213 results about "Model free" patented technology

Disclosed in the present invention is a networked multi-robot data-driven formation control method under a preview mechanism. A leader-follower formation method is used, wherein a preview point is determined on a reference trajectory, and by means of a cross-track error between a current position and the preview point, a model-free adaptive control (MFAC) algorithm is used to control an angular velocity; each follower uses the coordinates of a leader as the preview point, and the angular velocity and a linear velocity are respectively controlled by means of the cross-track error and an along-track error between the current position and the preview point; and in the algorithm, the angular velocity is controlled by single-closed-loop MFAC, the linear velocity is controlled by dual-closed-loop MFAC, an error term is controlled by weighted collaborative errors of each robot, forward prediction is used to compensate for network communication constraints in control algorithms, and a velocity control quantity after predictive compensation is transmitted to an end-device robot by means of an edge-end network, thereby realizing stable specific-pattern coordinated formation of multiple mobile robots. The method has stronger scenario applicability, flexibility and scalability.

The invention discloses a novel model-free super-spiral sliding mode control method based on a sliding mode disturbance observer, and the method comprises the steps: designing a novel model-free super-spiral sliding mode controller of a rotating speed ring based on the combination of a novel super-local model and a second-order super-spiral law; meanwhile, a sliding mode disturbance observer is designed to estimate uncertain and unknown disturbance parts of system parameters in real time and feed back to a novel model-free super-spiral sliding mode controller of a rotating speed ring, so that the rotating speed of the motor is controlled; the controller introduces a linear system state item and a nonlinear disturbance item according to the novel super-local model, establishes a novel super-local model of the system, combines the novel super-local model of the system with a rotating speed ring state equation of the motor, establishes a novel super-local model of a rotating speed ring, and performs the super-local model of the rotating speed ring; and establishing a novel model-free super-spiral sliding mode control law of the rotating speed ring based on the novel super-local model of the rotating speed ring and an observation result of the sliding mode disturbance observer. The influence of external disturbance on the motor is effectively solved, buffeting is weakened, and effective tracking of the given rotating speed can be achieved.

The invention discloses a method for generating a workpiece model based on point cloud data, and aims to automatically generate a high-precision workpiece model through reverse modeling. The method comprises the following steps: dynamically adjusting a data acquisition amount through a line structure light sensor, and obtaining workpiece surface point cloud data; preprocessing the point cloud, including three-dimensional effective area cutting, uniform downsampling and radius filtering to remove interference data and reduce data volume; carrying out clustering segmentation on the preprocessed data by utilizing a point cloud region growing method, separating a bottom surface part from a non-bottom surface part, extracting contour points of each plane region, calculating normal lines, generating vertexes by fitting contour edges and solving intersection points, and forcibly closing unclosed contours to construct complete geometric features; and finally, generating a workpiece model by combining the point, line and surface information packaged by the open source library. According to the method, the low-error and high-precision model can be directly generated for the model-free non-standard workpiece, limitation of manual modeling or model presetting is avoided, and programming time consumption is remarkably reduced.

The invention relates to the technical field of artificial intelligence and robot control, in particular to a reinforcement learning control method and system based on physical space feedback, and the method comprises the steps: training an initial strategy through domain randomization in a simulation environment; fusing multi-modal sensor data in a real environment, and constructing an environment state; deploying the strategy network after the strategy network parameters are optimized into a real environment, combining a model base and model-free reinforcement learning, utilizing simulation data and real data to jointly optimize the strategy network, and compensating a dynamical model error through online fine adjustment; and correcting the action instruction in real time based on the security constraint. The system comprises a sensor module, a strategy network module, a security constraint module and a simulation-real migration module. According to the method, the dynamical model error is compensated through online fine adjustment; through a mixed model base and a model-free reinforcement learning architecture, and in combination with multi-modal sensor data and a security constraint mechanism, high-sample-efficiency and high-security physical system control is realized.

The invention discloses a current ripple suppression method for an underwater propulsion motor, relates to the technical field of motor control, and reduces overshoot and adjustment time when the rotating speed of the motor changes suddenly by improving a traditional field-oriented control framework and adopting a differential tracker to smooth an input reference rotating speed. Dead-beat model-free predictive control and an ESO observer are introduced into a current loop, and current ripples caused by parameter mismatch and external interference are effectively restrained. Model-free predictive control is adopted, the number of mathematical model parameters is reduced, the model accuracy is improved, and the control precision is improved. The current ripple is further reduced by identifying the inductance parameter and adjusting the PWM frequency according to the actual inductance value. According to the invention, the overshoot phenomenon in the rotating speed adjusting process is obviously reduced, the stability and efficiency of motor operation are effectively improved, and the service life of the motor is prolonged. The invention provides a new solution for improving the performance of the underwater robot propulsion system.

The invention provides a permanent magnet motor continuous set model-free predictive control method based on Bayesian optimization, and relates to the field of permanent magnet synchronous motor control. The method comprises the following steps: acquiring a hyper-local model and an extended state observer; determining the bandwidth and the input gain as optimization variables, and obtaining a training set and a test set for Bayesian optimization; joint probability distribution of the training set and the test set is calculated through a kernel function, and a prediction mean value and a prediction variance of optimization variables are determined; an optimization variable combination is screened based on the expected value collection function, and the bandwidth and the input gain are updated based on the optimization variable combination; based on the updated extended state observer and the hyper-local model, calculating the reference voltage of the next control period by using a dead-beat control principle; the reference voltage is decomposed into basic voltage vectors through space vector modulation, and the switching sequence of the basic voltage vectors is executed according to the minimum switching times to drive the permanent magnet motor. The method is not affected by motor parameter changes, and the current prediction control steady-state performance can be improved.

The invention provides a novel model-free fast integration terminal sliding mode controller method for a permanent magnet synchronous motor based on an improved extended nonsingular terminal sliding mode disturbance observer, and the method is compared with a PI control method and a model-free sliding mode control method based on an extended sliding mode observer. The method can reduce the dependence of a controller on a specific mathematical model of a controlled system, is more suitable for nonlinear and strong coupling systems such as a permanent magnet synchronous motor, employs an improved extended nonsingular terminal sliding mode disturbance observer to estimate unknown total disturbance, and enhances the robustness and anti-interference capability of the method. The control method has high response speed and high control precision, and has a good fault-tolerant control function on the parameter perturbation of the motor, so that the permanent magnet synchronous motor can operate efficiently, stably and reliably under the parameter perturbation condition.

The invention discloses a model-free sliding mode single-loop control method for a permanent magnet synchronous motor. The method comprises the following steps: step 1, establishing a permanent magnet synchronous motor mathematical model under a d-q coordinate system; step 2, establishing a hyperlocal model of the permanent magnet synchronous motor; step 3, designing a system state equation, including establishing equations of state variables and matching disturbance, non-matching disturbance and q-axis stator voltage; 4, designing a finite time generalized proportional-integral observer for estimating matching disturbance and non-matching disturbance and estimating the change rate of the non-matching disturbance; and step 5, designing a single-loop model-free sliding mode speed controller to obtain the single-loop model-free sliding mode speed controller. According to the model-free sliding mode single-loop control method for the permanent magnet synchronous motor, the permanent magnet synchronous motor hyper-local model is established, the designed permanent magnet synchronous motor hyper-local model does not contain any motor parameter, the dependence on the motor parameter of the system is eliminated, and the anti-interference capability of the system is improved.

The invention relates to a bridge crane game model-free optimal control method based on event triggering, and the method comprises the steps: collecting a state vector of a bridge crane in real time, and building a nonlinear multi-player system model of the bridge crane; estimating an unknown dynamic function and an input gain matrix on line in the input identifier neural network; according to an event triggering mechanism, whether triggering is conducted or not is judged based on the error between the current state vector and the state vector of the recently-triggered sampling; if so, triggering a dynamic updating instruction; under a non-zero sum game framework, obtaining an optimal value function gradient based on an estimation result and a current state vector through a self-adaptive evaluator network, and generating an event triggering optimal control law of each player based on a sampled state vector; and the event triggering optimal control law is processed by a zero-order retainer and then is output as a physical driving signal to control the operation of the bridge crane. Compared with the prior art, the method has the advantages of high applicability, high disturbance resistance, high accuracy and the like.

The invention relates to the technical field of unmanned aerial vehicle anti-interference control, and provides an anti-interference control method, system and device for a photovoltaic panel cleaning unmanned aerial vehicle, and a medium, and the method comprises the steps: constructing an unmanned aerial vehicle dynamic linearization control model based on the external unknown disturbance of the unmanned aerial vehicle and the dynamic characteristics of a four-rotor unmanned aerial vehicle; respectively carrying out system input control analysis and pseudo partial derivative estimated value control analysis by taking a balance tracking error and an input quantity change as a principle and taking a balance modeling error and a pseudo partial derivative estimated value change as a principle to obtain a corresponding model-free adaptive control law and a model-free adaptive parameter estimation law; and acquiring an external unknown disturbance monitoring quantity based on a preset disturbance observer, and generating a control signal in combination with the dynamic linearization control model of the unmanned aerial vehicle to perform attitude regulation and control. On the basis of model-free self-adaptive control scheme design considering unknown disturbance, the control modeling workload can be reduced, self-adaptive change adjustment can be achieved, the self-adaptive anti-interference and vibration suppression capacity is improved, and the attitude control precision is effectively improved.

The invention relates to the technical field of drilling, and provides a roofbolter feedforward-feedback model-free adaptive tracking control method and system based on rotation force. According to the method, a valve-controlled hydraulic propulsion control model for driving a hydraulic cylinder and a corresponding reversing valve in the working process of the hydraulic jumbolter is established, and a nonlinear autoregressive moving average model between the propulsion force in the working process of the hydraulic jumbolter and the output driving current of the reversing valve is obtained through the valve-controlled hydraulic propulsion control model; through a nonlinear autoregression moving average model of the hydraulic jumbolter, an output identification observer deployed with a data-driven adaptive control algorithm of the hydraulic jumbolter is established, and the output identification observer performs adaptive tracking control on the working state of the hydraulic jumbolter through the deployed data-driven adaptive control algorithm. The tracking control problem of the valve control electro-hydraulic servo unit under the complex working condition is effectively solved.

The invention discloses a permanent magnet synchronous motor model-free sliding mode control method based on a novel reaching law, belongs to the field of power electronics, and aims at solving the problems that traditional sliding mode control is large in buffeting and high in steady-state error and depends on an accurate model. According to the method, based on a model-free theory, a permanent magnet synchronous motor rotating speed ring super-local model is constructed, then a novel reaching law containing a dynamic parameter delta = e-x beta is designed, and the contradiction between buffeting suppression and quick response is solved; meanwhile, designing a novel sliding-mode observer, and estimating an unknown part g of the system to realize disturbance feed-forward compensation; and finally, a q-axis current reference value control law is obtained through derivation in combination with a sliding mode surface and a mechanical motion equation. The method can weaken the buffeting of the sliding mode, improves the anti-interference performance and the steady-state precision of the system, guarantees the finite time convergence of the state, and is suitable for the control of the permanent magnet synchronous motor in the fields of aerospace and new energy automobiles.

The invention particularly relates to a model-free AUV depth control method based on reinforcement learning. The method comprises the following steps: establishing an AUV kinetic model; based on an AUV dynamical model, establishing an interaction model of the AUV and the environment by adopting a Markov decision process, determining an action space variable and a state space variable of an AUV control system, and establishing an AUV depth control objective function and a sectional reward function; establishing a neural network model, respectively establishing a strategy network and an evaluation network, and performing model training on the control strategy by adopting a continuous PPO algorithm to obtain control strategy parameters of the AUV; the control strategy parameters of the AUV are used for realizing vertical plane depth-keeping control of the AUV; and through reinforcement learning, enabling the AUV to update the training network at each fixed step length in the set depth control training until convergence, and obtaining a final strategy network at a predetermined depth. The method has high real-time control capability and self-adaptive control characteristics, can effectively cope with environmental changes, and shows depth control robustness and depth control adaptability.

The invention discloses a specified time performance control method applied to model-free unmanned ship data driving. The method comprises the following steps: acquiring an error conversion function based on a constructed specified time performance function; driving a neural predictor based on the constructed unmanned ship data so as to realize estimation of model uncertain items and unknown control input gains, and obtaining an unmanned ship dynamic model; on the basis of the error conversion function, a model-free data driving virtual control law is constructed according to the unmanned ship dynamics model, so that a data driving controller is constructed; and designing a switching dynamic event triggering mechanism for switching the fixed threshold triggering mechanism and the dynamic event triggering mechanism according to the data driving controller, and realizing data-driven specified time performance control of the unmanned ship according to the switching dynamic event triggering mechanism. The problems that in the existing unmanned ship control process, the control performance of the underactuated unmanned ship cannot be effectively improved through data driving, it is ensured that the precision of reaching the specified track within the preset time is poor are solved, in addition, most of existing methods based on a neural network depend on a large number of sample training fixed models, and when facing new tasks or environment changes, the control performance of the underactuated unmanned ship cannot be effectively improved. The problem that the adaptability of a training model is limited exists.

The invention discloses a model-free predictive control harmonic and torque ripple suppression method for a permanent magnet synchronous motor. The method comprises the following steps: sampling physical information of the permanent magnet synchronous motor; performing Clarke transformation on the obtained three-phase current, and performing Park transformation by applying the sampled rotor position to obtain actual current under the dq axis; according to the obtained dq-axis actual current and the motor winding voltage, updating an ESO-based hyper-local model after forward Euler method discrete transformation; calculating a control voltage by applying a traditional dead-beat current control method through a disturbance update value obtained through calculation; and carrying out inverse Park conversion on the deadbeat control voltage obtained after calculation, then carrying out traditional SVPWM modulation to obtain a three-phase PWM waveform, and inputting the three-phase PWM waveform into a driver to control the permanent magnet synchronous motor. According to the method, current ripples and torque ripples of model-free predictive control of the permanent magnet synchronous motor can be remarkably reduced, high-frequency harmonics in the operation process of the motor can be remarkably reduced, and the calculation burden is reduced.

The invention discloses a model-free predictive current control method based on an improved observer, and belongs to the technical field of permanent magnet synchronous motor control. The control method comprises the following steps: firstly, by analyzing the frequency characteristic of ESO, selecting an extended state observer order for obtaining balance between the anti-interference capability and the noise suppression capability; secondly, designing a model-free predictive current loop controller, and calculating the observer gain through a bandwidth method; then, a judgment mechanism of a current loop controller is constructed, the mechanism judges whether the system is in a steady state or not at the moment according to the current deviation value in the current state, and therefore the observer order with the better control effect is selected. According to the model-free predictive current control method based on the improved observer, the anti-interference performance of the system and the noise suppression capability of the current are remarkably improved, it is guaranteed that the improved observer has rapid tracking response and excellent steady-state performance, and the robustness of the control system is enhanced.

The invention relates to a behavior decision-making method for simulating a hippocampus-prefrontal lobe memory planning playback mechanism in the technical field of artificial intelligence, and the method comprises the steps: constructing the closed-loop interaction of a hippocampus planning playback network and a prefrontal lobe strategy evaluation network, and fusing the meta-reinforcement learning and a model-based strategy optimization technology; the bionic neural mechanism realizes the unification of dynamic planning and strategy optimization, and realizes the dynamic coupling of experience playback and prospective simulation in the brain-like decision process, so that the robot can learn in planning. The method comprises the following steps: acquiring environment basic data by using a model element-free reinforcement learning method, planning a certain number of steps by using a model by using a strategy optimization method based on model element reinforcement learning and taking the current basic data as a planning starting point, and training the model by using a planning result. The problems that an existing robot algorithm is limited by the complex degree of an actual environment, sample collection is difficult and the like are solved, the learning ability is effectively improved, and therefore the application requirements of the mobile robot in diversified complex scenes are met.

The invention discloses a multi-robot model-free adaptive cooperative control method and system based on neurodynamics. The method comprises the following steps: establishing a kinematic model containing a to-be-estimated Jacobian matrix and an unknown interference term for each robot; synchronously estimating the matrix and the interference term in real time by using an online learning algorithm based on the measurable speed; a comprehensive error model coupling individual tracking errors (actual and expected trajectory deviation of a single robot) and collaborative errors (tracking error difference of adjacent robots) is constructed, and a closed-loop error transmission structure is formed; then designing a distributed motion controller, and calculating a joint control speed enabling a comprehensive error to be converged to zero by using a neurodynamics method in combination with an online learning result and a synchronization error; and finally, discretizing a related formula, generating an iterative update formula, and outputting instructions in each control period to drive the robot to move according to the iterative update formula. The method does not need an accurate model, is high in robustness, can ensure that multiple robots accurately track the trajectory and maintain a stable formation, and is good in system expandability.

The invention relates to the technical field of network security, in particular to a CPS model-free adaptive prediction control method and system under hybrid network attacks, and the method comprises the steps: constructing a data model of a nonlinear information physical system; performing dynamic linearization on the established data model to obtain a linearized data model; based on the linearized data model, constructing a model-free adaptive controller of the cyber-physical system under the non-periodic DoS attack and the random FDI attack, and designing a predictive control algorithm of the model-free adaptive controller; an evaluation index with a tracking error bounded is introduced, and security control is performed on the information physical system based on a designed predictive control algorithm, so that the output of the information physical system can still stably track a reference signal when receiving a hybrid network attack. According to the method, stable tracking of the reference signal by the system can still be ensured under the hybrid network attack, and the adaptability to the uncertainty and complexity of the system is improved.

The invention discloses a permanent magnet synchronous motor double-inertia system model-free fast integration terminal sliding mode control method and system based on an enhanced fast terminal sliding mode extended disturbance observer, and the adopted model-free fast integration terminal sliding mode control method based on the enhanced fast terminal sliding mode extended disturbance observer is used. Compared with traditional PI control and a model-free sliding-mode controller based on an extended sliding-mode observer, the method can effectively improve the response speed and control precision under parameter perturbation and external disturbance, reduces the dependence of the controller on a system model, is more suitable for nonlinear systems such as a permanent magnet synchronous motor double-inertia system, and has a wide application prospect. Meanwhile, the total disturbance of the system is estimated by adopting an enhanced fast terminal sliding mode extended disturbance observer, so that the robustness of the method is enhanced, and the anti-interference capability of the permanent magnet synchronous motor system is effectively improved; according to the method, current harmonics and torque ripples generated by parameter perturbation and external disturbance can be effectively suppressed, and the overall control performance of the double-inertia system is further improved.

The invention discloses a non-parametric model free-form surface fillet feature suppression method, which comprises the following steps of: calculating the extension length and the extension direction of a curved surface adjacent to a fillet feature according to geometric characteristics and topological connection of a fillet surface, and performing extension operation on an adjacent surface based on the length and the direction; boolean union operation is carried out according to the edge characteristics of the original adjacent surfaces and the multiple extension surfaces, the extension surfaces are segmented, and boundary curved surfaces and internal curved surfaces are removed; and combining the cutting curved surface and an unextended operation curved surface in the model by adopting Boolean union operation to generate a closed shell, and converting the closed shell into a solid model through materialization processing to realize fillet feature suppression of the non-parameterized model. According to the method, the problem of fillet feature suppression of the non-parameterized model during data interaction of a heterogeneous system during CAD / CAE / CAM cooperative work is solved, the CAE simulation grid quality and calculation efficiency are remarkably improved, and powerful support is provided for CAM process automation.

A batch process model-free output feedback control method with non-repetitive interference belongs to the technical field of industrial process control, and specifically comprises the following steps: step 1, giving a mathematical description for an optimal tracking control problem of a non-repetitive batch process; 2, designing an optimal control law based on output feedback; 3, a game Q function construction method based on two-dimensional output feedback in the intermittent process; 4, solving a two-dimensional output feedback problem of batch operation by utilizing reinforcement learning; 5, solving an optimal control strategy based on output feedback through iteration to enable the optimal control strategy to be finally converged to an ideal optimal value; according to the method, the problem that a traditional method for processing the complex characteristics of a two-dimensional system is too tedious is solved, through the output feedback control method based on reinforcement learning, the method does not depend on a system model, the influence on the control performance is reduced, and the control effect is greatly improved.

The invention provides a model-free prediction energy storage converter voltage control method based on a Kalman observer, and the method is characterized in that the method comprises the following specific steps: 1, constructing a super-local model of a dual-active bridge converter, and simplifying the system description through local linearization; 2, designing a Kalman observer to estimate and compensate lumped disturbance in real time, and combining dynamic matrix updating and gain adaptive adjustment; 3, designing a cost function to solve an optimal shift ratio, and realizing model-free prediction voltage control; according to the control method provided by the invention, the robustness of the converter under the conditions of model mismatch and working condition change is effectively improved, and meanwhile, a good dynamic response characteristic can be kept.

The invention discloses a control method of a space rope-driven mechanical arm based on model-free reinforcement learning, which comprises the following steps: S1, a simulation environment is established for the space rope-driven mechanical arm by utilizing multi-joint contact dynamics, the space rope-driven mechanical arm comprises a floating base and a rope-driven mechanical arm, and the floating base is connected with the rope-driven mechanical arm; the first end of the rope-driven mechanical arm is connected to the floating base, and the other end of the rope-driven mechanical arm is the tail end point of the space rope-driven mechanical arm. And S2, a reinforcement learning framework based on a dense reward function is constructed, the reward function is designed so as to control the space rope-driven mechanical arm, and the reward function is related to the distance between the tail end point of the space rope-driven mechanical arm at each moment and the target point. According to the control method of the space rope-driven mechanical arm based on model-free reinforcement learning, generalization of strategies can be improved, and the capacity of an intelligent agent for executing complex tasks is enhanced.

The invention discloses a model-free predictive control method and device for a permanent magnet synchronous motor, and the method comprises the steps: constructing a hyper-local model, carrying out the disturbance observation of the hyper-local model through an ESO observer, and generating a model-free current prediction controller; obtaining current and voltage vectors of a plurality of historical control periods, constructing a first cost function, and calculating a first descent gradient of the first cost function about an input gain value of the hyper-local model and a second descent gradient about a bandwidth value of the ESO observer; performing parameter updating based on the first descent gradient and the second descent gradient, and calculating a current prediction value at the (k + 1) th moment through the model-free current prediction controller after parameter updating; and constructing a second cost function, and based on the current predicted value at the (k + 1) th moment, selecting a switching state which enables the second cost function to be minimum to control the motor system. The method can get rid of dependence on motor parameters, and the prediction precision and control robustness of the control system are improved.

The invention provides a model-free predictive current control method and system for a permanent magnet synchronous motor, belongs to the field of permanent magnet synchronous motor control, and avoids dependence on flux linkage and resistance parameters by adaptively switching unknown and disturbance parts of a high-order observer estimation system. Carrying out inductance parameter identification through expansion Kalman filtering, and optimizing a noise variance matrix of Kalman filtering through a particle swarm algorithm; and enabling the dq-axis reference current and the dq-axis feedback current of the motor to pass through a permanent magnet synchronous motor model-free predictive controller based on the self-adaptive switching high-order observer to obtain a dq-axis reference voltage vector, and obtaining an inverter switching signal through a delay compensation module and an SVPWM (Space Vector Pulse Width Modulation) module so as to control the permanent magnet synchronous motor.

The invention relates to a locomotive adhesion control method and device based on model-free self-adaptive sliding mode control and a medium, and the method comprises the steps: collecting the traction torque and the wheel rotating speed of a locomotive at the current moment, and calculating the current creep speed; on the basis of the current creep speed and the adhesion coefficient obtained through the full-dimensional state observer, the optimal reference creep speed under the current rail surface condition is estimated online; the optimal reference creep speed is used as a tracking target, a model-free self-adaptive sliding mode controller constructed based on a partial format dynamic linearization data model and sliding mode control is utilized, and the traction torque adjusting amount is obtained through calculation; and the locomotive traction torque is adjusted in real time according to the traction torque adjusting quantity, so that the actual creep speed tracks the optimal reference creep speed, and the locomotive runs near the optimal adhesion point. Compared with the prior art, the method has the advantages of being high in practicability, improving user experience, being high in safety and the like.

The present invention provides a model-free control method for a human-in-the-loop multi-agent system under a denial of service attack, belonging to the field of multi-agent system control technology. This control method is based on human-in-the-loop technology and introduces external human experts to supervise the system to improve the security and reliability of the system; at the same time, the DoS attack between the intelligent agents is modeled as a time-varying switching topology, and a fully distributed designated time leader output observer is designed, which effectively reduces the adverse effects of the DoS attack jump on the observation effect and achieves the designated time convergence performance of the observation error; in addition, an optimization performance function is designed and a model-free Q learning algorithm is derived. This control method realizes model-free optimization control of a human-in-the-loop multi-agent system under the influence of DoS attacks, while taking into account the designated time convergence performance of the consistency error, effectively improving the control quality.

The present invention discloses a method for suppressing harmonics and torque ripple in model-free predictive control of a permanent magnet synchronous motor. The method comprises the following steps: sampling physical information of the permanent magnet synchronous motor; performing a Clarke transform on the obtained three-phase current, and then performing a Park transform on the sampled rotor position to obtain the actual current along the dq axes; updating an ESO-based hyperlocal model after forward Euler method discrete transformation based on the obtained actual current along the dq axes and the motor winding voltage; calculating a control voltage using a traditional deadbeat current control method based on the calculated disturbance update value; and performing an inverse Park transform on the calculated deadbeat control voltage, followed by traditional SVPWM modulation, to obtain a three-phase PWM waveform, which is input into a driver to control the permanent magnet synchronous motor. The method can significantly reduce current ripple and torque ripple in the model-free predictive control of the permanent magnet synchronous motor, significantly reduce high-frequency harmonics during motor operation, and reduce the computational burden.

The application belongs to the technical field of building structure damage identification, and particularly relates to a model-free detection method for lateral stiffness mutation position of a high-rise structure, comprising the following steps: step one, obtaining the displacement time history response of the same height interval measuring points in the same vertical line of the high-rise structure in the same horizontal direction; step two, calculating the second-order statistical moment of the relative displacement of each measuring point according to the time history response, and solving the lower-to-upper value corresponding to the statistical moment of the measuring point; step three, taking the height of each measuring point as the horizontal coordinate and the lower-to-upper value corresponding to the statistical moment of the measuring point as the vertical coordinate to draw a curve; and step four, observing the mutation of the curve to identify the lateral stiffness mutation position.