A Quadratic Programming Optimization Method for Model-Free Speed ​​Control of Ultrasonic Motors

Abstract

This invention discloses a quadratic programming optimization method for model-free speed control of an ultrasonic motor, comprising: initializing servo system parameters, predicting the real-time operating state of the system and correcting the predicted real-time operating state, constructing a quadratic programming optimization problem for the input estimates, using the effective set method to find the optimal solution of the quadratic programming problem, selecting control indices to monitor the system operating state in real time according to control requirements, and adjusting the values ​​of the control parameters within the allowable range defined by each parameter based on the influence law of each control parameter on the motor speed control effect. This invention does not rely on a physical model of the system, introduces a quadratic programming optimization method, constructs a quadratic programming problem for the estimation of the constrained input frequency, and uses the effective set method to find the optimal solution for the operating frequency, ultimately driving the ultrasonic motor to work; it has advantages such as a small number of custom parameters, ease of application, high real-time state tracking accuracy, good robustness, and strong adaptability.

Description

Technical Field

[0001] This invention relates to automation control and motor control technology, and in particular to a quadratic programming optimization method for model-free speed control of ultrasonic motors. Background Technology

[0002] Ultrasonic motors possess complex time-varying and nonlinear characteristics. After constructing a physical model based on their working principle, control algorithms based on this physical model often struggle to achieve a balance between reducing unmodeled dynamics and improving response speed. Therefore, with the maturation of intelligent algorithms, servo control systems for ultrasonic motors mostly construct mathematical models based on input and output data and design intelligent algorithms tailored to the characteristics of different mathematical models.

[0003] Currently, neural network-based intelligent algorithms are the most widely used. For example, sliding mode control is introduced into cerebellar neural networks to simplify the neural network structure of multi-input multi-output ultrasonic motor servo systems, designing self-organizing structures and assisting robust controllers in eliminating approximation errors; adaptive adjustment algorithms for network layer count are designed, and the characteristics of online adjustment of control parameters are based on gradient descent; using the second-order transfer function as a reference model, the neural network is adaptively adjusted to continuously approximate the real state, achieving millimeter-level sinusoidal trajectory tracking. In addition, the combination of wavelet fuzzy algorithms and neural network algorithms can also reduce tracking errors; Petri fuzzy control can significantly reduce computational load and simplify control algorithms.

[0004] This demonstrates that introducing other intelligent algorithms to simplify the neural network structure and computation process is currently the main approach to improving control efficiency. While this method is universally applicable to time-varying nonlinear systems, it does not fully utilize the characteristics of ultrasonic motor servo systems and there is still room for improvement. Summary of the Invention

[0005] Purpose of the invention: The purpose of this invention is to provide a quadratic programming optimization method for model-free speed control of ultrasonic motors, which reduces the number of custom parameters and improves the applicability and convenience of the model-free adaptive control method for ultrasonic motors.

[0006] Technical solution: The present invention provides a quadratic programming optimization method for model-free speed control of an ultrasonic motor, comprising the following steps:

[0007] S1. Initialize the servo system, including the state estimation step size factor η, the state estimation weight factor μ, and the local characteristic parameters φ(k) of the servo system;

[0008] S2. Predict the real-time operating status of the servo system and correct the predicted real-time operating status of the servo system.

[0009] S3. Based on the expression of the quadratic programming problem, construct a quadratic programming optimization problem for the input estimate;

[0010] S4. Quadratic Programming Optimization Initialization: Define x = u(k) as the direction of particle movement, d, and step size α. It is stipulated that the particle moves according to a linear rule in each iteration. x i Let x be the position of the particle in the i-th iteration. i+1 Let α be the position of the particle in the (i+1)th iteration. i Let d be the step size of the particle in the i-th iteration. i Let x be the direction of movement of the particle in the i-th iteration; select the upper or lower limit of the input estimate as the initial working set W0, and select any point within the feasible region restricted by the working set as the starting point x0 for optimization, and design the initial movement step size as α0 = 1;

[0011] S5. Discriminant parameter calculation: Calculate the current working set W. i Minimum point d of the particle's direction of movement under constraint i and the corresponding Lagrange multiplier λ i ;

[0012] S6. Determine the property of particle movement direction: If d i =0, the particle does not need to move, and step S7 is executed sequentially; otherwise, step S8 is executed directly.

[0013] S7. Determine the property of Lagrange multipliers: If λ i If x > 0, then the independent variable x i The value is taken as the global optimal solution u(k) = x within the allowable error range. * The current optimization round is complete; proceed directly to step S11. If λ i ≤0, remove the current working set W i In which λ i For invalid inequality constraints t ≤ 0, update the working set W. i+1 =W i \{t}, and directly execute step S10;

[0014] S8. Determine the validity of particle movement: If the particle remains in the current working set W after moving according to the rules... i Inside, i.e., x i +d i ∈W i Then update the particle position x. i+1 =x i +d i If the condition is met, proceed directly to step S10; otherwise, proceed sequentially to step S9.

[0015] S9. Modify particle movement rules: shorten particle movement step size α i Calculate the minimum movement step size within the feasible region. Update particle position The maximum allowable step size within the feasible region. Incorporate the inequality constraints violated by the particles before the correction into the working set for the next iteration, and update W. i+1 =W i ∪{j *}, where j * It makes Inequality constraints that yield the minimum value It is a work set W i The matrix equation formed by the constraints, b is the transpose of the step size of the particle in the i-th iteration. i These are constant parameters of the matrix equation;

[0016] S10. Iteration Termination Criterion: Based on the control effect requirements, the number of optimization iterations is limited to m; within the allowed number of iterations, steps S5 to S7 are repeated until the specified number of iterations is exceeded, and the calculation result of the last iteration is defined as the approximate optimal solution u(k) = x in this round of optimization. m x m It is the value of particle x up to the maximum number of iterations.

[0017] S11. Control Status Monitoring: Based on the control requirements, select all or some of the control indicators among the settling time, average steady-state error, overshoot, rise time, and unit control time to monitor the servo system's operating status in real time. If the control effect is not up to standard, execute step S12; otherwise, proceed to the next (k+1) servo system moment and return to step S2 until a control end command is received.

[0018] S12, Control Parameter Correction: Based on the influence of each control parameter on the motor speed control effect, adjust the value of the control parameter within the allowable range defined by each control parameter; then the system enters the next (k+1) servo system moment, returns to step S2, until the control end command is received.

[0019] Furthermore, in step S1, the initialized state estimation step size factor η and state estimation weight factor μ are random numbers not greater than 1, and the initialized local feature parameter φ(1) of the servo system is a non-positive random number.

[0020] Furthermore, in step S2, an iterative formula is used to calculate the local feature parameters of the system and predict the real-time operating state of the system; the iterative formula is:

[0021]

[0022] Where φ(k-1) is the local characteristic parameter of the system at time k-1, Δu(k-1)=u(k-1)-u(k-2) is the change in the system input at time k-1, u(k-1) is the system input at time k-1, u(k-2) is the system input at time k-2; y(k) is the system output at time k, y(k-1) is the system output at time k-1.

[0023] Furthermore, the formula for correcting the predicted real-time operating state of the system in step S2 is as follows:

[0024]

[0025] in, Let φ(k) be the local feature parameters of the corrected system, and sign(φ(k)) be the sign function of φ(k), which ensures that the local feature parameters are always non-positive.

[0026] Furthermore, the quadratic programming problem expression in step S3 is:

[0027]

[0028] st ax-b≥0

[0029] Where x = u(k) is the input value to be estimated. and Characterizes the system's operating state. y e (k+1) is the expected output of the system at time (k+1), and y(k) is the output of the system at time k. Let u(k-1) be the local characteristic parameters of the system after correction at time k, and u(k-1) be the system input at time k-1; a = [1, -1] T b = [f l -f h ] T f l and f h These are the lower and upper limits of the ultrasonic motor control input frequency, respectively.

[0030] Furthermore, in step S5, the current working set W i Minimum point d under constraints i and the corresponding Lagrange multiplier λ i The calculation formula is:

[0031]

[0032] Among them, g i =Hx i +c, B=(AH -1 A T )-1 AH -1 G = H -1 -H -1 A T B, A is the working set W i The coefficient matrix satisfies Ax = 0. and Characterizing the system's operating state, A T H is the transpose of A. -1 It is the inverse matrix of H. y e (k+1) is the expected output of the system at time (k+1), and y(k) is the output of the system at time k. Let u(k-1) be the local characteristic parameters of the system after correction at time k, and u(k-1) be the system input at time k-1.

[0033] Furthermore, in step S9, the particle movement step size α is shortened according to the following formula. i Calculate the minimum movement step size within the feasible region.

[0034]

[0035] in, This represents the shortened particle movement step size.

[0036] Furthermore, the influence of each control parameter on the motor speed control effect in step S12 is as follows:

[0037]

[0038] The present invention provides a quadratic programming optimization system for model-free speed control of an ultrasonic motor, comprising:

[0039] The initialization module is used to initialize the control parameters of the servo system, including the state estimation step size factor η, the state estimation weight factor μ, and the system local characteristic parameter φ(k);

[0040] The prediction and correction module is used to predict the real-time operating status of the system and correct the predicted real-time operating status of the system.

[0041] The quadratic programming optimization problem is constructed to generate a quadratic programming problem for the input estimate.

[0042] The quadratic programming problem-solving module is used to find the optimal solution to a quadratic programming problem using the effective set method.

[0043] The monitoring module is used to monitor the system's operating status in real time by selecting all or some of the control indicators, such as settling time, average steady-state error, overshoot, rise time, and unit control time, according to control requirements.

[0044] The control parameter correction module is used to adjust the values ​​of the control parameters within the allowable range defined by each control parameter, based on the influence of each control parameter on the motor speed control effect.

[0045] An apparatus of the present invention includes a memory and a processor, wherein:

[0046] Memory is used to store computer programs that can run on a processor;

[0047] The processor is configured to, when running the computer program, execute the steps of the quadratic programming optimization method for model-free speed control of an ultrasonic motor as described above.

[0048] Beneficial effects: Compared with the prior art, the technical effects of the present invention are as follows: (1) The present invention integrates system constraints and input estimation loss function, and introduces a quadratic programming expression, which is crucial for simplifying the control idea and optimizing the control process; (2) The present invention selects the effective set method as the quadratic programming problem optimization with respect to the input frequency, which is simple in steps and helps to improve the optimization efficiency and accuracy; (3) Compared with the existing classical model-free control improvement algorithm, the present invention has the advantages of fewer custom parameters, easy application, high real-time state tracking accuracy, good robustness and strong adaptability. Attached Figure Description

[0049] Figure 1 This is a schematic diagram of the system structure of the present invention;

[0050] Figure 2 This is a flowchart of the method of the present invention;

[0051] Figure 3 This is a comparison of experimental results when the ultrasonic motor tracks the step signal load in the embodiment of the present invention. (a) shows the speed change under PID control, (b) shows the speed change under the control strategy of this scheme, (c) shows the periodic change of the load torque, (d) shows the frequency change during PID control, (e) shows the frequency change during the control process of this scheme, and (f) shows the change of the state estimation quantization parameter during the control process of this scheme. Detailed Implementation

[0052] Embodiments of the present invention are described in detail below, examples of which are illustrated in the accompanying drawings. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and should not be construed as limiting the present invention.

[0053] Since the frequency estimation input constraint of the ultrasonic motor is a system of linear equations, a quadratic programming problem with respect to the frequency input can be constructed. This allows for a further simplification of the control strategy and an improvement in control efficiency through the optimization method of quadratic programming.

[0054] Quadratic programming problems, as a common type of optimization problem, have several proven feasible solution methods. The loss function of the ultrasonic motor control input estimation problem is a quadratic function of the independent variable, and the constraints are a system of linear equations about the independent variable. Therefore, it can be transformed into a quadratic programming problem, and the effective set method is selected as a feasible approach to find the optimal solution for estimating the input value. Simulation results based on the method of this invention show that the improved control algorithm demonstrates advantages in overshoot, settling time, average steady-state error, and rise time.

[0055] The present invention provides a quadratic programming optimization method for model-free speed control of an ultrasonic motor. The control structure in a specific implementation within an ultrasonic motor servo system is as follows: Figure 1 As shown, the servo system includes: an ultrasonic motor, a local dynamic feature adaptive identification module, and a controlled input quadratic programming optimization estimation module. The controlled input quadratic programming optimization estimation module accepts the target output as a reference value for finding the estimated input, and uses a quadratic programming optimization estimation method to estimate the input value. The ultrasonic motor selects the estimated input value as the excitation electrical signal frequency to complete the servo system output. The local dynamic feature adaptive identification module uses a local dynamic feature adaptive identification method to analyze the numerical relationship between the estimated input and the corresponding servo system output, obtaining the local feature parameters of the servo system. These parameters, together with the historical output of the servo system, guide the quadratic programming optimization estimation method to adjust the optimization parameters.

[0056] like Figure 2 As shown, the specific implementation process is as follows:

[0057] Step 1, servo system initialization. Set the two control parameters of the servo system: the state estimation step size factor η and the state estimation weight factor μ to random numbers not greater than 1; set the local characteristic parameter φ(1) of the servo system to a non-positive random number.

[0058] Step 2: Predict the real-time operating state of the system. For a servo system that can be represented in discrete form, at time k (k = 2, 3, 4, ...), the servo system calculates the local characteristic parameter φ(k) according to the following iterative formula to characterize the real-time operating state of the system.

[0059]

[0060] Where φ(k-1) is the local characteristic parameter of the servo system at time k-1, Δu(k-1)=u(k-1)-u(k-2) is the change in the input of the servo system at time k-1, u(k-1) is the system input at time k-1, u(k-2) is the system input at time k-2, y(k) is the output of the servo system at time k, and y(k-1) is the output of the servo system at time k-1.

[0061] Step 3: The system performs real-time prediction and state correction. For the local feature parameter φ(k) calculated at time k, correction is performed according to the following rules:

[0062]

[0063] in, Here, φ(k) represents the local characteristic parameters of the corrected system. Sign(φ(k)) is a sign function for φ(k). This function ensures that the local characteristic parameters are always non-positive, which conforms to the law that the output and input of the ultrasonic motor are negatively correlated, thus avoiding divergence of the servo system.

[0064] Step 4: Construct a quadratic programming optimization problem for the input estimate. Based on the following quadratic programming problem expression, we obtain a method to find the optimal solution for the input estimate using quadratic programming.

[0065]

[0066] st ax-b≥0

[0067] Where x = u(k) is the input value to be estimated. and Characterizes the system's operating state. y e (k+1) is the expected output of the system at time (k+1), and y(k) is the output of the system at time k. Let u(k-1) be the local characteristic parameters of the system after correction at time k, and u(k-1) be the system input at time k-1; a = [1, -1] T b = [f l ,-f h ] T f l and f h These are the lower and upper limits of the ultrasonic motor control input frequency, respectively, and the specific values ​​are provided by the manufacturer.

[0068] Step 5, Quadratic Programming Optimization Initialization. This invention uses the effective set method to find the optimal solution to the quadratic programming problem in Step 4. Define the numerical particle x = u(k) with the moving direction d and the step size α. It is stipulated that the particle moves according to a linear rule in each iteration, i.e. x i Let x be the position of the particle in the i-th iteration. i+1 Let α be the position of the particle in the (i+1)th iteration. i Let d be the step size of the particle in the i-th iteration. i Let x be the direction of movement of the particle in the i-th iteration; randomly select some inequality constraints as the working set W0, and select any point within the feasible region restricted by the working set as the starting point x0 for optimization, and design the initial movement step size as α0 = 1.

[0069] Step 6, Calculate the discrimination parameter. Calculate the current working set W. i Minimum point d under constraints i and the corresponding Lagrange multiplier λ i .

[0070]

[0071] Among them, g i =Hx i +c, B=(AH -1 A T ) -1 AH -1 G = H -1 -H -1 A T B, A is the working set W i The coefficient matrix satisfies Ax = 0, A T H is the transpose of A. -1 It is the inverse matrix of H.

[0072] Step 7: Determine the nature of the particle's direction of movement. If d i =0, the particle does not need to move, proceed to step 8 sequentially; otherwise, proceed directly to step 9.

[0073] Step 8: Determine the properties of the Lagrange multipliers. If λ i If x > 0, then the independent variable x i The value is taken as the global optimal solution u(k) = x within the allowable error range. * This round of optimization is complete; proceed directly to step 12. If λ i ≤0, remove the current working set W i In which λ i For invalid inequality constraints t ≤ 0, update the working set W. i+1 =W i \{t}, and directly execute step 11 and subsequent steps.

[0074] Step 9: Determine the validity of the particle movement. If the particle remains in the current working set W after moving according to the rules... i Inside, i.e., x i +d i ∈W i Then update the particle position x. i+1 =x1+d1, and directly execute step 11 and subsequent steps; otherwise, execute step 10 and subsequent steps sequentially.

[0075] Step 10, correct the particle movement rules. Shorten the particle movement step size α according to the following formula. i Calculate the minimum movement step size within the feasible region. Update particles Position. Incorporate the constraints violated by the particles before the correction into the working set for the next iteration, and update W. i+1 =W k ∪{j *}, where j * It makes Constraints for obtaining the minimum value It is the shortened x i The step size of movement, It is a work set W i The matrix equation formed by the constraints, b is the transpose of the step size of the particle in the i-th iteration. i is a constant parameter of the matrix equation.

[0076]

[0077] Step 11, Iteration Termination Judgment. Based on the control effect requirements, the number of optimization iterations is limited to m. Within the allowed number of iterations, steps 6 to 8 are repeated until the specified number of iterations is exceeded, and the calculation result of the last iteration is defined as the approximate optimal solution u(k) = x for this round of optimization. m .

[0078] Step 12, Control Status Monitoring. Based on the control requirements, select all or some of the control indicators among settling time, average steady-state error, overshoot, rise time, and unit control time to monitor the servo system's operating status in real time. If the control effect is not satisfactory, proceed to step 13; otherwise, enter the next (k+1) servo system moment and repeat steps 2 to 7 until a control termination command is received.

[0079] Step 13, Control Parameter Correction. Based on the influence of each control parameter on the motor speed control effect summarized in the table below, adjust the values ​​of the control parameters within the allowable range defined for each parameter. The servo system then enters the next (k+1) system moment and repeats steps 2 to 7 until a control termination command is received.

[0080]

[0081] The present invention provides a quadratic programming optimization system for model-free speed control of an ultrasonic motor, comprising:

[0082] The initialization module is used to initialize the control parameters of the servo system, including the state estimation step size factor η, the state estimation weight factor μ, and the system local characteristic parameter φ(k);

[0083] The prediction and correction module is used to predict the real-time operating status of the system and correct the predicted real-time operating status of the system.

[0084] The quadratic programming optimization problem is constructed to generate a quadratic programming problem for the input estimate.

[0085] The quadratic programming problem-solving module is used to find the optimal solution to a quadratic programming problem using the effective set method.

[0086] The monitoring module is used to monitor the system's operating status in real time by selecting all or some of the control indicators, such as settling time, average steady-state error, overshoot, rise time, and unit control time, according to control requirements.

[0087] The control parameter correction module is used to adjust the values ​​of the control parameters within the allowable range defined by each control parameter, based on the influence of each control parameter on the motor speed control effect.

[0088] An apparatus of the present invention includes a memory and a processor, wherein:

[0089] Memory is used to store computer programs that can run on a processor;

[0090] The processor is configured to execute the steps of the quadratic programming optimization method for model-free speed control of an ultrasonic motor as described above when running the computer program, and to achieve the same technical effect as the above method.

[0091] The following examples illustrate in detail the experimental results of the quadratic programming optimization method for model-free speed control of ultrasonic motors according to the present invention during specific implementation.

[0092] Figure 3Figures (a) to (f) show the ability of this scheme and the PID control to stabilize the speed under constant speed tracking of 30 r / min when dealing with periodic load changes. The PID parameters are adjusted to P = 10.43, I = 8.25, and D = 0. Referring to the load change cycle in Figure (c), a comparison between Figures (a) and (b) shows that when the load suddenly increases and the speed decreases, the control error of the PID control system increases accordingly. Within the half-cycle of a 0.8 Nm load, the error remains relatively large. When the load decreases to 0.2 Nm, the error decreases accordingly. Entering the next load change cycle, the error is slightly lower in the 0.8 Nm range compared to the previous cycle, but it is still larger than that of this scheme. In the control system of this scheme, an increase in load also leads to an increase in error, but the difference is not significant, with maximum errors of 4.97% and 5.43%, respectively. During the same period, the error of the PID control system increases from a maximum tracking error of 5.18% at 0.2 Nm to 13.33%. As can be seen from the corresponding input frequency diagrams (d) and (e), the PID control system continuously attempts to find the optimal control frequency after the speed decreases. However, due to the inability to adjust the PID parameters, the old parameters cannot adapt to the new environment. The robustness advantage of AS-MFAC allows it to track new system state changes in real time and adjust the control input accordingly. Figure (f) shows that the sliding partial derivative φ jumps at the moment of load change, which also corresponds to the frequency change. Therefore, the results indicate that this scheme is more robust than PID in dealing with speed changes caused by load variations.

[0093] In summary, the quadratic programming optimization method for model-free speed control of an ultrasonic motor of this invention, based on the characteristics of an ultrasonic motor servo system, introduces a quadratic programming optimization method to simplify the control process and improve operational convenience. After online analysis of the system's input and output data, without relying on the system's physical model, the local characteristics of the motor speed with respect to frequency are extracted, and a quadratic programming problem for estimating the constrained input frequency is constructed. The optimal solution for the operating frequency is found using the efficient set method, ultimately driving the ultrasonic motor to operate. The quadratic programming optimization method for model-free speed control of an ultrasonic motor of this invention has advantages such as a small number of custom parameters, ease of application, high real-time state tracking accuracy, good robustness, and strong adaptability.

Claims

1. A quadratic programming optimization method for model-free speed control of an ultrasonic motor, characterized in that, Includes the following steps: S1. Initialize the servo system, including the state estimation step size factor. State estimation weighting factor and servo system local characteristic parameters ; S2. Predict the real-time operating status of the servo system and correct the predicted real-time operating status of the servo system. S3. Based on the quadratic programming problem expression, construct a quadratic programming optimization problem for the input estimate; the quadratic programming problem expression is: ； in, The input value to be estimated. and Characterizes the system's operating state. , yes The system expects to output at any given time. yes The system outputs the timeline. for Local characteristic parameters of the system after time-correction. for Time system input; , , and These are the lower and upper limits of the ultrasonic motor control input frequency, respectively. S4. Quadratic Programming Optimization Initialization: Definition The direction of numerical particle movement is The movement step size is It is stipulated that the particle moves according to a linear rule in each iteration, that is... , For the first The position of the next iteration particle. For the first The position of the next iteration particle. For the first The step size of the particle in the next iteration For the first The direction of particle movement in the next iteration; selecting the upper or lower bound of the input estimate as the initial working set. And within the feasible region constrained by the working set, any point is selected as the starting point for the optimization. The initial movement step size is designed to be ; S5. Discriminant parameter calculation: Calculate the current working set. Minimum of the direction of particle movement under constraint and the corresponding Lagrange multipliers ; S6. Determine the property of particle movement direction: If If the particles do not need to move, proceed sequentially to step S7; otherwise, proceed directly to step S8. S7. Determine the properties of Lagrange multipliers: If Then the independent variable at this time The value is the global optimal solution within the allowable error range. This round of optimization is complete; proceed directly to step S11. If... Remove the current working set China makes Invalid inequality constraints Update working set And directly execute step S10; S8. Determine the validity of particle movement: If the particle remains in the current working set after moving according to the rules... within, that is Then update the particle position. If the condition is met, proceed directly to step S10; otherwise, proceed sequentially to step S9. S9. Modify particle movement rules: shorten particle movement step size. Calculate the minimum movement step size within the feasible region. Update particle position , The maximum allowable step size within the feasible region. The inequality constraints violated by the particles before the correction will be included in the working set of the next iteration, and the settings will be updated. ,in It makes Inequality constraints that yield the minimum value It is a work set The matrix equation formed by the constraints, For the first The transpose of the particle's movement step size in the next iteration. These are constant parameters of the matrix equation; S10. Iteration Termination Criterion: Based on the control effect requirements, the number of optimization iterations is limited to [number missing]. Within the allowed number of iterations, repeat steps S5 to S7 until the specified number of iterations is exceeded, and define the calculation result of the last iteration as the approximate optimal solution for this round of optimization. , The particles up to the maximum number of iterations. Values; S11. Control Status Monitoring: Based on the control requirements, select all or some of the control indicators among settling time, average steady-state error, overshoot, rise time, and unit control time to monitor the servo system's operating status in real time; if the control effect is not up to standard, proceed to step S12; otherwise, proceed to the next step. The servo system returns to step S2 until a control termination command is received. S12, Control Parameter Correction: Based on the influence of each control parameter on the motor speed control effect, adjust the values ​​of the control parameters within the allowable range defined for each parameter; then the system proceeds to the next step. When the servo system is in operation, it returns to step S2 until a control termination command is received.

2. The quadratic programming optimization method for model-free speed control of an ultrasonic motor according to claim 1, characterized in that, The state estimation step size factor after initialization in step S1 State estimation weighting factor The initial local characteristic parameters of the servo system are random numbers not greater than 1. These are non-positive random numbers.

3. The quadratic programming optimization method for model-free speed control of an ultrasonic motor according to claim 1, characterized in that, In step S2, an iterative formula is used to calculate the local feature parameters of the system and predict the real-time operating state of the system; the iterative formula is: ； in, for Local characteristic parameters of the time system yes The change in system input at any given time, for Time system input, for Time system input; yes The system outputs the timeline. yes Time system output.

4. The quadratic programming optimization method for model-free speed control of an ultrasonic motor according to claim 1, characterized in that, The formula for correcting the predicted real-time operating state of the system in step S2 is as follows: ； in, These are the local characteristic parameters of the system after correction. For about The sign function ensures that the local feature parameters are always non-positive.

5. The quadratic programming optimization method for model-free speed control of an ultrasonic motor according to claim 1, characterized in that, The current working set in step S5 Minimum under constraints and the corresponding Lagrange multipliers The calculation formula is: ； in, , , , For work set The coefficient matrix satisfies , and Characterizes the system's operating state. yes The transpose of the matrix, yes The inverse matrix, , yes The system expects to output at any given time. yes The system outputs the timeline. for Local characteristic parameters of the system after time-correction. for Time system input.

6. The quadratic programming optimization method for model-free speed control of an ultrasonic motor according to claim 1, characterized in that, In step S9, the particle movement step size is shortened according to the following formula. Calculate the minimum movement step size within the feasible region. : ； in, This represents the shortened particle movement step size.

7. The quadratic programming optimization method for model-free speed control of an ultrasonic motor according to claim 1, characterized in that, The influence of each control parameter on the motor speed control effect in step S12 is as follows: 。 8. A system for a quadratic programming optimization method for model-free speed control of an ultrasonic motor according to any one of claims 1 to 7, characterized in that, include: The initialization module is used to initialize the control parameters of the servo system, including the state estimation step size factor. State estimation weighting factor and system local characteristic parameters ; The prediction and correction module is used to predict the real-time operating status of the system and correct the predicted real-time operating status of the system. The quadratic programming optimization problem is constructed to generate a quadratic programming problem for the input estimate. The quadratic programming problem-solving module is used to find the optimal solution to a quadratic programming problem using the effective set method. The monitoring module is used to monitor the system's operating status in real time by selecting all or some of the control indicators, such as settling time, average steady-state error, overshoot, rise time, and unit control time, according to control requirements. The control parameter correction module is used to adjust the values ​​of the control parameters within the allowable range defined by each control parameter, based on the influence of each control parameter on the motor speed control effect.

9. A device, characterized in that, Includes memory and processor, wherein: Memory is used to store computer programs that can run on a processor; A processor, configured to, while running the computer program, execute the steps of the quadratic programming optimization method for model-free speed control of an ultrasonic motor as described in any one of claims 1-7.

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