A method for shape and trajectory cooperative control of a magnetic micro-robot cluster

Abstract

The application discloses a morphology and trajectory cooperative control method of a magnetic micro-robot cluster, and belongs to the technical field of micro-nano robots. Step 1: a state space evolution model of the centroid and morphology of the micro-robot cluster is constructed; step 2: based on the model of step 1, a space-time flow field disturbance observer fusing multi-physical field priori is designed; step 3: based on the disturbance compensation of step 2, a centroid and morphology cooperative controller based on an event triggering mechanism is designed; and step 4: based on the cooperative controller of step 3, the morphology and trajectory cooperative control of the magnetic micro-robot cluster is realized. The application aims at the problems of model mismatch, observation lag and easy overload and heating of an executing mechanism in the existing magnetic micro-robot cluster control system.

Description

Technical Field

[0001] This invention belongs to the field of micro-nano robotics technology, specifically relating to a method for coordinated control of the morphology and trajectory of a magnetic microrobot swarm. Background Technology

[0002] In recent years, magnetic micro- and nanorobots have shown great application potential in biomedical fields such as targeted drug delivery, thrombus removal, and minimally invasive surgery due to their advantages such as cableless, non-contact actuation, strong penetrating power, and good biocompatibility. Because of the limited carrying capacity and execution efficiency of a single microrobot, current research trends increasingly employ microrobot swarms for collaborative operations to improve task success rates and system robustness. Magnetic microrobot swarm systems, as a typical underactuated, strongly coupled nonlinear control system, require the controlled object to not only achieve precise transfer of its macroscopic center of mass but also adjust its microscopic clustering morphology in real time according to the constraints of the external spatial topology.

[0003] Existing methods for trajectory tracking and motion control of microrobot swarms generally suffer from the following shortcomings: 1) the controlled object is modeled with a single dimension, lacking system-level control over multi-variable coupled states; 2) disturbance observers based on pure mathematics exhibit phase lag; and 3) high-frequency chattering and actuator constraints arise from continuous robust control. Therefore, there is an urgent need to design a collaborative control method that integrates prior physical characteristics and a multi-objective decoupling architecture to achieve high-precision closed-loop control of microrobot swarms. Summary of the Invention

[0004] This invention provides a method for coordinated control of the morphology and trajectory of a magnetic microrobot swarm, addressing the problems of model mismatch, observation lag, and overload and overheating of actuators in existing magnetic microrobot swarm control systems.

[0005] This invention is achieved through the following technical solution:

[0006] A method for coordinated morphological and trajectory control of a magnetic microrobot swarm, the control method comprising the following steps:

[0007] Step 1: Establish a morphological dynamic model of the center of mass of the microrobot cluster, and define the joint state variables of the system, taking into account fluid resistance, pulsating flow, and complex boundaries;

[0008] Step 2: Based on the model in Step 1, design a spatiotemporal flow field disturbance observer to compensate for the external disturbance forces experienced by the microrobot swarm during its movement.

[0009] Step 3: Based on the disturbance compensation in Step 2, design a centroid-morphology cooperative controller based on an event triggering mechanism, construct a joint triggering function for discretization update and issue control commands to realize the morphology and trajectory cooperative control of the magnetic microrobot cluster.

[0010] Further, step 1 specifically involves establishing a joint nonlinear dynamic model that considers non-Newtonian fluid drag, time-varying flow field fluctuations, and internal magnetic dipole coupling force, using the centroid pose vector of the microrobot cluster and the morphological covariance matrix representing the cluster density distribution as joint state variables of the system; based on non-singular perturbation theory, the joint nonlinear dynamic model is decoupled into a macroscopic centroid translation subsystem and a microscopic morphological evolution subsystem, and the unmodeled fluid dynamic characteristics are defined as the lumped perturbation term of the system.

[0011] Furthermore, step 2 specifically involves using a microscopic imaging device to acquire the current real-time state information of the controlled object and the geometric topological boundary features of the execution space at the current moment; using a pre-constructed multiphysics fluid dynamics prior mapping matrix, a velocity gradient prediction term is introduced into the dynamic update law of the traditional nonlinear observer to construct a spatiotemporal flow field disturbance observer; through the spatiotemporal flow field disturbance observer, combined with real-time state feedback and physical priors, a high-precision observation value of the lumped disturbance term is calculated and output in real time.

[0012] Furthermore, step 3 specifically involves designing an adaptive sliding mode control law based on feedforward compensation of lumped disturbance observations for the macroscopic centroid translation subsystem, and calculating the first control input for stabilizing trajectory tracking error; and designing an auxiliary morphological control law for the microscopic morphological evolution subsystem based on the morphological evolution error calculated by the desired spatial geometric constraints, and calculating the second control input for adjusting the cluster density.

[0013] Furthermore, an adaptive event triggering mechanism is constructed, defining a joint triggering function that includes system state tracking error and fluid disturbance change rate; only when the value of the joint triggering function exceeds the dynamic safety threshold, the first control input and the second control input are updated and sent to the external electromagnetic actuator; in the non-triggered state, the electromagnetic actuator maintains the control input at the previous triggering moment to complete the collaborative control of the micro-robot cluster.

[0014] A morphology and trajectory collaborative control system for a magnetic microrobot swarm, the control system employing the morphology and trajectory collaborative control method for a magnetic microrobot swarm as described above, the control system comprising:

[0015] Model building module: Establish a morphological dynamic model of the center of mass of the microrobot cluster, define the joint state variables of the system, and consider fluid resistance, pulsating flow, and complex boundaries;

[0016] Spatiotemporal flow field disturbance observer design module: Based on the model provided by the model building module, design a spatiotemporal flow field disturbance observer to compensate for external disturbance forces encountered during the movement of the microrobot swarm;

[0017] The design module for the centroid-morphology collaborative controller: Based on spatiotemporal flow field disturbance compensation, a centroid-morphology collaborative controller based on an event triggering mechanism is designed. The joint triggering function is discretized and updated to issue control commands, thereby realizing the collaborative control of the morphology and trajectory of the magnetic microrobot cluster.

[0018] Furthermore, the model building module specifically involves establishing a joint nonlinear dynamic model that considers non-Newtonian fluid drag, time-varying flow field fluctuations, and internal magnetic dipole coupling force, using the centroid pose vector of the microrobot cluster and the morphological covariance matrix representing the cluster density distribution as joint state variables of the system. Based on non-singular perturbation theory, the joint nonlinear dynamic model is decoupled into a macroscopic centroid translation subsystem and a microscopic morphological evolution subsystem, and the unmodeled fluid dynamic characteristics are defined as the lumped perturbation term of the system.

[0019] Furthermore, the spatiotemporal flow field disturbance observer design module specifically involves: acquiring the current real-time state information of the controlled object and the geometric topological boundary features of the execution space at the current moment using a microscopic imaging device; introducing a velocity gradient prediction term into the dynamic update law of a traditional nonlinear observer using a pre-constructed multiphysics fluid dynamics prior mapping matrix to construct a spatiotemporal flow field disturbance observer; and using the spatiotemporal flow field disturbance observer, combining real-time state feedback and physical priors to calculate and output the high-precision observation value of the lumped disturbance term in real time.

[0020] Furthermore, the centroid and morphology co-controller design module specifically involves designing an adaptive sliding mode control law based on feedforward compensation of the lumped disturbance observations for the macroscopic centroid translation subsystem, and calculating the first control input for stabilizing trajectory tracking errors; and designing an auxiliary morphology control law based on the morphology evolution error calculated according to the desired spatial geometric constraints for the microscopic morphology evolution subsystem, and calculating the second control input for adjusting the cluster density.

[0021] Furthermore, an adaptive event triggering mechanism is constructed, defining a joint triggering function that includes system state tracking error and fluid disturbance change rate; only when the value of the joint triggering function exceeds the dynamic safety threshold, the first control input and the second control input are updated and sent to the external electromagnetic actuator; in the non-triggered state, the electromagnetic actuator maintains the control input at the previous triggering moment to complete the collaborative control of the micro-robot cluster.

[0022] The beneficial effects of this invention are:

[0023] This invention proposes a collaborative control architecture with decoupled dual systems, which achieves high-precision trajectory control while maintaining cluster form.

[0024] This invention introduces multiphysics dynamic priors into the disturbance observer, eliminating the phase lag of traditional control.

[0025] This invention significantly optimizes control output based on an event-triggered mechanism, greatly reducing system energy consumption and coil heating.

[0026] This invention constructs a dual-loop control architecture to achieve decoupling and collaborative stabilization of the cluster centroid trajectory and morphological evolution.

[0027] This invention introduces a spatiotemporal physical prior to improve the dynamic compensation accuracy of the system for sudden nonlinear fluid disturbances.

[0028] This invention reduces the frequency of high-frequency switching of the actuator while ensuring the asymptotic stability of the Lyapunov closed-loop system. Attached Figure Description

[0029] Figure 1 This is a flowchart of the method of the present invention.

[0030] Figure 2 This is a block diagram of the microrobot cluster collaborative control system of the present invention. Detailed Implementation

[0031] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods are omitted so as not to obscure the description of this application with unnecessary detail.

[0032] It should be understood that, when used in this specification and the appended claims, the term "comprising" indicates the presence of the described features, integrals, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or collections thereof.

[0033] It should also be understood that the terminology used in this application specification is for the purpose of describing particular embodiments only and is not intended to limit the application. As used in this application specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.

[0034] The technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of the embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0035] Many specific details are set forth in the following description in order to provide a full understanding of this application. However, this application may also be implemented in other ways different from those described herein. Those skilled in the art can make similar extensions without departing from the spirit of this application. Therefore, this application is not limited to the specific embodiments disclosed below.

[0036] Implementation Method 1

[0037] This embodiment provides a method for coordinated control of the morphology and trajectory of a magnetic microrobot swarm, such as... Figure 1 As shown, the control method includes the following steps:

[0038] Step 1: Establish a morphological dynamic model of the center of mass of the microrobot cluster, and define the joint state variables of the system, taking into account fluid resistance, pulsating flow, and complex boundaries;

[0039] Furthermore, such as Figure 2 As shown, the state-space evolution model of the centroid and morphology of the microrobot cluster in step 1 is specifically as follows: using the centroid pose vector of the microrobot cluster and the morphological covariance matrix representing the density distribution of the cluster as the joint state variables of the system, a joint nonlinear dynamic model considering non-Newtonian fluid drag, time-varying flow field fluctuations, and internal magnetic dipole coupling force is established; based on the nonsingular perturbation theory, the joint nonlinear dynamic model is decoupled into a macroscopic centroid translation subsystem and a microscopic morphological evolution subsystem, and the unmodeled fluid dynamic characteristics are defined as the lumped perturbation term of the system.

[0040] A centroid-morphodynamic model of a microrobot swarm under low Reynolds number conditions is established. In complex fluid environments with low Reynolds number conditions, such as microfluidic channels, the inertial force of the microrobot swarm is negligible, and the dynamic evolution of the system is jointly dominated by fluid viscous drag, external magnetic driving force, and magnetic dipole interaction force between internal particles.

[0041] Define system state variables, modeling the microrobot swarm as a deformable continuum with macroscopic translational and microscopic distribution characteristics. Define the macroscopic centroid position vector of the swarm as... The morphological covariance matrix characterizing the microscopic spatial distribution of a cluster is defined as follows: In this invention, the spatial morphological characteristics of a microrobot swarm are rigorously mapped to a three-dimensional covariance matrix. algebraic characteristics;

[0042] The eigenvectors uniquely determine the principal axis orientation (direction) of the cluster in three-dimensional space. The sum of eigenvalues ​​(the trace of the matrix) characterizes the spatial divergence (size) of the cluster; while The relative ratios of the three internal eigenvalues ​​precisely quantify the deformation state (shape) of the cluster across topological structures such as spherical, needle-like, and disk-like structures. This dimensionality reduction mapping transforms the highly free multi-body morphological evolution problem into a low-dimensional matrix algebraic control problem, significantly reducing the computational overhead of the host computer algorithm.

[0043] Based on the system's force equilibrium state, dynamic equations for the translation of the center of mass and the morphological evolution are established separately. For the macroscopic center of mass translation subsystem, the fluid viscous drag force is in equilibrium with the external magnetic field driving force and the environmental fluid thrust; its dynamic equation is as follows:

[0044] ,

[0045] in, The matrix represents the translational viscous damping coefficients of a microrobot swarm in a fluid. The external three-dimensional gradient magnetic field (i.e., the first control input) The equivalent magnetic driving force applied; Fluid thrust applied to complex time-varying flow fields; It is the lumped disturbance of the centroid translation, which includes unmodeled time-varying pulsating flow and nonlinear frictional forces at complex boundaries.

[0046] For the micromorphic evolution subsystem, its dynamic equation is:

[0047] ,

[0048] in, The equivalent damping parameter for morphological deformation; To adjust the external high-frequency oscillating magnetic field (i.e., the second control input) Dynamically controlled magnetic dipole interaction forces within the cluster; For non-uniform velocity gradients in the flow field The resulting fluid shear force; It is the lumped perturbation of morphological evolution.

[0049] To implement closed-loop control design, the above model is transformed into standard affine nonlinear state equations:

[0050] ,

[0051] ,

[0052] in, and The nominal nonlinear term is known in the system; , The input gain matrix; and This refers to the equivalent lumped disturbance that needs to be estimated and compensated in real time by the spatiotemporal disturbance observer.

[0053] Step 2: Design a spatiotemporal flow field disturbance observer to compensate for external disturbances experienced by the microrobot swarm during its movement;

[0054] Furthermore, step 2 specifically involves using a microscopic imaging device to acquire the current real-time state information of the controlled object and the geometric topological boundary features of the execution space at the current moment; using a pre-constructed multiphysics fluid dynamics prior mapping matrix, a velocity gradient prediction term is introduced into the dynamic update law of a traditional nonlinear observer to construct a spatiotemporal flow field disturbance observer; through the spatiotemporal flow field disturbance observer, combined with real-time state feedback and physical priors, the high-precision observation value of the lumped disturbance term is calculated and output in real time.

[0055] Due to the presence of extremely strong local eddies and dynamic disturbances in the confined space, conventional observers exhibit phase lag. This step introduces prior knowledge of fluid dynamics to improve the accuracy of feedforward compensation.

[0056] Spatial coordinates are extracted using a pre-constructed finite element multiphysics analysis model. With the nominal velocity gradient of the local flow field The mapping relationship is used to generate the prior flow field mapping matrix of the system. .

[0057] Design an observer dynamic update law. Address the lumped perturbation of the centroid translation subsystem. Define the observer auxiliary state variables Positive definite observer gain matrix and satisfy nonlinear functions Real-time location combined with visual feedback Extracting prior flow field prediction The dynamic equations for designing the spatiotemporal perturbation observer are as follows:

[0058] ,

[0059] in, This is the prior weight coefficient matrix.

[0060] The lumped disturbance observations output by the real-time solution are:

[0061] ,

[0062] This observer achieves advanced prediction and accurate approximation of abrupt hydrodynamic disturbances by fusing prior features of topological boundaries.

[0063] Step 3: Based on the disturbance compensation in Step 2, design a centroid-morphology cooperative controller based on an event triggering mechanism, construct a joint triggering function for discretization update and issue control commands to realize the morphology and trajectory cooperative control of the magnetic microrobot cluster.

[0064] Furthermore, step 3 specifically involves designing an adaptive sliding mode control law based on feedforward compensation of the lumped disturbance observations for the macroscopic centroid translation subsystem, and calculating the first control input (low-frequency gradient magnetic field force) for stabilizing trajectory tracking error; and designing an auxiliary morphological control law based on the morphological evolution error calculated by the desired spatial geometric constraints for the microscopic morphological evolution subsystem, and calculating the second control input (high-frequency oscillating magnetic field parameters) for adjusting the cluster density state.

[0065] Furthermore, an adaptive event triggering mechanism is constructed, defining a joint triggering function that includes system state tracking error and fluid disturbance change rate; only when the value of the joint triggering function exceeds the dynamic safety threshold, the first control input and the second control input are updated and sent to the external electromagnetic actuator; in the non-triggered state, the electromagnetic actuator maintains the control input at the previous triggering moment to complete the collaborative control of the micro-robot cluster.

[0066] Design a centroid-morphological cooperative controller based on an event-triggered mechanism. Solve the control law using the decoupled state variables and disturbance observations after order reduction.

[0067] Design an adaptive sliding mode control law for the centroid translation subsystem. Define the desired tracking trajectory. Centroid position error Since the system is dominated by first-order kinematics, the sliding surface is directly selected as... Differentiating with respect to the sliding surface yields...

[0068] ,

[0069] Introducing observer output Perform feedforward compensation and design the first control input. :

[0070] ,

[0071] in, For positive definite switching gain matrix, This is a saturation function used to reduce chattering. Due to unknown disturbances... The vast majority have been Cancel, gain Only a very small observation error boundary needs to be covered, making the calculated three-dimensional gradient magnetic field force smoother.

[0072] Design auxiliary morphological control laws. Based on the geometric boundary information of the confined flow channel space ahead, set the desired morphological covariance matrix for the safe passage of the cluster. The morphological evolution error matrix is ​​defined as follows: To ensure the convergence of the morphological error matrix, the desired magnetic dipole interaction force matrix within the cluster is first calculated. ,

[0073] in, Used to feedforward offset the affine deformation shear force generated by the time-varying velocity gradient on the cluster; For linear error feedback stabilization term, It is a positive definite diagonal gain matrix; For robust control terms, constant matrix Used to suppress the influence of unmodeled perturbations at the microscopic level on morphology.

[0074] Furthermore, based on the nonlinear mapping relationship between the external high-frequency alternating magnetic field and the dipole force... The second control input actually sent to the coil is obtained by inverse mapping.

[0075] ,

[0076] Finally, to avoid overload and severe Joule heating of the triaxial Helmholtz coil under high-frequency continuous control, while ensuring the Lyapunov stability of the closed-loop system, the above continuous control input was adjusted. and Implement discrete distribution.

[0077] Let the current instruction execution time be... Define control execution error Construct a joint trigger function that incorporates state error and flow field abrupt change rate:

[0078] ,

[0079] in,

[0080] Real-time monitoring trigger function of the host computer computing platform The value of . When This indicates that the pose error is too large or the spatiotemporal observer has captured a drastic change in the flow field, triggering a system state update. and the newly calculated control commands at the current moment and Converted into a current signal and sent to the coil driver; when At this time, the controller remains silent, and the external actuator maintains the control command from the previous trigger moment.

[0081] like Figure 2As shown, the collaborative control system is a closed-loop control system. Its input is the desired state, and its output is the actual state of the microrobot swarm. The actual state is fed back to the controller and observer to achieve collaborative control of the swarm's center of mass motion and swarm morphological evolution. The desired state includes the desired center of mass position. and expected shape covariance matrix The actual state includes the actual position of the centroid. and the actual form covariance matrix .

[0082] The cooperative control system comprises a trajectory-morphology cooperative controller, a spatiotemporal flow field disturbance observer, an adaptive event triggering mechanism, and a microrobot swarm model. The trajectory-morphology cooperative controller includes a centroid translation sliding mode controller and a swarm morphology controller. The centroid translation sliding mode controller is used to determine the desired centroid position. Actual centroid location and the generation of the first control input from the disturbance observations. To regulate the macroscopic translational motion of the microrobot swarm; the swarm morphology controller is used to adjust the macroscopic translational motion of the swarm based on the desired morphology covariance matrix. Covariance matrix of actual form The error between them generates a second control input. This is to adjust the density and topology of the microrobot swarm. First control input. With the second control input Together they constitute the collaborative control input of the microrobot cluster.

[0083] The spatiotemporal flow field disturbance observer is connected to the trajectory-morphology co-controller to receive actual state feedback information from the microrobot swarm. It combines this information with geometric and topological boundary features in the execution space and pre-constructed multiphysics prior information to perform online estimation of the flow field disturbances experienced by the system, outputting disturbance observation values. The disturbance observations are input to the trajectory-morphology co-controller for feedforward compensation in the control law, thereby improving the system's disturbance rejection performance and trajectory tracking accuracy in complex time-varying flow field environments.

[0084] An adaptive event triggering mechanism is set between the trajectory-morphology cooperative controller and the microrobot swarm model to determine the timing of control input updates. When the joint triggering function meets the triggering conditions, the adaptive event triggering mechanism will trigger at the specified time. Output discrete control commands and To the micro-robot cluster model; when the joint triggering function does not meet the triggering conditions, the system retains the control command from the previous triggering moment. Through the above settings, the control input update frequency can be reduced, the high-frequency switching of external electromagnetic actuators can be decreased, thereby reducing system energy consumption and coil heating.

[0085] The microrobot swarm model is used to characterize the dynamic response process of a microrobot swarm under external control input. The microrobot swarm model receives discrete control commands. and Then, output the actual centroid position of the microrobot cluster. and the actual form covariance matrix Actual centroid location and the actual form covariance matrix On the one hand, the feedback is sent to the trajectory-morphology cooperative controller to correct the centroid tracking error and morphology evolution error; on the other hand, it is sent to the spatiotemporal flow field disturbance observer to update the disturbance estimation results, thus forming a closed-loop control loop of "state feedback - disturbance observation - event triggering - control execution" to achieve cooperative and stable control of the morphology and trajectory of the microrobot cluster.

[0086] Figure 2 The composition modules of the collaborative control system of the present invention, the connection relationship between the modules, the control signal transmission path, and the actual state feedback path are clearly shown.

[0087] Implementation Method 2

[0088] This embodiment provides a morphology and trajectory collaborative control system for a magnetic microrobot swarm. The control system uses a morphology and trajectory collaborative control method for a magnetic microrobot swarm as described in Embodiment 1. The control system includes:

[0089] Model building module: Constructs a state-space evolution model of the centroid and morphology of a microrobot cluster;

[0090] Spatiotemporal flow field perturbation observer design module: Based on the model provided by the model building module, design a spatiotemporal flow field perturbation observer that integrates multi-physics priors;

[0091] Center of mass and shape collaborative controller design module: Based on spatiotemporal flow field disturbance compensation, a center of mass and shape collaborative controller based on event triggering mechanism is designed to realize the coordinated control of shape and trajectory of magnetic microrobot clusters.

[0092] Furthermore, the model construction module consists of establishing a joint nonlinear dynamic model that considers non-Newtonian fluid drag, time-varying flow field fluctuations, and internal magnetic dipole coupling force, using the centroid pose vector of the microrobot cluster and the morphological covariance matrix representing the cluster density distribution as joint state variables of the system. Based on non-singular perturbation theory, the joint nonlinear dynamic model is decoupled into a macroscopic centroid translation subsystem and a microscopic morphological evolution subsystem, and the unmodeled fluid dynamic characteristics are defined as the lumped perturbation term of the system.

[0093] Furthermore, the spatiotemporal flow field disturbance observer design module specifically involves: acquiring the current real-time state information of the controlled object and the geometric topological boundary features of the execution space at the current moment using a microscopic imaging device; introducing a velocity gradient prediction term into the dynamic update law of a traditional nonlinear observer using a pre-constructed multiphysics fluid dynamics prior mapping matrix to construct a spatiotemporal flow field disturbance observer; and using the spatiotemporal flow field disturbance observer, combining real-time state feedback and physical priors to calculate and output the high-precision observation value of the lumped disturbance term in real time.

[0094] Furthermore, the centroid and morphology co-controller design module specifically involves designing an adaptive sliding mode control law based on feedforward compensation of the lumped disturbance observations for the macroscopic centroid translation subsystem, and calculating the first control input (low-frequency gradient magnetic field force) for stabilizing trajectory tracking error; and designing an auxiliary morphology control law based on the morphology evolution error calculated according to the desired spatial geometric constraints for the microscopic morphology evolution subsystem, and calculating the second control input (high-frequency oscillating magnetic field parameters) for adjusting the cluster density state.

[0095] Furthermore, an adaptive event triggering mechanism is constructed, defining a joint triggering function that includes system state tracking error and fluid disturbance change rate; only when the value of the joint triggering function exceeds the dynamic safety threshold, the first control input and the second control input are updated and sent to the external electromagnetic actuator; in the non-triggered state, the electromagnetic actuator maintains the control input at the previous triggering moment to complete the collaborative control of the micro-robot cluster.

Claims

1. A method for coordinated morphological and trajectory control of a magnetic microrobot swarm, characterized in that, The control method includes the following steps: Step 1: Establish a morphological dynamic model of the center of mass of the microrobot cluster, and define the joint state variables of the system, taking into account fluid resistance, pulsating flow, and complex boundaries; Step 2: Based on the model in Step 1, design a spatiotemporal flow field disturbance observer to compensate for the external disturbance forces experienced by the microrobot swarm during its movement. Step 2 specifically involves: acquiring the current real-time state information of the controlled object and the geometric topological boundary features of the execution space at the current moment using a microscopic imaging device; introducing a velocity gradient prediction term into the dynamic update law of a traditional nonlinear observer using a pre-constructed multiphysics fluid dynamics prior mapping matrix to construct a spatiotemporal flow field disturbance observer; and using the spatiotemporal flow field disturbance observer, combined with real-time state feedback and physical priors, calculating and outputting high-precision observation values ​​of the lumped disturbance term in real time. Step 3: Based on the disturbance compensation in Step 2, design a centroid-morphology cooperative controller based on an event triggering mechanism, construct a joint triggering function for discretization update and issue control commands to realize the morphology and trajectory cooperative control of the magnetic microrobot cluster.

2. The morphology and trajectory coordinated control method according to claim 1, characterized in that, Specifically, step 1 involves using the centroid pose vector of the microrobot cluster and the morphological covariance matrix representing the density distribution of the cluster as joint state variables of the system, and establishing a joint nonlinear dynamic model that considers non-Newtonian fluid drag, time-varying flow field fluctuations, and internal magnetic dipole coupling forces. Based on the nonsingular perturbation theory, the joint nonlinear dynamic model is decoupled into a macroscopic centroid translation subsystem and a microscopic morphological evolution subsystem, and the unmodeled hydrodynamic characteristics are defined as the lumped perturbation term of the system.

3. The morphology and trajectory coordinated control method according to claim 1, characterized in that, Specifically, step 3 involves designing an adaptive sliding mode control law based on feedforward compensation of lumped disturbance observations for the macroscopic centroid translation subsystem, and calculating the first control input for stabilizing trajectory tracking error. For the micromorphological evolution subsystem, the morphological evolution error is calculated based on the desired spatial geometric constraints, an auxiliary morphological control law is designed, and a second control input for adjusting the density of the cluster is calculated.

4. The morphology and trajectory coordinated control method according to claim 3, characterized in that, An adaptive event triggering mechanism is constructed, defining a joint triggering function that includes system state tracking error and fluid disturbance change rate; only when the value of the joint triggering function exceeds the dynamic safety threshold, the first control input and the second control input are updated and sent to the external electromagnetic actuator; in the non-triggered state, the electromagnetic actuator maintains the control input at the previous triggering moment to complete the collaborative control of the micro-robot cluster.

5. A morphology and trajectory collaborative control system for a magnetic microrobot swarm, characterized in that, The control system uses the morphology and trajectory collaborative control method for a magnetic microrobot swarm as described in any one of claims 1-4, and the control system includes: Model building module: Establish a morphological dynamic model of the center of mass of the microrobot cluster, define the joint state variables of the system, and consider fluid resistance, pulsating flow, and complex boundaries; Spatiotemporal flow field disturbance observer design module: Based on the model provided by the model building module, design a spatiotemporal flow field disturbance observer to compensate for external disturbance forces experienced by the microrobot swarm during its movement; The spatiotemporal flow field disturbance observer design module specifically involves: acquiring the current real-time state information of the controlled object and the geometric and topological boundary features of the execution space at the current moment using a microscopic imaging device; introducing a velocity gradient prediction term into the dynamic update law of a traditional nonlinear observer using a pre-constructed multiphysics fluid dynamics prior mapping matrix to construct a spatiotemporal flow field disturbance observer; and using the spatiotemporal flow field disturbance observer, combining real-time state feedback and physical priors to calculate and output the high-precision observation value of the lumped disturbance term in real time. The design module for the centroid-morphology collaborative controller: Based on spatiotemporal flow field disturbance compensation, a centroid-morphology collaborative controller based on an event triggering mechanism is designed. The joint triggering function is discretized and updated to issue control commands, thereby realizing the collaborative control of the morphology and trajectory of the magnetic microrobot cluster.

6. The morphology and trajectory coordinated control system according to claim 5, characterized in that, The model building module specifically establishes a joint nonlinear dynamic model that considers non-Newtonian fluid drag force, time-varying flow field fluctuations, and internal magnetic dipole coupling force by using the centroid pose vector of the microrobot cluster and the morphological covariance matrix representing the density distribution of the cluster as joint state variables of the system. Based on the nonsingular perturbation theory, the joint nonlinear dynamic model is decoupled into a macroscopic centroid translation subsystem and a microscopic morphological evolution subsystem, and the unmodeled hydrodynamic characteristics are defined as the lumped perturbation term of the system.

7. The morphology and trajectory coordinated control system according to claim 5, characterized in that, Specifically, the centroid and morphology co-controller design module designs an adaptive sliding mode control law based on the feedforward compensation of the lumped disturbance observation for the macroscopic centroid translation subsystem, and calculates the first control input for stabilizing trajectory tracking error. For the micromorphological evolution subsystem, the morphological evolution error is calculated based on the desired spatial geometric constraints, an auxiliary morphological control law is designed, and a second control input for adjusting the density of the cluster is calculated.

8. The morphology and trajectory coordinated control system according to claim 5, characterized in that, An adaptive event triggering mechanism is constructed, defining a joint triggering function that includes system state tracking error and fluid disturbance change rate; only when the value of the joint triggering function exceeds the dynamic safety threshold, the first control input and the second control input are updated and sent to the external electromagnetic actuator; in the non-triggered state, the electromagnetic actuator maintains the control input at the previous triggering moment to complete the collaborative control of the micro-robot cluster.

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