An adaptive sliding mode control method for piezoelectric driving micro-gripper switching system

Abstract

The present application relates to a kind of piezoelectric drive micro clamp switching system adaptive sliding mode control method, comprising the following steps: S1, the dynamics model of piezoelectric micro clamp switching system is constructed and parameter identification is carried out;S2, based on the tracking error of desired displacement and actual displacement, the sliding surface of fixed time convergence is designed, and the initial control voltage of non-contact state and contact state is obtained;S3, the fuzzy logic system with sliding surface as input and control gain as output is constructed, and the accurate control gain is obtained by defuzzification, and the fuzzy fixed time sliding mode control law is obtained;S4, the comprehensive disturbance of system is estimated in real time, and the estimated value is fed back to compensate in fuzzy fixed time sliding mode control law, and the control voltage with disturbance compensation is obtained;S5, set adaptive switching boundary layer, output final control voltage to drive unit.The beneficial effects of the present application are: solve the problem of large modeling error of traditional single model, and the problem of insufficient hysteresis fitting precision.

Description

Technical Field

[0001] This invention relates to the field of precision robot automation control technology, specifically to an adaptive sliding mode control method for a piezoelectric-driven micro-gripper switching system. Background Technology

[0002] Micro- and nano-manipulation devices are core equipment for positioning and manipulation at the micro- and nano-scale, and are widely used in high-end manufacturing and scientific research scenarios such as biological cell manipulation, chip packaging, and microelectromechanical system assembly. Among them, piezoelectric microgrippers have become the mainstream choice for micro- and nano-manipulation actuators due to their advantages such as nanometer-level positioning accuracy, millisecond-level fast response, and large working stroke.

[0003] In existing technologies, Preisach models, Maxwell models, and traditional Bouc-Wen models are commonly used to fit hysteresis characteristics. However, these models can only achieve fitting with limited accuracy and cannot fundamentally solve the control deviation caused by hysteresis. Furthermore, the model parameter identification efficiency is low. In addition, the piezoelectric microgripper needs to switch between two core working states, non-contact and contact, in the complete workflow of cell, clamping, transport, and release. This results in problems such as large modeling errors and insufficient hysteresis fitting accuracy of traditional single models. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this application proposes an adaptive sliding mode control method for a piezoelectric-driven micro-clamp switching system. Dynamic models are constructed for the non-contact and contact working states of the micro-clamp, respectively. A symbolically enhanced Bouc-Wen model is used to describe the hysteresis nonlinearity, and a particle swarm optimization algorithm is combined to achieve global optimal identification of model parameters, which significantly improves the modeling accuracy of hysteresis characteristics and solves the problems of large modeling error and insufficient hysteresis fitting accuracy of traditional single models.

[0005] The following is the technical solution of the present invention: an adaptive sliding mode control method for a piezoelectric-driven micro-gripper switching system, comprising the following steps: S1. Construct a dynamic model of the piezoelectric micro-clamp switching system and identify its parameters. The system includes non-contact and contact states. S2. Based on the tracking error between the expected displacement and the actual displacement, a sliding mode surface with fixed-time convergence is designed, and a fixed-time sliding mode control law is constructed based on the constant velocity approach law to obtain the initial control voltages for the non-contact state and the contact state. S3. Construct a fuzzy logic system with sliding surface as input and control gain as output. Obtain the precise control gain by defuzzification and use it to replace the discontinuous terms in the fixed-time sliding control law to obtain the fuzzy fixed-time sliding control law. S4. Design a disturbance estimator to estimate the comprehensive disturbance of the system in real time, and feed the estimated value back to the fuzzy fixed-time sliding mode control law to obtain the control voltage with disturbance compensation. S5. Set an adaptive switching boundary layer. Adjust the boundary layer thickness and switching signal adaptively according to the relationship between the actual displacement and the desired displacement switching point. Use the switching signal to smoothly switch the control voltage with disturbance compensation in the non-contact state and the contact state, and output the final control voltage to the drive unit.

[0006] As a preferred embodiment of the present invention, S1 includes the following steps: S101. Based on the non-contact and contact states of the piezoelectric microclamp during cell manipulation, corresponding dynamic equations are established respectively. The model divides the system into a nominally definite part and an uncertain part. S102. The piezoelectric hysteresis effect is described by a symbolically enhanced Bouc-Wen model, and the model parameters are identified using a particle swarm optimization algorithm, with the goal of minimizing the sum of squared errors between the experimental displacement and the model-identified displacement.

[0007] As a preferred embodiment of the present invention, in S101, the expression for the non-contact state dynamic model is as follows: In the above formula, , , These are the equivalent mass, damping coefficient, and equivalent stiffness of the gripper fingers, respectively. These correspond to the finger displacement, velocity, and acceleration at time t, respectively. Voltage coefficient, The hysteresis effect of the piezoelectric microclamp is described. To control the voltage, This represents external disturbances that are not modeled. The contact state dynamic model is expressed as follows: In the above formula, , , These are the equivalent mass, damping coefficient, and equivalent stiffness of the gripper fingers, respectively. These correspond to the finger displacement, velocity, and acceleration at time t, respectively. Voltage coefficient, The hysteresis effect of piezoelectric microclamps To control the voltage, This represents external unmodeled disturbance terms. This represents the total force, including the contact force between the microgripper and the object, the external load force, and other unmodeled additional forces. , , For nominal items, , , This is an uncertain term.

[0008] As a preferred embodiment of the present invention, S2 includes the following steps: S201. Define the system tracking error based on the expected displacement signal and the actual feedback displacement signal; S202, Design a sliding surface that combines superlinear and sublinear terms; S203. Based on the constant velocity approach law, the initial control voltages for the non-contact state and the contact state are derived respectively.

[0009] As a preferred embodiment of the present invention, in S2, the expression of the sliding surface is as follows: In the above formula, and These are the superlinear gain coefficient and the sublinear gain coefficient, respectively. It is a superlinear power exponent. It is a sublinear power exponent. It is a piecewise function.

[0010] As a preferred embodiment of the present invention, S3 includes the following steps: S301. The fuzzy logic system is a single-input single-output system, with the sliding surface as the input and the sliding control switching gain as the output. S302. Define the fuzzy set of input and output as {negative large, negative small, zero, positive small, positive large}, and formulate fuzzy control rules; S303. The center-average defuzzification method is used to obtain the precise control gain; S304. Replace the symbolic function term in the initial control law with the control gain obtained by defuzzification to obtain the fuzzy fixed-time sliding mode control law.

[0011] In a preferred embodiment of the present invention, in S302, the fuzzy control rules include: If the sliding surface is negatively large, then the control gain is negatively large; If the sliding surface is negatively small, then the control gain is negatively small; If the sliding surface is zero, then the control gain is zero; If the sliding surface is positively small, then the control gain is positively small; If the sliding surface is positive, then the control gain is positive.

[0012] In a preferred embodiment of the present invention, the perturbation estimator expression in S4 is as follows: in, These are auxiliary variables used for perturbation estimation; This represents the perturbation estimation gain.

[0013] When the micro clamp is in an uncontacted state: in, , A non-zero constant, a constant satisfy ; When the micro clamp is in contact: .

[0014] As a preferred embodiment of the present invention, S5 includes the following steps: S501. Define the adaptive boundary layer interval centered on the desired displacement switching point and the boundary layer thickness Φ. S502. Based on the system sliding mode surface value, the boundary layer thickness is dynamically adjusted using an adaptive law; S503. Based on the positional relationship between the actual displacement and the boundary layer interval, generate a switching signal that continuously varies between 0 and 1; S504: Based on the switching signal, the non-contact state control voltage and the contact state control voltage with disturbance compensation are weighted and fused, and the final control voltage is output to the drive unit.

[0015] As a preferred embodiment of the present invention, in S504, the expression for the control voltage is as follows: In the above formula, To switch signals, This is the control voltage with disturbance compensation in non-contact mode. The control voltage is for contact state with disturbance compensation.

[0016] The beneficial effects of this invention are: 1. In this invention, dynamic models are constructed for the non-contact and contact working states of the micro clamp, respectively. The symbol-enhanced Bouc-Wen model is used to describe the hysteresis nonlinearity. The particle swarm optimization algorithm is combined to achieve the global optimal identification of the model parameters, which greatly improves the modeling accuracy of the hysteresis characteristics and solves the problems of large modeling error and insufficient hysteresis fitting accuracy of traditional single model. 2. In this invention, by using a sliding mode control law with fixed-time convergence, the dependence of the convergence time of traditional sliding mode control on the initial conditions of the system is eliminated, ensuring that the system can converge to the desired trajectory within a preset fixed time under any initial conditions, which greatly improves the consistency and stability of the dynamic response of the micro clamp. Attached Figure Description

[0017] Figure 1 This is a flowchart illustrating the control method steps of the present invention; Figure 2 Error identification diagram for the control method of the present invention Figure 3 This is a displacement tracking error diagram of the control method of the present invention. Detailed Implementation

[0018] To make the technical problems solved by the present invention, the technical solutions adopted, and the technical effects achieved clearer, the technical solutions of the embodiments of the present invention will be further described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] Example like Figures 1 to 3 As shown, an adaptive sliding mode control method for a piezoelectric-driven micro-gripper switching system includes the following steps: S1. Construct a dynamic model of the piezoelectric micro-clamp switching system and identify its parameters. The system includes non-contact and contact states. S2. Based on the tracking error between the expected displacement and the actual displacement, a sliding mode surface with fixed-time convergence is designed, and a fixed-time sliding mode control law is constructed based on the constant velocity approach law to obtain the initial control voltages for the non-contact state and the contact state. S3. Construct a fuzzy logic system with sliding surface as input and control gain as output. Obtain the precise control gain by defuzzification and use it to replace the discontinuous terms in the fixed-time sliding control law to obtain the fuzzy fixed-time sliding control law. S4. Design a disturbance estimator to estimate the comprehensive disturbance of the system in real time, and feed the estimated value back to the fuzzy fixed-time sliding mode control law to obtain the control voltage with disturbance compensation. S5. Set an adaptive switching boundary layer. Adjust the boundary layer thickness and switching signal adaptively according to the relationship between the actual displacement and the desired displacement switching point. Use the switching signal to smoothly switch the control voltage with disturbance compensation in the non-contact state and the contact state, and output the final control voltage to the drive unit.

[0020] In step S1, a dynamic model of the piezoelectric micro-clamp switching system is constructed and its parameters are identified. The system includes non-contact and contact states. Specifically, a dynamic model of the piezoelectric micro-clamp switching system is constructed and its parameters are identified. Based on the workflow of cell tracking and clamping of the piezoelectric micro-clamp, two core states, non-contact and contact, are defined, and corresponding dynamic models are constructed for each. Simultaneously, the system is divided into a nominally deterministic part and an uncertain part to improve the robustness of the model.

[0021] S101. Based on the non-contact and contact states of the piezoelectric microclamp during cell manipulation, corresponding dynamic equations are established respectively. The model divides the system into a nominally definite part and an uncertain part.

[0022] A non-contact dynamic model was constructed, with the micro-clamp fingers not in contact with the cell and in an unloaded tracking state. The dynamic model expression is as follows: In the above formula, , , These are the equivalent mass, damping coefficient, and equivalent stiffness of the gripper fingers, respectively. These correspond to the finger displacement, velocity, and acceleration at time t, respectively. Voltage coefficient, The hysteresis effect of the piezoelectric microclamp is described. To control the voltage, This represents external disturbances that are not modeled.

[0023] A contact state dynamics model is constructed. When the microclamping fingers come into contact with the cell and enter the clamping state, the influence of contact forces needs to be considered. The dynamics model is expressed as follows: In the above formula, , , These are the equivalent mass, damping coefficient, and equivalent stiffness of the gripper fingers, respectively. These correspond to the finger displacement, velocity, and acceleration at time t, respectively. Voltage coefficient, The hysteresis effect of the piezoelectric microclamp is described. To control the voltage, This represents external unmodeled disturbance terms. This represents the contact force between the pincers and the object.

[0024] Due to the presence of unmodeled perturbation terms, the piezoelectric microgripper model cannot be accurately established. Therefore, the piezoelectric microgripper model can be divided into a deterministic part and an uncertain part, as expressed below: S102. The piezoelectric hysteresis effect is described by a symbolically enhanced Bouc-Wen model, and the model parameters are identified using a particle swarm optimization algorithm, with the goal of minimizing the sum of squared errors between the experimental displacement and the model-identified displacement.

[0025] A hysteresis nonlinear model was constructed and parameters were identified. The symbolically enhanced Bouc-Wen model was used to describe the hysteresis nonlinearity of the piezoelectric microgripper. The model expression is as follows: In the above formula, Its shape and size are determined by parameters Decide, Its shape and size are determined by parameters Decide,.

[0026] Particle swarm optimization (PSO) was used for parameter identification, with a population size of 50, an iteration count of 200, and a learning factor of [missing information]. Inertial weight The objective function is to minimize the sum of squared errors between the experimental displacement and the model-identified displacement, and its expression is as follows: In the above formula, The total number of sampled data. For experimentally measured displacement, Displacements are identified for the symbolically enhanced Bouc-Wen model.

[0027] In step S2, based on the tracking error between the desired displacement and the actual displacement, a sliding mode surface with fixed-time convergence is designed, and a fixed-time sliding mode control law is constructed based on the constant velocity reaching law to obtain the initial control voltages for the non-contact and contact states. Specifically, based on the desired displacement signal of the piezoelectric microgripper and the actual feedback displacement signal collected in real time by the displacement sensor, the system tracking error is defined, a sliding mode surface with fixed-time convergence is designed, and a fixed-time sliding mode control law is constructed based on the constant velocity reaching law to ensure that the system state converges to the sliding mode surface within a fixed time independent of the initial conditions.

[0028] S201. Define the system tracking error based on the expected displacement signal and the actual feedback displacement signal.

[0029] The system tracking error is defined as follows: In the above formula, For the desired displacement signal, This is the actual feedback displacement signal.

[0030] S202. Design a sliding surface that combines superlinear and sublinear terms, which converges to the sliding surface in a fixed time independent of initial conditions.

[0031] Design a fixed-time convergent sliding surface, expressed as follows: In the above formula, and These are the superlinear gain coefficient and the sublinear gain coefficient, respectively. It is a superlinear power exponent. It is a sublinear power exponent. This is a piecewise function. In this embodiment, the sliding surface parameters are set to... =1200, =800, =5, =3, =3, =5.

[0032] S203. Based on the constant velocity approach law, the initial control voltages for the non-contact and contact states are derived respectively, and the fixed-time convergence of the system is proved by Lyapunov stability analysis.

[0033] Based on the constant velocity reaching law, a fixed-time sliding mode control law is constructed to obtain the initial control voltage in the non-contact state, as expressed below: The initial control voltage for the contact state is obtained as follows: .

[0034] In this embodiment, the reaching law gain is set to =150, =180.

[0035] Lyapunov stability analysis is used to construct the Lyapunov function, whose expression is as follows: Differentiation yields: In the above formula, It is a positive number.

[0036] The system meets the fixed-time stability criterion, and the upper bound expression for the convergence time is: It does not depend on the initial conditions of the system.

[0037] In step S3, a fuzzy logic system is constructed with the sliding surface as input and the control gain as output. The precise control gain is obtained through defuzzification and used to replace the discontinuous terms in the fixed-time sliding mode control law, resulting in a fuzzy fixed-time sliding mode control law. Specifically, a single-input, single-output fuzzy logic system is constructed, using the real-time value of the sliding surface as input and the switching gain of the sliding mode control as output. Fuzzy control rules matching the sliding mode control requirements are formulated. The center-average defuzzification method is used to obtain the precise control gain, which is then used to replace the discontinuous symbolic function terms in the fixed-time sliding mode control law, resulting in a fuzzy fixed-time sliding mode control law that suppresses chattering during system switching.

[0038] S301. Establish a single-input single-output fuzzy logic system with a sliding surface as input and a sliding control switching gain output.

[0039] Construct a single-input, single-output fuzzy logic system using a sliding surface. As input, the gain is switched using sliding mode control. For output, suppress control chattering.

[0040] S302. Formulate corresponding fuzzy control rules based on the sign and size of the sliding surface.

[0041] Define the fuzzy sets of input and output as {negative large, negative small, zero, positive small, positive large}, use the Gaussian membership function as the membership function, the input universe of discourse is [-6,6], and the output universe of discourse is [-300,300].

[0042] Formulate fuzzy control rules, including: if the sliding surface s is negatively large, then the control gain... If the sliding surface s is negatively large, then the control gain is negatively small. The negative value is small; if the sliding surface s is zero, then the control gain is small. The control gain is zero; if the sliding surface s is positively small, then the control gain is zero. If the sliding surface s is positively small, then the control gain is positively large. It is upright and righteous.

[0043] S303. The precise control gain is obtained by using the center-average defuzzification method, and it is used to replace the discontinuous symbolic function term in the control law in step S2 to form a fuzzy fixed-time sliding mode control law.

[0044] The precise control gain is obtained using the center-average defuzzification method, as expressed below: in, Represents the number of rules. This represents the result of the fuzzy rule. and It is the weight of a single-point fuzzy subset.

[0045] Obtained by defuzzification Replace the symbolic function term in the initial control law This yields a fuzzy fixed-time sliding mode control law, eliminating high-frequency chattering caused by the sign function and improving the smoothness of the control voltage.

[0046] In step S4, a disturbance estimator is designed to estimate the comprehensive disturbance of the system in real time, and the estimated value is fed back to compensate the fuzzy fixed-time sliding mode control law to obtain the control voltage with disturbance compensation. Specifically, a disturbance estimator is designed to estimate the comprehensive disturbance formed by the coupling of external unknown disturbances and parameter uncertainties during the operation of the piezoelectric micro-clamp switching system in real time, and the disturbance estimate is compensated to the fuzzy fixed-time sliding mode control law to obtain the control law with disturbance compensation.

[0047] S401. Design an observer to perform real-time online estimation of the combined disturbances formed by the coupling of external unknown disturbances and parameter uncertainties during system operation.

[0048] Design a disturbance estimator to perform real-time online estimation of the combined disturbances formed by unmodeled disturbances, parameter uncertainties, and external interference coupling of the system. The expression for the disturbance estimator is as follows: in, These are auxiliary variables used for perturbation estimation.

[0049] When the micro clamp is in an uncontacted state: in, , A non-zero constant, a constant satisfy .

[0050] When the micro clamp is in contact: .

[0051] The perturbation estimation error is defined as follows: In the above formula, For the actual comprehensive disturbance of the system, The error is the disturbance estimation error. Lyapunov stability analysis proves that the estimation error converges to near zero within a fixed time, and the estimation accuracy meets the control requirements.

[0052] S402. Feedback the disturbance estimate obtained in step S401 to the fuzzy fixed-time sliding mode control law obtained in step S3 to obtain the control voltage with disturbance compensation in non-contact and contact states respectively.

[0053] The disturbance estimate is compensated into the fuzzy fixed-time sliding mode control law to obtain the control voltage with disturbance compensation. The expression for the control voltage with disturbance compensation in the non-contact state is as follows: The expression for the control voltage with disturbance compensation in the contact state is as follows: In the above formula, This is the control voltage with disturbance compensation in non-contact mode. The control voltage with disturbance compensation in the contact state. The control input gain in the non-contact state. This represents the control input gain under contact conditions. Feedback compensation eliminates the impact of unknown disturbances on system control accuracy and improves the system's anti-interference capability.

[0054] In step S5, an adaptive switching boundary layer is set. The boundary layer thickness and switching signal are adaptively adjusted based on the relationship between the actual displacement and the desired displacement switching point. The switching signal is used to smoothly fuse the disturbance-compensated control voltage between the non-contact and contact states, and the final control voltage is output to the drive unit. Specifically, an adaptive switching boundary layer is set, and an adaptive law for the boundary layer thickness is designed. Based on the positional relationship between the actual displacement and the desired displacement switching point of the piezoelectric microgripper, the boundary layer thickness and switching signal are adaptively adjusted to achieve a smooth switch between the non-contact and contact states. The final control voltage is output to the drive unit of the piezoelectric microgripper, completing closed-loop tracking control.

[0055] S501. Define an adaptive boundary layer interval centered on the desired displacement switching point and the boundary layer thickness.

[0056] An adaptive switching boundary layer is set to achieve a smooth transition between non-contact and contact states, avoiding abrupt rate changes.

[0057] First, define the adaptive switching boundary layer interval, as shown in the following expression: In the above formula, For the desired displacement switching point, To adapt to the boundary layer thickness. In this embodiment, the desired displacement switching point is... =43 The maximum thickness of the boundary layer corresponds to the position where the clamp finger is about to contact the cell. =2 .

[0058] S502. Dynamically adjust the boundary layer thickness based on the system sliding surface value.

[0059] The expression for the adaptive law for designing the boundary layer thickness is as follows: In the above formula, The attenuation coefficient is... For adaptive gain, This represents the maximum thickness of the boundary layer. In this embodiment, the attenuation coefficient... =5, adaptive gain =120, guaranteeing Always in (0, Adjustments can be made within the specified range.

[0060] S503. Based on the positional relationship between the actual displacement and the boundary layer interval, generate a switching signal that continuously varies between 0 and 1.

[0061] Define the switching signal as follows: In the above formula, Corresponding to the non-contact state, Corresponding to the contact state. In the non-contact state, using... Control is performed; in the contact state, the following is adopted: Control is implemented; within the boundary layer interval, the switching signal transitions linearly to achieve smooth state switching.

[0062] S504: Based on the switching signal, the non-contact state control voltage and the contact state control voltage with disturbance compensation are weighted and fused, and the final control voltage is output to the drive unit for smooth switching between the two states.

[0063] The final output control voltage is expressed as follows: The final control voltage is output to the drive amplifier of the piezoelectric micro-clamp, which drives the clamp fingers to move. At the same time, the clamp finger displacement signal is collected in real time by a laser displacement sensor for feedback control.

[0064] A simulation model was built on the MATLAB / Simulink platform to compare and verify the proposed adaptive fuzzy fixed-time sliding mode control method (DA-FFTSMC) based on disturbance estimation with the traditional sliding mode control method (SMC). The same controlled object model, desired trajectory, and external disturbance were set. The maximum steady-state tracking error of the traditional sliding mode control was 0.045 μm, while the maximum steady-state tracking error of the proposed method was only 0.01 μm, significantly improving the tracking accuracy.

[0065] This invention constructs dynamic models for both non-contact and contact working states of the micro-gripper, employing a symbolically enhanced Bouc-Wen model to describe hysteresis nonlinearity. Combined with particle swarm optimization, it achieves global optimal identification of model parameters, significantly improving the modeling accuracy of hysteresis characteristics and solving the problems of large modeling errors and insufficient hysteresis fitting accuracy in traditional single-model approaches. Furthermore, by using a fixed-time convergent sliding mode control law, it eliminates the dependence of traditional sliding mode control convergence time on initial system conditions, ensuring that the system can converge to the desired trajectory within a preset fixed time under any initial conditions. This significantly improves the consistency and stability of the micro-gripper's dynamic response.

[0066] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Clearly, those skilled in the art can make various alterations and variations to the invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of equivalents of the invention, the invention is also intended to include these modifications and variations.

Claims

1. An adaptive sliding mode control method for a piezoelectric-driven micro-gripper switching system, characterized in that, Includes the following steps: S1. Construct a dynamic model of the piezoelectric micro-clamp switching system and identify its parameters. The system includes non-contact and contact states. S2. Based on the tracking error between the expected displacement and the actual displacement, a sliding mode surface with fixed-time convergence is designed, and a fixed-time sliding mode control law is constructed based on the constant velocity approach law to obtain the initial control voltages for the non-contact state and the contact state. S3. Construct a fuzzy logic system with sliding surface as input and control gain as output. Obtain the precise control gain by defuzzification and use it to replace the discontinuous terms in the fixed-time sliding control law to obtain the fuzzy fixed-time sliding control law. S4. Design a disturbance estimator to estimate the comprehensive disturbance of the system in real time, and feed the estimated value back to the fuzzy fixed-time sliding mode control law to obtain the control voltage with disturbance compensation. S5. Set an adaptive switching boundary layer. Adjust the boundary layer thickness and switching signal adaptively according to the relationship between the actual displacement and the desired displacement switching point. Use the switching signal to smoothly switch the control voltage with disturbance compensation in the non-contact state and the contact state, and output the final control voltage to the drive unit.

2. The adaptive sliding mode control method for a piezoelectric-driven micro-gripper switching system according to claim 1, characterized in that, S1 includes the following steps: S101. Based on the non-contact and contact states of the piezoelectric microclamp during cell manipulation, corresponding dynamic equations are established respectively. The model divides the system into a nominally definite part and an uncertain part. S102. The piezoelectric hysteresis effect is described by a symbolically enhanced Bouc-Wen model, and the model parameters are identified using a particle swarm optimization algorithm, with the goal of minimizing the sum of squared errors between the experimental displacement and the model-identified displacement.

3. In the adaptive sliding mode control method for a piezoelectric-driven micro-gripper switching system according to claim 2, in S101, the expression for the non-contact state dynamic model is as follows: In the above formula, , , These are the equivalent mass, damping coefficient, and equivalent stiffness of the gripper fingers, respectively. These correspond to the finger displacement, velocity, and acceleration at time t, respectively. For voltage coefficient, The hysteresis effect of the piezoelectric microclamp is described. To control the voltage, This represents external disturbances that are not modeled. The contact state dynamic model is expressed as follows: In the above formula, , , These are the equivalent mass, damping coefficient, and equivalent stiffness of the gripper fingers, respectively. These correspond to the finger displacement, velocity, and acceleration at time t, respectively. For voltage coefficient, The hysteresis effect of piezoelectric microclamps To control the voltage, This represents external unmodeled disturbance terms. This represents the combined forces, which consist of the interaction forces between the microclamp and the cell, the external load forces, and other unmodeled additional forces.

4. The adaptive sliding mode control method for a piezoelectric-driven micro-gripper switching system according to claim 1, characterized in that, S2 includes the following steps: S201. Define the system tracking error based on the expected displacement signal and the actual feedback displacement signal; S202, Design a sliding surface that combines superlinear and sublinear terms; S203. Based on the constant velocity approach law, the initial control voltages for the non-contact state and the contact state are derived respectively.

5. The adaptive sliding mode control method for a piezoelectric-driven micro-gripper switching system according to claim 4, characterized in that, In S2, the expression for the sliding surface is as follows: In the above formula, and These are the superlinear gain coefficient and the sublinear gain coefficient, respectively. It is a superlinear power exponent. It is a sublinear power exponent. It is a piecewise function.

6. The adaptive sliding mode control method for a piezoelectric-driven micro-gripper switching system according to claim 1, characterized in that, S3 includes the following steps: S301. The fuzzy logic system is a single-input single-output system, with the sliding surface as the input and the sliding control switching gain as the output. S302. Define the fuzzy set of input and output as {negative large, negative small, zero, positive small, positive large}, and formulate fuzzy control rules; S303. The center-average defuzzification method is used to obtain the precise control gain; S304. Replace the symbolic function term in the initial control law with the control gain obtained by defuzzification to obtain the fuzzy fixed-time sliding mode control law.

7. The adaptive sliding mode control method for a piezoelectric-driven micro-gripper switching system according to claim 5, characterized in that, In S302, the fuzzy control rules include: If the sliding surface is negatively large, then the control gain is negatively large; If the sliding surface is negatively small, then the control gain is negatively small; If the sliding surface is zero, then the control gain is zero; If the sliding surface is positively small, then the control gain is positively small; If the sliding surface is positive, then the control gain is positive.

8. The adaptive sliding mode control method for a piezoelectric-driven micro-gripper switching system according to claim 1, characterized in that, In S4, the perturbation estimator expression is as follows: in, These are auxiliary variables used for perturbation estimation; Represents the perturbation estimation gain; When the micro clamp is in an uncontacted state: in, , A non-zero constant, a constant satisfy ; For nominal items; When the micro clamp is in contact: ， This is a nominal item.

9. The adaptive sliding mode control method for a piezoelectric-driven micro-gripper switching system according to claim 1, characterized in that, S5 includes the following steps: S501. Define the adaptive boundary layer interval centered on the desired displacement switching point and the boundary layer thickness Φ. S502. Based on the system sliding mode surface value, the boundary layer thickness is dynamically adjusted using an adaptive law; S503. Based on the positional relationship between the actual displacement and the boundary layer interval, generate a switching signal that continuously varies between 0 and 1; S504: Based on the switching signal, the non-contact state control voltage and the contact state control voltage with disturbance compensation are weighted and fused, and the final control voltage is output to the drive unit.

10. The adaptive sliding mode control method for a piezoelectric-driven micro-gripper switching system according to claim 9, characterized in that, In S504, the control voltage expression is as follows: In the above formula, To switch signals, This is the control voltage with disturbance compensation in non-contact mode. The control voltage is for contact state with disturbance compensation.

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