electric actuator in real time; using the real-time estimation results based on the particle swarm optimization algorithm to identify the model parameters of the improved LuGre friction model, and obtaining the mathematical model of the electric actuator; S3: According to the mathematical model of the electric actuator, an adaptive sliding mode control strategy is adopted, combined with a nonlinear sliding mode surface switching function, and an adaptive robust controller for the electric actuator based on the improved LuGre friction model is obtained through backstepping design.

2. The adaptive robust control method for electric actuator based on improved LuGre friction model according to claim 1 is characterized in that: The electric actuator model architecture is: Among them, the output shaft model of the permanent magnet synchronous motor is: T f is the LuGre friction torque; J1 is the equivalent moment of inertia on the motor output shaft; B is the viscous damping coefficient; T e is the driving torque, i.e. the electromagnetic torque of the motor; i is the reduction ratio of the gear transmission reduction mechanism; M is the output torque of the gear reduction mechanism; M1 is the resistance torque, i.e. the output torque of the motor shaft; θ is the output angle of the gear transmission reduction mechanism, is the speed of the output shaft of the gear transmission reduction mechanism; θ1 is the mechanical angle of the motor output shaft, is the speed of the motor output shaft; T e is the driving torque, that is, the electromagnetic torque of the motor.

3. The adaptive robust control method for electric actuator based on improved LuGre friction model according to claim 1 is characterized in that: The improved LuGre friction model is: Where, T f is the LuGre friction torque; σ0 is the friction stiffness coefficient; σ1 is the friction damping coefficient; B0 is the viscous friction coefficient; θ is the output angle of the gear transmission reduction mechanism; z is the average displacement change between the bristle contact surfaces; is the parameter of the Stribeck effect; θ s is the stribeck speed; T s is the maximum static friction; T c is the Coulomb friction force.

4. The adaptive robust control method for electric actuator based on improved LuGre friction model according to claim 1 is characterized in that: The steps to build a system state observer include: A Kalman filter or a Luenberger observer is constructed using the target input of the electric actuator system and the measured output of the electric actuator system, and the state of the electric actuator system is estimated in real time through the error between the target input and the measured output, wherein the state includes the movement speed and / or displacement of the electric actuator.

5. The adaptive robust control method for electric actuator based on improved LuGre friction model according to claim 1 is characterized in that: The steps to construct the bristle deformation observer include: A method for constructing a sliding mode observer or a dynamic observer using the target input of the electric actuator system and the measured output of the electric actuator system estimates the deformation of the bristle in real time through the error between the target input and the measured output.

6. The adaptive robust control method for electric actuator based on improved LuGre friction model according to claim 1 is characterized in that: The step of identifying the model parameters of the improved LuGre friction model comprises: Initialization: randomly generate a specified number of particles, each particle represents a set of parameters of the improved LuGre friction model; Fitness evaluation: evaluating the fitness of the particle according to the error between the state estimate output by the model and the measured output of the electric actuator system; Update particle position: Update the particle position and velocity according to the particle's own historical best position and the global best position; Iteration: Repeat the fitness evaluation and position update process until the stopping condition is met, then stop the iteration and output the parameters of the improved LuGre friction model.

7. The adaptive robust control method for electric actuator based on improved LuGre friction model according to claim 1 is characterized in that: The adaptive sliding mode control strategy comprises the following steps: S31: Select a sliding surface S that is compatible with the electric actuator system: S = e + λ∫edt; Where, e is the tracking error and λ is a positive gain; S32: Design of sliding surface using nonlinear switching function: Among them, η is a design parameter, α and β are parameters related to system characteristics; S33: Design of control law based on sliding mode control principle: Where K is the control gain, F friction is the friction torque, is an estimate of the control input.

8. The adaptive robust control method for electric actuator based on improved LuGre friction model according to claim 7 is characterized in that: The steps of designing an adaptive robust controller for an electric actuator based on an improved LuGre friction model by backstepping method include: S34: Introduce virtual control input u1 to control the state of the system: u1=-K1·S-F friction ; Among them, K1 is the control gain corresponding to the virtual control input; S35: Design an adaptive parameter update law based on the system status: Among them, γ is the adaptive gain, which is used to guide the convergence of parameters.

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