High-speed and high-precision collaborative optimization control method for electromechanical system

Abstract

The invention belongs to the technical field of electromechanical control, and discloses a high-speed and high-precision collaborative optimization control method for an electromechanical system, which comprises the following steps: considering the influence of input saturation constraint, unknown external interference and actuator fault on the system, and taking the input saturation uncertain electromechanical system as a multi-port signal processing and energy conversion device from the perspective of signal and energy; establishing a dynamic model; establishing an uncertain electromechanical system by using the dynamic model, the actuator fault type and an actuator input saturation problem in an actual system; designing a high-speed and high-precision collaborative optimization control strategy; and constructing a fixed time interference observer for estimating unknown multi-source uncertainty and faults, and compensating the estimated unknown multi-source uncertainty and faults to a high-speed and high-precision collaborative optimization control strategy. According to the method, high-quality tracking performance can be ensured under the conditions of actuator faults, input constraints and multi-source uncertainty, and the method has important application prospects and value.

Description

Technical Field

[0001] This application relates to the field of electromechanical control technology, and in particular to a high-speed, high-precision collaborative optimization control method for electromechanical systems. Background Technology

[0002] High-speed and high-precision control is an inevitable requirement for the development of many fields such as aerospace, high-speed rail, subway, electric vehicles, autonomous driving, intelligent manufacturing, and robotics. In the practical application of permanent magnet synchronous motor servo systems, from the perspective of performance requirements, how to ensure high-performance control with fast response speed, high tracking accuracy, strong robustness, and reliable safety has always been and remains an essential research hotspot.

[0003] Currently, proportional-integral (PI) control and its improved algorithms are widely used in industrial applications due to their simple structure and ease of implementation. However, traditional PI control struggles to achieve a good balance between dynamic response speed, steady-state accuracy, and disturbance suppression capability. When faced with complex operating conditions such as sudden load changes or actuator failures, it often exhibits problems such as large overshoot, slow recovery, and significant steady-state error. Although some advanced control methods, such as sliding mode control and terminal sliding mode control, have improved system robustness to some extent, these methods are prone to chattering during switching, which can impact the actuator and affect system lifespan and stability. Furthermore, most existing control strategies focus on optimizing a single performance indicator, lacking a strategy that can intelligently and smoothly coordinate different control mechanisms based on the system's operating state to achieve a balance between dynamic speed and steady-state accuracy.

[0004] The key technical problem that urgently needs to be solved is how to comprehensively handle input saturation, unknown interference and actuator failure, and achieve high-speed and high-precision tracking across the entire operating range. Summary of the Invention

[0005] To address the aforementioned issues, this application provides a high-speed, high-precision collaborative optimization control method for electromechanical systems. This method designs a smooth convex combination collaborative mechanism based on tracking error and its rate of change, enabling the system to prioritize the rapid response advantage of signal processing-based control in the dynamic phase and the high precision advantage of energy transformation-based control in the steady-state phase, thereby achieving a balance between speed and accuracy. Furthermore, by designing a fixed-time disturbance observer, the method can quickly and accurately estimate and compensate for lumped uncertainties in the system, effectively enhancing the system's anti-interference capability and fault tolerance under complex operating conditions.

[0006] This application provides a high-speed, high-precision collaborative optimization control method for electromechanical systems, the method comprising the following steps: Considering the impact of input saturation constraints, unknown external disturbances, and actuator failures on the system, a dynamic model is established for the input saturated uncertain electromechanical system as a multi-port signal processing and energy conversion device from the perspective of signal and energy. The types of actuator faults and the actuator input saturation problem in the actual system are determined, and an uncertain electromechanical system is established using the dynamic model, the types of actuator faults, and the actuator input saturation problem in the actual system. Based on the aforementioned uncertain electromechanical system, a high-speed, high-precision collaborative optimization control strategy is designed; wherein, the high-speed, high-precision collaborative optimization control strategy is expressed as: (5) In the formula, and For a given constant, and , g ij To control the input gain matrix elements, v j To control the input signal, v Sj For control input signals based on signal processing, v Ej For control input signals based on energy transformation, For filtering error, The derivative of the filtering error. c Si For smooth switching functions, c Ei For smooth switching functions, XOR; A fixed-time disturbance observer is constructed to estimate unknown multi-source uncertainties and faults, and the estimated unknown multi-source uncertainties and faults are compensated into the high-speed and high-precision collaborative optimization control strategy.

[0007] Furthermore, the dynamic model is represented as: (1) In the formula, For state vectors, Given a smooth vector field, This represents the actuator fault and input saturation vector. To control the input gain matrix, To account for model uncertainties and unknown external disturbances, For the set of real numbers, Let be the state vector The derivative, n It is a positive integer. m It is a positive integer.

[0008] Furthermore, the actuator fault type is represented as follows: (2) In the formula, u F For actuator faults and input saturation vectors, I It is the identity matrix. As a failure factor, b f This is a bias fault.

[0009] Furthermore, the actuator input saturation problem in the actual system is expressed as: (3) In the formula, For practical design control input signals, and These are the upper and lower bounds of the physical limitations of the actuator, respectively. It is a saturation function. j It is a positive integer. u j The control input signal is saturated; Furthermore, the uncertain electromechanical system is represented as: (4) In the formula, This is interference or a malfunction in the system. .

[0010] Furthermore, smooth continuous switching function C Si and C Ei Represented as: (6) In the formula, For the defined filter error variable, To track the error vector, To track the trajectory signal, This is the saturation compensation auxiliary vector. , , This is the gain matrix. For the defined gain matrix, , , To adjust parameters and To adjust the parameters, and As a weighting factor, and , .

[0011] Furthermore, a fixed-time disturbance observer is constructed as follows: Under the premise of satisfying the set assumptions, a fixed-time disturbance observer is constructed in conjunction with the uncertain electromechanical system; wherein, the set assumptions include: disturbances in the system Continuously differentiable, and The derivative of is bounded.

[0012] Furthermore, the fixed-time interference observer is represented as: (7) In the formula, This is the filtering error vector. For the new extended state vector, To adjust the parameters, To adjust the parameters, To adjust the parameters, To adjust the parameters, , To track the error vector, This is the saturation compensation auxiliary vector.

[0013] Furthermore, the method is applied to a PMSM servo system, the dynamics of which are described as follows: (8) In the formula, , , and They are respectively Stator current and stator voltage on the shaft; , and These are the motor's mechanical angular velocity, rotor flux, and stator phase resistance, respectively. and For stator inductance, The coefficient of viscous friction is... For extreme logarithms, It is the moment of inertia; , , , and Uncertainty in system model parameters; For electromagnetic torque, For unknown load torque, , and It is an unknown external interference. .

[0014] The high-speed, high-precision collaborative optimization control method for electromechanical systems according to the various schemes of this application has at least the following technical effects: 1) This application designs a smooth convex combination cooperative mechanism based on tracking error and its rate of change, so that the system can give priority to the fast response advantage based on signal processing control in the dynamic stage and give priority to the high precision advantage based on energy transformation control in the steady state stage, thereby achieving a unity of speed and accuracy.

[0015] 2) This application designs a fixed-time disturbance observer to quickly and accurately estimate and compensate for the lumped uncertainty in the system, effectively enhancing the system's anti-interference capability and fault tolerance under complex operating conditions.

[0016] 3) This application avoids shocks and oscillations during control mode switching by designing a smooth and continuous switching function, thus ensuring the stability of the control process. Simultaneously, it rigorously proves the global stability of the closed-loop system and the boundedness of all signals using Lyapunov stability theory.

[0017] 4) This application explicitly describes the input saturation problem in system modeling and weakens or eliminates the negative impact of saturation nonlinearity on system performance by designing corresponding compensation mechanisms.

[0018] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0019] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein: Figure 1 A flowchart of a high-speed, high-precision collaborative optimization control method for an electromechanical system provided in this application embodiment; Figure 2 The control principle diagram of the high-speed and high-precision collaborative optimization control strategy provided in the embodiments of this application; Figure 3 A structural diagram of the PMSM servo system experimental platform provided in the embodiments of this application; Figure 4 A schematic diagram of a PMSM servo system based on LINKS-RT provided in this application embodiment; Figure 5 Various response curves of PI, STSMC, FTISMC, FTEPH and the proposed method are provided for the mutation load disturbance provided in the embodiments of this application. Figure 6 Various response curves of PI, STSMC, FTISMC, FTEPH and the improved high-speed and high-precision collaborative optimization method are used to illustrate the failure of the q-axis actuator provided in the embodiments of this application. Detailed Implementation

[0020] To enable those skilled in the art to better understand the technical solution of this application, the application will be described in detail below with reference to the accompanying drawings and specific embodiments.

[0021] This application provides a high-speed, high-precision cooperative optimization control method for electromechanical systems. First, the invention utilizes errors and their rate of change to design a cooperative optimization function within a smooth convex combination mechanism, improving the performance of the cooperative optimization control strategy. Second, it explicitly describes the input saturation constraint problem and designs an input saturation compensation mechanism to weaken or even eliminate its negative impact on the system. For model uncertainties and unknown external disturbances in the system, considering the concept of refined control, adaptive techniques and error-based variable gain disturbance observers are employed to suppress their negative impact on the system. Furthermore, an adaptive PI (API) control algorithm based on signal processing is designed to improve the speed of dynamic adjustment in system trajectory tracking control; its structure is simple and easy to implement. An adaptive (Error Port-controlled Hamiltonian, EPH) control method based on energy transformation is designed, further enhancing its advantages in steady-state processes. Moreover, this invention utilizes Lyapunov theory to systematically verify for the first time the stability of a closed-loop system employing a high-speed, high-precision cooperative optimization (CO) control strategy, and that all signals are bounded. Finally, to verify the effectiveness of the proposed high-speed, high-precision collaborative optimization control strategy, a permanent magnet synchronous motor (PMSM) servo system was employed, which is widely used in many industrial fields such as robotics, industrial manufacturing, electric vehicles, transportation, and aerospace due to its high power density and high reliability. Therefore, using this system to verify the proposed strategy has significant guiding and application value.

[0022] Specifically, such as Figure 1 As shown, the high-speed and high-precision collaborative optimization control method for this electromechanical system includes the following steps S10-S40.

[0023] S10: Considering the impact of input saturation constraints, unknown external disturbances, and actuator failures on the system, a dynamic model is established by treating the input saturated uncertain electromechanical system as a multi-port signal processing and energy conversion device from the perspective of signal and energy.

[0024] This embodiment considers the impact of input saturation constraints, unknown external disturbances, and actuator failures on the system. From the perspective of signal and energy, the input-saturated uncertain electromechanical system is treated as a multi-port signal processing and energy conversion device, and a dynamic model is established as follows: (1) In the formula, For state vectors, Given a smooth vector field, This represents the actuator fault and input saturation vector. To control the input gain matrix, To account for model uncertainties and unknown external disturbances, For the set of real numbers, Let be the state vector The derivative, n It is a positive integer. m It is a positive integer.

[0025] S20: Determine the actuator fault type and the actuator input saturation problem in the actual system, and establish an uncertain electromechanical system using the dynamic model, actuator fault type, and actuator input saturation problem in the actual system.

[0026] This embodiment considers the following actuator failure types: (2) In the formula, u F For actuator faults and input saturation vectors, I It is the identity matrix. As a failure factor, b f This is a bias fault.

[0027] The actuator input saturation problem in a real system can be described as follows: (3) In the formula, For practical design control input signals, and These are the upper and lower bounds of the physical limitations of the actuator, respectively. It is a saturation function. j It is a positive integer. u j The control input signal is for saturation.

[0028] Using equations (1), (2), and (3), the uncertain electromechanical system is redescribed as (4) In the formula, This is interference or a malfunction in the system. .

[0029] S30: Design a high-speed, high-precision collaborative optimization control strategy based on uncertain electromechanical systems; To achieve high-speed and high-precision control of the permanent magnet synchronous motor servo system, this embodiment proposes a high-speed and high-precision collaborative optimization control strategy, which realizes rapid trajectory signal adjustment in the dynamic stage, optimized energy dissipation control and accurate trajectory tracking in the steady-state stage.

[0030] The working principle of the high-speed, high-precision collaborative optimization control strategy is described as follows: (5) In the formula, and For a given constant, and , g ij To control the input gain matrix elements, v j To control the input signal, v Sj For control input signals based on signal processing, v Ej For control input signals based on energy transformation, For filtering error, The derivative of the filtering error. c Si For smooth switching functions, c Ei For smooth switching functions, XOR.

[0031] like Figure 2 The diagram shown illustrates the control principle of the high-speed, high-precision collaborative optimization control strategy provided in this embodiment. In the diagram, x 0 represents the ideal trajectory or target state that the system needs to track, such as the expected speed and position of the PMSM servo system; x The real-time operating status of the electromechanical system can be obtained through sensor feedback; external disturbances are unknown disturbances that act on the electromechanical system and affect the system's tracking accuracy, such as sudden load changes and environmental interference. When this high-speed, high-precision collaborative optimization control strategy is applied to the PMSM servo system, it includes collaborative optimization control, a smooth convex combination mechanism, and a fixed-time disturbance observer.

[0032] Cooperative optimization control coordinates the outputs of two control mechanisms—signal processing-based control and energy transformation-based control—in real time according to the system state. Specifically, signal processing-based control (such as adaptive PI control) generates the control output. v S Its design focuses on improving the speed of the system's dynamic response, ensuring rapid convergence even with large trajectory errors; control output is generated based on energy transformation control (such as adaptive error port controlled Hamiltonian control). v EIts design focuses on optimizing the system's energy dissipation and improving the tracking accuracy and energy efficiency of steady-state processes.

[0033] The smooth convex combination mechanism is one of the core mechanisms of collaborative optimization. It receives the trajectory tracking error signal from the system and dynamically adjusts the weights of the two control outputs according to the error magnitude. This smooth convex combination mechanism uses a smooth and continuous switching function to avoid the shocks and oscillations caused by traditional switching. When the error is large, the mechanism prioritizes the signal processing-based control output. v S When the error is small, the mechanism smoothly transitions to control output based on energy conversion. v E During the transition phase, the two outputs are switched via a switching function. C S and C E Mix and match for seamless switching.

[0034] A fixed-time disturbance observer estimates the lumped disturbances in the system in real time, including external disturbances, model uncertainties, and actuator faults, and feeds the estimates back to the control loop for feedforward compensation. This design effectively suppresses the negative impact of disturbances on system performance and improves the robustness and fault tolerance of the control. Actuator faults and external disturbances, as part of the system input, directly affect the dynamic response of the electromechanical system, but through compensation by the disturbance observer and the cooperative control strategy, the system can still maintain stable and high-performance operation.

[0035] Ultimately, the integrated control signal output by the collaborative optimization control is applied to the electromechanical system through the actuator, driving the system state to track the desired trajectory.

[0036] As can be seen from equation (5), the proposed high-speed and high-precision collaborative optimization mechanism is a refined control scheme. By introducing the trajectory tracking error and its rate of change, and adjusting the control input according to its magnitude of change, the significant advantages of the system at different operating stages can be fully utilized. The specific analysis is as follows: i) When the trajectory tracking error is large or the error rate of change is large, i.e. Signal processing-based control aims for rapid convergence of the control objective. ii) During the transition phase, i.e. Depending on the magnitude of the error, signal processing-based control and energy conversion-based control work in tandem. iii) When the tracking error and the rate of change of error are small, i.e. Energy transformation-based control is used to improve the tracking accuracy of the controlled target.

[0037] When designing switching functions using the direct switching method, it's easy for the system to experience surges during operation, reducing component lifespan and even compromising overall system stability, especially under rapidly changing conditions. To address this issue, we consider modifying the switching function... and The design is intended to be smooth and continuous to avoid negative impacts such as oscillations and shocks caused by non-smoothness. Furthermore, trajectory tracking error is introduced into the smooth switching function. and its rate of change It can respond more effectively to changes in system state, track the desired trajectory more accurately, and reduce lag effects. More importantly, it can achieve a refined collaborative optimization process, allowing collaborative optimization control to fully leverage its significant advantages at different stages of system operation based on changes in trajectory error.

[0038] Therefore, the smooth continuous switching function and The design is as follows: (6) In the formula, For the defined filter error variable, To track the error vector, To track the trajectory signal, This is the saturation compensation auxiliary vector. , , This is the gain matrix. For the defined gain matrix, , , To adjust parameters and To adjust the parameters, and As a weighting factor, and , .

[0039] Choosing a Gaussian function based on trajectory error and its rate of change as the smoothing convex combination mechanism can more effectively achieve the synergistic effect of signal processing and energy transformation control. From the smoothing convex combination mechanism (5) and the selected smoothing switching function (6), it can be seen that when the trajectory error is large or the error rate of change is large, , , Signal processing-based control plays a dominant role, enabling the control objective to converge rapidly. When the error and its rate of change are small, , , Energy transformation-based control plays a dominant role, optimizing and regulating the system's input energy, output energy, and energy dissipation to improve control accuracy and reduce energy consumption. This synergistic mechanism, by combining signal processing and energy optimization strategies, can balance the system's accuracy and efficiency under different operating conditions, ensuring that the system exhibits good robustness and adaptability under various circumstances.

[0040] S40: Construct a fixed-time disturbance observer to estimate unknown multi-source uncertainties and faults, and compensate the estimated unknown multi-source uncertainties and faults into the high-speed and high-precision collaborative optimization control strategy.

[0041] To improve the uncertainty suppression and fault tolerance performance of the system, this embodiment designs a novel fixed-time-extended state observer (FTESO) for estimating unknown multi-source uncertainties and faults. And compensate it into the closed-loop system.

[0042] Assumption 1: Interference in the system It is continuously differentiable, and its derivative is bounded, that is... , It is an unknown positive number.

[0043] Combining equation (4) and assumption 1 above, a fixed-time disturbance observer is constructed as follows: (7) In the formula, This is the filtering error vector. For the new extended state vector, To adjust the parameters, To adjust the parameters, To adjust the parameters, To adjust the parameters, , To track the error vector, This is the saturation compensation auxiliary vector.

[0044] This embodiment uses a PMSM servo system to experimentally verify the proposed method. The dynamics of the PMSM servo system are described below: (8) In the formula, , , and They are respectively Stator current and stator voltage on the shaft; , and These are the motor's mechanical angular velocity, rotor flux, and stator phase resistance, respectively. and For stator inductance, The coefficient of viscous friction is... For extreme logarithms, It is the moment of inertia; , , , and Uncertainty in system model parameters; For electromagnetic torque, For unknown load torque, , and It is an unknown external interference. .

[0045] The experimental platform for the PMSM servo system built in this embodiment is as follows: Figure 3 As shown. The experimental platform includes a braking resistor 100, a servo driver 200, an adapter box 300, a simulator 400, a host computer 500, a load motor 600, a torque sensor 700, and a drive motor 800. The high-speed, high-precision collaborative optimization control method for electromechanical systems proposed in this embodiment is built and run in the simulator 400. A control model is established in the Matlab / Simulink environment, and executable code is compiled using the LINKS-RT real-time simulation software package and deployed on the simulator 400 to ensure that the control algorithm can run in real time. Combined with... Figure 4 As shown, the simulator 400 acts as a real-time controller, calculating the trajectory tracking error based on the desired trajectory and the actual system state fed back by the torque sensor 700 and the drive motor 800. Based on this error, a smoothing convex combination mechanism dynamically coordinates the outputs of the two control modes and performs feedforward compensation by combining the lumped disturbance estimated by the fixed-time disturbance observer, ultimately generating a comprehensive control signal. This control signal is transmitted to the servo driver 200 via the adapter box 300. The servo driver 200 receives instructions from the controller and controls the drive motor 800 to operate. The load motor 600 simulates various load disturbances under actual working conditions; its torque changes are accurately detected by the torque sensor 700 and fed back to the control system, forming a closed loop. The braking resistor 100 dissipates the regenerative energy generated by the system during braking or sudden load changes, protecting the system hardware. The host computer 500 communicates with the simulator 400 to monitor the experimental process, set control parameters, and record and display system operating data in real time.

[0046] This embodiment verifies the superiority of the proposed strategy in terms of dynamic response speed, tracking accuracy, interference suppression, and fault tolerance by comparing and analyzing experimental data of the proposed collaborative optimization method with other methods such as PI control under different operating conditions.

[0047] Specifically, the drive motor 800 is selected as a 130MB150A surface-mount PMSM, and the torque sensor 700 is selected as a YH502 with a range of [missing information]. Nm, power supply is DC 24 V, output is The control algorithm is implemented using Matlab modeling software, and its design is based on Simulink software. Code is generated through compilation, and the control algorithm is verified using the LINKS-RT real-time simulation software package. The structure of the PMSM servo system based on LINKS-RT is as follows: Figure 4 As shown. The experimental platform parameters are: , , , , , , The parameters of the drive motor and the load motor are the same.

[0048] To verify the effectiveness of the proposed method, PI control is used as a comparison method, and the speed loop controller and current loop controller are given below: (9) In the formula, , It is a proportional gain matrix. This is the integral gain matrix.

[0049] This embodiment uses an unknown load disturbance variation as an example to verify the robustness and disturbance suppression performance of the proposed collaborative optimization control strategy. The forms of load disturbance considered in this embodiment are shown in Table 1. To effectively and qualitatively evaluate the performance of the proposed controller, several different performance indicators are used for verification, such as overshoot, settling time, steady-state error, q-axis peak current / voltage, disturbance recovery time, speed reduction / increase due to disturbance, and speed flutter amplitude under load disturbance.

[0050] Table 1 Load Interference Modes

[0051] (1) Sudden load interference suppression performance This embodiment tests the robustness and anti-interference capability of the proposed high-speed, high-precision collaborative optimization control strategy using an example of unknown load disturbance variation. The form of the load disturbance is assumed to be as shown in Table 1. Under load conditions, the speed, dq-axis current, and voltage response curves and their local magnified views of the PMSM servo system are tested using PI, STSMC, FTISMC, FTEPH, and CO methods. These tests are performed with the desired speed adjustment at 1000 rpm. The results are as follows: Figure 5 As shown in the figure, the speed regulation curves and their magnified partial views reveal that the FTISMC strategy exhibits the fastest dynamic response speed, followed by CO and FTEPH, while CO shows the smallest steady-state error and no overshoot. The speed regulation curves using the PI and STSMC methods show significant oscillations and large steady-state errors. When the load changes, the speed response curves using the PI and STSMC methods show significant drops and rises, while the speed response curves using the FTISMC, FTEPH, and CO strategies show smaller changes and shorter recovery times, reflecting better interference suppression performance. Furthermore, the current and voltage response curves using the FTISMC, FTEPH, and CO strategies exhibit smaller fluctuations, indicating good stability. The performance indicators of different methods are compared in Table 2.

[0052] Table 2 Performance metrics of different methods under sudden load

[0053] (2) Fault tolerance performance of actuator Consider actuator failure modes: failure ( ) + Deviation fault ( When the desired speed is adjusted to 1000 RPM, under fault conditions, the speed, dq axis current, and voltage response curves of the permanent magnet synchronous motor servo system using PI, STSMC, FTISMC, FTEPH, and CO methods, along with their enlarged partial diagrams, are shown below. Figure 6 As shown in the figure, the speed regulation curves and their magnified partial views reveal that when the actuator malfunctions, the speed curves using the PI and STSMC methods exhibit significant drops and rises, while the speed response curves using the FTISMC, FTEPH, and CO strategies show smaller changes and shorter recovery times, reflecting better fault tolerance. Furthermore, the current and voltage response curves using the FTISMC, FTEPH, and CO strategies exhibit smaller fluctuations and better stability. Taking a q-axis actuator failure as an example, the comparison results of different methods are shown in Table 3.

[0054] Table 3 Performance indicators of different methods in case of actuator failure

[0055] In summary, the high-speed and high-precision collaborative optimization control strategy proposed in this embodiment can ensure high-quality tracking performance (no overshoot, high tracking accuracy, and fast convergence) under the conditions of actuator failure, input constraints, and multi-source uncertainties, and has important application prospects and value.

[0056] The above embodiments are only used to illustrate this application and are not intended to limit this application. Those skilled in the art can make various changes and modifications without departing from the spirit and scope of this application. Therefore, all equivalent technical solutions also fall within the scope of this application, and the patent protection scope of this application should be defined by the claims.

Claims

1. A method for high-speed and high-precision collaborative optimization control of an electromechanical system, characterized in that, The method comprises the following steps: Considering the influence of input saturation constraints, unknown external disturbances and actuator faults on the system, an uncertain electromechanical system with input saturation is regarded as a multi-port signal processing and energy conversion device from the perspective of signals and energy, and a dynamic model is established; An actuator fault type and an actuator input saturation problem in an actual system are determined, and an uncertain electromechanical system is established by using the dynamic model, the actuator fault type and the actuator input saturation problem in the actual system; A high-speed and high-precision collaborative optimization control strategy is designed based on the uncertain electromechanical system, and the high-speed and high-precision collaborative optimization control strategy is expressed as: （5） wherein and is a constant set, and , g ij is an element of the control input gain matrix , v j is a control input signal, v Sj is a control input signal based on signal processing, v Ej is a control input signal based on energy transformation, is a filtered error, is a derivative of the filtered error, c Si is a smooth switching function, c Ei is a smooth switching function, is exclusive or; A fixed-time disturbance observer is constructed to estimate unknown multi-source uncertainties and faults, and the estimated unknown multi-source uncertainties and faults are compensated to the high-speed and high-precision collaborative optimization control strategy.

2. The method of claim 1, wherein, The dynamic model is expressed as: （1） where is the state vector, is a known smooth vector field, denotes the actuator fault and input saturation vector, is the control input gain matrix, is the model uncertainty and unknown external disturbance, is the set of real numbers, is the derivative of the state vector with respect to time, n is a positive integer, m is a positive integer.

3. The method of claim 2, wherein, The actuator fault type is expressed as: （2） wherein u F is the actuator fault and input saturation vector, I is the identity matrix, is the fault failure factor, b f is the bias fault.

4. The method of claim 3, wherein, The actuator input saturation problem in the actual system is expressed as: （3） wherein is the actual design control input signal, and are the upper and lower bounds of the actuator physical limits, respectively, is a saturation function, j is a positive integer, u j is the saturated control input signal.

5. The method of claim 4, wherein, The uncertain electromechanical system is expressed as: （4） In the formula, is a disturbance or fault in the system, .

6. The method of claim 1, wherein, Smooth continuous switching function C Si and C Ei is represented as: （6） wherein is a defined filter error variable, is a tracking error vector, is a desired tracking trajectory signal, is a saturation compensation auxiliary vector, , , is a gain matrix. is a defined gain matrix, , , is a tuning parameter and is a tuning parameter, and are weight factors, and , .

7. The method of claim 5, wherein, The fixed-time disturbance observer is constructed in the following manner: Under the premise of meeting a set assumption, a fixed-time disturbance observer is constructed in combination with the uncertain electromechanical system; wherein the set assumption includes that the disturbance in the system is continuously differentiable, and the derivative of the disturbance is bounded.

8. The method of claim 7, wherein, The fixed-time disturbance observer is expressed as: （7） wherein is a filtered error vector, is a new extended state vector, is a tuning parameter, is a tuning parameter, is a tuning parameter, is a tuning parameter, , is a tracking error vector, is a saturation compensation auxiliary vector.

9. The method according to any one of claims 1 to 8, characterized in that, The method is applied to a PMSM servo system, and the dynamics of the PMSM servo system is described as follows: （8） where, , , and are the stator currents and stator voltages on the d and q axes, respectively; , and are the mechanical angular speed of the motor, the rotor flux and the per-phase resistance of the stator, respectively; are the stator inductances, is the viscous friction coefficient, is the number of pole pairs, is the moment of inertia; , , and are the system model parameter uncertainties; is the electromagnetic torque, is the unknown load torque, and are unknown external disturbances.​​​​

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