145 results about "Model free" patented technology

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 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 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 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 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.

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 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 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 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.

The present application relates to the field of drone control technology and discloses a method, system and medium for cooperative task allocation among multiple drones. The method comprises: S1, defining the problem of cooperative task allocation among multiple drones; S2, solving the optimal control strategy for the problem of cooperative task allocation among multiple drones; S3, solving the dual optimization problem based on the primal-dual theory; S4, solving the convex dual optimization problem using the Q-function and Schur Complementation theory transforms the convex dual optimization problem into a semidefinite programming problem. S5, based on the properties of matrix congruence, transforms the semidefinite programming problem into a model-free semidefinite programming problem. S6, using a solver, obtains the optimal control strategy. S7, by varying the weight coefficients and repeating S2-S6, obtains the optimal energy loss function bound. This application, based on the Q-learning method, requires only a small amount of data collection, not a precise dynamics model or extensive data, to obtain the optimal strategy for multi-UAV cooperative task allocation.

The invention discloses a pressure control method and system for a marine ammonia fuel supply system based on model-free self-adaptive control, and the method employs a model-free self-adaptive control scheme based on tight format dynamic linearization, and carries out the real-time collection of system pressure and valve control quantity data. And dynamically estimating a pseudo partial derivative phi (k) on line to capture system characteristics, and designing an adaptive control law to calculate a valve opening instruction according to the system characteristics. A dual-robust mechanism is formed by introducing an estimation weight factor mu and a control weight factor lambda, and noise interference and controlled quantity mutation are effectively suppressed. The method can automatically adapt to system dynamic change and external disturbance, high-precision and strong-robustness stable control over the ammonia fuel supply pressure is achieved under the complex ship working condition, and the safety and reliability of the ammonia power ship are remarkably improved.

The invention relates to a dual three-phase permanent magnet synchronous motor model-free prediction repetitive control method and system based on a double-subspace virtual vector, and the method comprises the steps: decomposing the vector of a motor into a fundamental wave subspace and a harmonic wave subspace which are orthogonal to each other based on an obtained operation parameter, and constructing an independent super-local model in the two subspaces; constructing a linear expansion state observer in the fundamental wave subspace, and constructing a repetitive expansion state observer based on repetitive control in the harmonic wave subspace; respectively synthesizing an independent virtual voltage vector set and a decoupling virtual voltage vector set in the two subspaces; and calculating reference voltage vectors of the two subspaces, selecting an optimal virtual voltage vector and an optimal decoupling virtual voltage vector, respectively calculating optimal duty ratios, synthesizing control signals applied to each bridge arm of the inverter, and driving the dual three-phase permanent magnet synchronous motor. According to the method, model-free control of double sub-spaces is realized, model parameters of a motor system of an algorithm are controlled, and better robustness is shown when the parameters are mismatched.

The application belongs to the technical field of power electronics, and discloses a double-vector model-free predictive control method for a reduced matrix converter and a related device, the method comprising: obtaining the AC side voltage, DC side voltage and DC side reference voltage data of the reduced matrix converter at a target moment, and calculating the inner loop reference current; obtaining the AC side current and switch input side current of the reduced matrix converter at an adjacent moment, calculating the system gain and system concentrated disturbance of the reduced matrix converter at the target moment, and combining the DC side current of the reduced matrix converter at the target moment to calculate the AC side current at the next moment corresponding to each switch state; and according to the inner loop reference current and the AC side current at the next moment, calculating the action time corresponding to the optimal vector and the suboptimal vector and determining the wave emission mode. The application realizes the model-free predictive control of the RMC, improves the anti-interference of the RMC, and reduces the AC side current ripple through the double-vector fixed-frequency control.

The invention relates to the technical field of electric power, and particularly discloses a model-free adaptive control method for interconnected power grid AGC, and the method comprises the steps: constructing an interconnected power grid AGC system mathematical model in which multiple types of units participate; the pseudo gradient of the interconnected power grid AGC system mathematical model is calculated by using real-time input-output measurement data, and dynamic linearization is carried out on the interconnected power grid AGC system mathematical model at the operation point of each discrete moment of the interconnected power grid; constructing a weighted one-step forward control law, and adding additional regulatory factors and control items in the control law; and pre-optimizing parameters of the constructed model-free adaptive control method through an intelligent optimization algorithm. The method has the advantages that the limitation that a traditional AGC method depends on an accurate power grid frequency response model is solved, and the power grid frequency stability and the efficiency and robustness of power exchange control of all areas of the interconnected power grid are improved.

The invention provides a second-order model-free predictive control method for a voltage source inverter, relates to the technical field of power electronic converter control, and solves the problem that model predictive control depends on model parameters, system parameters do not need to be considered, and only input and output of a system need to be considered. And the influence of parameter mismatch on a control system is greatly reduced. Therefore, the system has good steady-state performance, robust performance and anti-disturbance performance. The provided control scheme is economical, reliable and easy to implement.

The invention provides a model-free smooth transition control method for aero-engine mode switching, and belongs to the field of aero-engine control. Firstly, a design method based on linear active disturbance rejection control is introduced into an aero-engine control system and used for estimating and compensating unmodeled dynamic and external disturbance in the aero-engine control system in real time. And dividing the time scheduling linear active-disturbance-rejection controller into a transition controller and a steady-state controller according to the operation time interval. And by considering the actual operation time interval of the aero-engine control system, time scheduling parameters are introduced to dynamically adjust the maximum update times of the operation time interval of the time scheduling linear active disturbance rejection controller. And finally, for the switching dynamic error system of time scheduling, deducing a sufficient condition that the switching dynamic error system of time scheduling can keep asymptotically stable under the constraint of average residence time by performing stability analysis and designing that time and switching signals depend on a multi-discontinuous Lyapunov function.

The invention discloses a model-free predictive current control method for a permanent magnet fault-tolerant vernier rim propulsion motor, and the method comprises the steps: constructing a model-free predictive current control frame, selecting a parameter optimization design domain based on bandwidth, determining the input and output of an artificial neural network, carrying out the simulation to obtain an artificial neural network data set under different parameter combinations, and carrying out the calculation of the model-free predictive current control frame. Designing a two-input four-output artificial neural network structure; designing hyper-parameters including an activation function, a learning rate, a training round number, initialization and a data use mode; dividing a training set and a test set through five-fold cross validation; carrying out small-batch gradient descent back propagation; and predicting a control performance index with a smaller step length for a parameter optimization design domain, designing a fitness function and carrying out minimization optimization to obtain a parameter optimal combination, and substituting the optimal parameter to realize model-free predictive current control of the permanent magnet fault-tolerant vernier rim propulsion motor. According to the invention, parameter optimal design is facilitated, and control robustness is improved.

This invention relates to the field of intelligent control technology for industrial robots, and discloses a precise compliant force control method for a grinding and polishing robot under uncertain environmental conditions. The method includes establishing a dual-closed-loop control model of the environment and the robotic arm based on a set linear environment model and a robotic arm dynamics model; iteratively solving for the optimal control gain in a data-driven manner based on adaptive dynamic programming for the unknown environmental characteristics in the control model; designing a free-space control gain to achieve stable relative motion approaching the environment; designing a smooth transition mechanism for the free-contact space control gain; achieving model-free optimal control based on data iteration through adaptive dynamic programming; employing dual-closed-loop impedance control to achieve the impedance effect of the force; and utilizing contact transient impact modeling to dynamically adjust the control gain, ensuring the stability of the contact force and processing quality, making it suitable for various complex working environments.

To easily determine a design parameter suitable for a model free type controller.SOLUTION: The parameter determination device 20 includes the acquisition unit 221 that acquires a plurality of pieces of input data u to the control target P and a plurality of pieces of output data y output by the control target P in a predetermined period, and the parameter determination device 20 obtains a minimum value of a cost function by inputting the input data u and the output data y to the cost function indicating a difference between the input data u acquired by the acquisition unit 221 and a virtual input of the control system 10. The control system 10 includes the calculator 222 that calculates the control parameter of the feedback controller 13, and the determiner 223 that determines, as the design parameter used as the constant in the control system 10, one design parameter in which the difference between the signal levels of the first signal uest, the second signal uff, and the third signal ufb output by inputting the control parameter and the one design parameter to the control system 10 is included in a predetermined range.SELECTED DRAWING: Figure 5

The invention provides an intelligent operation and maintenance system for a high-speed railway overhead line system based on multi-source data fusion, belongs to the technical field of intelligent operation and maintenance of high-speed railways, and is suitable for scenes such as dropper arrangement optimization, tension detection, model-free robust control and low-noise structure design of the high-speed railway overhead line system. Accurate sensing, intelligent decision making and efficient operation and maintenance of the whole life cycle of the overhead line system can be achieved, technical positioning in the new-generation information technology field is met, locking state testing and self-adaptive adjustment are adopted, the tension measurement error is controlled within + / -1%, and the slippage problem is solved. The disturbance adaptive capacity is enhanced, the contact force standard deviation is reduced by 25% through model-free robust control, stable current collection is still kept under disturbance of the wind speed of 15 m / s, aerodynamic noise is reduced, and the A-weighted sound pressure level drop amplitude reaches 8.59 dB (A) when the wave-shaped cavity structure is 350 km / h.

The invention belongs to the technical field of robot intelligent control, and particularly relates to a mechanical arm tracking control method based on transferable depth incremental reinforcement learning, which effectively solves the nonlinear optimal tracking control problem of a mechanical arm in a model-free mode by constructing a transferable incremental reinforcement learning framework. The method comprises the following steps: firstly, constructing a 1-degree-of-freedom depth increment model by utilizing one-step forward data offline learning, and providing universal dynamic representation for a mechanical arm which is difficult to accurately model; furthermore, an asynchronous depth value network is designed for the one-degree-of-freedom mechanical arm, stable and rapid convergence value function approximation is achieved through a separation base layer and an adaptive layer, and a cross-mechanical-arm migration mechanism is established, so that a pre-training model and a network base layer on the one-degree-of-freedom mechanical arm can be directly migrated to a high-degree-of-freedom mechanical arm subsystem; and the system difference is compensated only by updating the adaptive layer online, so that the repeated training overhead is remarkably reduced, and meanwhile, rapid, robust and adaptive tracking control on the mechanical arms with different degrees of freedom is realized.

The application discloses a robot obstacle avoidance method based on model-based and model-free reinforcement learning, belongs to the field of robot navigation, and adds model-based reinforcement learning as a forward-looking module into the framework of model-free reinforcement learning, uses the model-based reinforcement learning to improve the sample data utilization efficiency of the model-free reinforcement learning, and avoids the robot from selecting actions that may cause the robot to be in a dangerous situation by deducing the interaction between the robot and the surrounding environment in a future period of time, uses the model-free reinforcement learning as a main decision algorithm, and reduces the influence caused by an inaccurate environment model; the obstacle avoidance strategy is trained in a two-dimensional simulation environment, the perception module is designed and built offline on a data set, the perception module with the output information in the form of pseudo laser radar data is obtained, and then the obstacle avoidance strategy network obtained in the two-dimensional simulation environment can be combined to realize obstacle avoidance in a real environment. The application improves the performance of the obstacle avoidance algorithm.

The invention discloses a motor control method and device, and the method comprises the steps: building a hyper-local model of a rotating speed ring, determining an expression of lumped uncertainty disturbance through the combination of a rotating speed expression of a motor, carrying out the algebraic identification processing of the expression, so as to determine an expression of a lumped uncertainty disturbance estimation value, and substituting the expression into the hyper-local model, and determining a model-free controller of the rotating speed loop, and inputting the actual rotating speed error and the actual q-axis current into the model-free controller to obtain a target control current for controlling the motor. According to the method, the part which can generate disturbance in the motor control process is concentrated into the lumped uncertainty disturbance, and parameter identification is carried out on the part to determine a lumped uncertainty disturbance estimated value which is not influenced by motor parameters and external load fluctuation, so that the influence of the disturbance on the motor during motor control can be effectively inhibited. Therefore, the anti-interference performance of the rotating speed ring is improved, and the stable operation of the motor is ensured.

The application discloses a kind of based on data-driven adaptive cycle engine multivariable sliding mode control method, belong to aero-engine control technical field, this method is based on a certain type of adaptive cycle engine, considering the influence of actuator, based on input, output data, the dynamic linearization data model of engine is established online;Three-variable model-free adaptive sliding mode controller based on sliding mode control algorithm is designed;Adaptive weight factor is introduced, the control conservative problem of controller when the controlled object parameter occurs large-scale change is solved, and the control effect is optimized;Finally, the algorithm designed is used to realize the multivariable steady control of adaptive cycle engine under different modes.The application can solve the problem that traditional sliding mode control of aero-engine depends on accurate mathematical model, ensure the control effect while having good real-time performance.

The application discloses an improved model-free current prediction control method and device and system based on a forgetting factor. The method does not need to acquire system parameters in advance, only needs to collect a load current, and then approximately calculates a current increment from historical current data, and then outputs an optimal inverter switch state from a minimized current value function, and meanwhile, a forgetting factor is introduced to weaken the adverse effect of a sampling error on the system, so that the model-free current prediction control is realized. On the basis of guaranteeing the realization of the parameter-free control, the forgetting factor is introduced to weaken the adverse effect of the sampling error on the system, the current quality is improved while the response speed is faster, and the robustness of the control is improved. The application is suitable for different types of power electronic topological structures, and does not need to separately perform mathematical modeling on different types of power electronic topological structures, has a reference significance for the model-free prediction current control of the power electronic converter, and has a wide application prospect.

This invention discloses a dual-prediction collaborative safety control method and system for shaping energetic materials. The method includes: real-time acquisition of multi-source parameters; extraction of time-series features based on a sliding window mechanism to construct a dynamic temperature prediction model and output the predicted value of the maximum cutting temperature; simultaneously, correction of manual quality scoring data using a large language model to construct a dynamic quality prediction model and output the machining quality score; inputting the dual prediction results into an adaptive control flow; combining a dynamic linear time-varying model and a sequential quadratic programming algorithm to achieve dual-objective collaborative optimization of maximizing quality and temperature safety constraints to generate control commands; and finally, dynamically adjusting the safety threshold and executing a hierarchical response strategy based on tool wear state and material phase transformation characteristics. This method innovatively integrates time-series temperature prediction and large language model quality assessment through the collaborative optimization of temperature and quality dual prediction models, constructing a model-independent adaptive control framework, effectively preventing thermal runaway, improving prediction accuracy, and increasing system efficiency.