260 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 model-free self-adaptive independent variable pitch control method and system for a wind turbine generator based on multivariable decoupling, and aims to solve the problem of blade fatigue damage caused by blade root asymmetric load under a high-wind-speed turbulence working condition. A multi-input multi-output control architecture is designed, control parameters are dynamically adjusted by using a model-free adaptive control (MFAC) algorithm, a cooperative adjustment instruction of an independent variable pitch angle is generated, optimal control of a three-blade pitch angle is realized, and power output stability is maintained while a blade root load is suppressed. Compared with traditional independent pitch control (IPC), the method has the advantages that model dependence is eliminated through pseudo-gradient matrix multivariable decoupling, the anti-interference capacity under the turbulent wind condition is remarkably enhanced, and technical guarantee is provided for safe operation of a large wind turbine generator.

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 belongs to the technical field of vertical mill gearboxes, and particularly discloses a vertical mill gearbox gear life online intelligent regulation and control method and system.The method comprises the steps that a main test gearbox gear rotating speed signal and a box body vibration acceleration signal are collected, whether a gear enters a degeneration stage or not is judged, and if the gear enters the degeneration stage, the gear is started; if yes, constructing a life prediction model and predicting the remaining life of the gear of the vertical mill gearbox by using the life prediction model; in combination with a vertical mill gearbox gear remaining life prediction result and the current expected remaining life, optimal regulation and control working condition parameters are calculated on the basis of a model-free adaptive regulation and control MFAC theory and by introducing a pseudo partial derivative PPD and a tight format dynamic linearization CFDL model; and based on the optimal regulation and control working condition parameters, the operation working condition of the vertical mill gearbox gear is regulated and controlled. By adopting the technical scheme, the remaining life of the gear of the vertical mill gearbox is predicted, the running state of the gear of the gearbox is regulated in real time, and the actual service life of the gear of the vertical mill gearbox is prolonged to be close to the expected service life.

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 discloses a multi-mode redundant model-free longitudinal control method for an automatic driving vehicle. The method comprises the following steps: collecting a longitudinal control signal source; establishing an off-line driving torque database and an off-line braking pressure database; designing a model-free vehicle speed control strategy, and calculating a vehicle speed expected driving torque and a vehicle speed expected braking pressure; designing a model-free acceleration control strategy, and calculating an expected acceleration driving torque; a model-free deceleration control strategy is designed, and deceleration expected brake pressure is calculated; and according to the vehicle speed expected driving torque, the vehicle speed expected braking pressure, the acceleration expected driving torque and the deceleration expected braking pressure, a model-free longitudinal control mode arbitration strategy is designed, the request driving torque and the request braking pressure are output, and longitudinal operation is achieved. According to the method, the stability problem of longitudinal control of the automatic driving vehicle can be solved under the condition that vehicle model parameters are missing or the model parameters are uncertain, and unexpected driving or braking caused by interference of factors such as program abnormity and communication congestion is prevented.

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 discloses an engineering system fault-tolerant tracking control method based on data driving, and belongs to the technical field of engineering control methods. Comprising the following steps: S1, establishing an engineering system equivalence model under a fault, and defining a performance index function and a corresponding value function so as to convert an optimal control problem into a zero-sum game problem; s2, aiming at the problem that the state of the engineering system is unmeasurable, reconstructing the state by utilizing historical input and output data so as to design and solve an optimal control strategy; and S3, introducing and defining a Q function, designing a controller gain and a disturbance gain # imgabs0 #, and optimizing the # imgabs1 # in combination with a designed model-free non-strategy Q-learning output feedback algorithm to obtain an optimal controller. Through an injection molding industrial process and a depth control simulation experiment of an autonomous underwater vehicle (AUV), convergence of the algorithm and an excellent tracking control effect of a designed controller are verified.

The invention belongs to the technical field of motor driving and control application, and provides a permanent magnet synchronous motor model-free and dead-beat predictive current control method based on current separation, and the main scheme is as follows: firstly, a permanent magnet synchronous motor current separation model is introduced, and the relationship between different parts in sampling current change is deduced based on the model; then, a model-free dead-beat control integral control law with extremely low parameter dependence is designed; furthermore, only a series of uncomplicated calculations need to be carried out in a control period, and more accurate predicted current and predicted voltage can be calculated without depending on motor parameters. Therefore, no observer is needed, and errors and oscillation possibly brought by the observer are avoided.

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 discloses a model-free adaptive control method based on UKF pseudo partial derivative estimation, and belongs to the technical field of automatic control. The method mainly solves the problems that an accurate mathematical model is difficult to establish in a nonlinear control system in the actual industry, and various uncertainties, complex unknown disturbances and the like influence the control accuracy. The control scheme comprises the following steps: designing a model-free adaptive controller based on input and output data; designing an unscented Kalman filter (UKF) to estimate a pseudo partial derivative; establishing a model-free adaptive control scheme based on UKF pseudo partial derivative estimation; according to the model-free adaptive control method based on UKF pseudo partial derivative estimation disclosed by the invention, better control precision and stronger anti-interference capability can be realized in an industrial nonlinear process under the influence of unknown disturbance, and the control precision requirement of industrial production is met.

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 provides a mechanical arm trajectory tracking control method and system combining data driving and model control. The method comprises the following steps: firstly, establishing a dynamic linearization model (PFDL) for a mechanical arm, and designing a model-free adaptive controller (MFAC); meanwhile, based on trajectory tracking errors, a sliding mode control (SMC) method is introduced to enhance the robustness and control performance of the system. According to the method, a double-delay deterministic strategy gradient algorithm (TD3) in reinforcement learning is creatively fused, and controller parameters are adaptively adjusted through online learning, so that the controller can dynamically find an optimal parameter combination under a fixed structure, and an optimal control effect is realized; according to the method, the dependence of traditional model driving on an accurate mathematical model is broken through, the method can adapt to variable environments and complex trajectories, the control precision and the error convergence speed are remarkably improved, and a new solution is provided for trajectory tracking control of the mechanical arm.

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.