87 results about "Model free" patented technology

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.

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 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 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.

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 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.

The invention discloses a four-rotor aircraft data driving control method breaking through bandwidth constraint under hostile attack. The method specifically comprises the following steps: establishing a kinetic model of a four-rotor aircraft; converting the obtained kinetic model into an equivalent dynamic linearization equation by using a dynamic linearization method; designing a criterion function, and designing a data driving control method of the four-rotor aircraft; an event triggering mechanism is introduced, denial of service attack is considered, and then the final four-rotor aircraft safety event triggering data driving control method is obtained through arrangement of the obtained control method. According to the method, model-free adaptive control is applied to attitude control of the four-rotor aircraft, the advantages of PID are combined, malicious denial of service attacks are considered, an event triggering mechanism is introduced, the good flight quality of the aircraft is guaranteed, system bandwidth resources can be saved, and the working time of the aircraft is prolonged.

This invention relates to a model-free predictive repetitive control method and system for a dual-subspace virtual vector dual-three-phase permanent magnet synchronous motor. Based on the acquired operating parameters, the motor's vectors are decomposed into mutually orthogonal fundamental and harmonic subspaces, and independent hyperlocal models are constructed in both subspaces. A linear extended state observer is constructed in the fundamental subspace, and a repetitive extended state observer based on repetitive control is constructed in the harmonic subspace. Independent virtual voltage vector sets and decoupled virtual voltage vector sets are synthesized in the two subspaces, respectively. Reference voltage vectors for the two subspaces are calculated, and the optimal virtual voltage vector and optimal decoupled virtual voltage vector are selected. The optimal duty cycle is calculated for each subspace, and control signals are synthesized and applied to each arm of the inverter to drive the dual-three-phase permanent magnet synchronous motor. This invention achieves model-free control in a dual-subspace manner, and the control algorithm for the motor system's model parameters exhibits better robustness in the event of parameter mismatch.

The invention provides a novel model-free predictive current control method and system, belongs to the field of model-free predictive current control of an IPMSM, and provides a variable-speed double-power reaching law (VSDPRL) for solving the contradiction between rapid convergence and buffeting suppression of a traditional exponential reaching law (ERL). And then parameter deviation and unmodeled dynamics are uniformly regarded as lumped disturbance, dq-axis current and lumped disturbance are used as observation variables, a sliding-mode observer based on a variable-speed double-power reaching law is provided, and the steady-state performance of the motor running under parameter disturbance is improved to a certain extent. Furthermore, in order to enhance the robustness of the DPCC, a first-order hyper-local model is established, and a variable-speed sliding-mode observer combining the hyper-local model and a variable-speed double-power reaching law VSDPRL is designed, so that the steady-state performance of the motor under parameter disturbance is further improved, and the control robustness of the IPMSM system during parameter mismatch is remarkably improved.

The application discloses a mobile manipulator control method and device based on online learning and fuzzy neural dynamics, and relates to the technical field of mobile manipulator control. The method comprises the following steps: acquiring an actual pose of an end effector, a desired pose trajectory and joint speed; introducing an excitation signal formed after superimposing random noise, updating a current Jacobian matrix estimation value of the mobile manipulator; inputting each data into a fuzzy neural dynamic solver; the fuzzy neural dynamic solver is configured to take a quadratic form as an optimization objective, take a differential kinematics tracking equation as an equality constraint, and take a non-convex feasible region as an inequality constraint; and finally outputting a control signal of the joint speed obtained by solving the fuzzy neural dynamic solver to drive the mobile manipulator to move. The application solves the technical problems of low control precision of the mobile manipulator under the conditions of parameter uncertainty and non-convex constraints without an accurate prior model, and realizes high-precision model-free pose control.

This disclosure provides a method for controlling the speed of a gas turbine based on a model-free adaptive controller. The method includes: determining the predicted fuel quantity of the gas turbine based on the desired speed of the gas turbine; determining the actual speed of the gas turbine based on the predicted fuel quantity; inputting the error determined based on the actual speed and the desired speed into the model-free adaptive controller, and outputting a correction amount for the predicted fuel quantity so as to obtain the target fuel quantity of the gas turbine based on the correction amount; and correcting the actual speed of the gas turbine based on the target fuel quantity until the error between the actual speed and the desired speed of the gas turbine meets a preset threshold.

The invention discloses a three-phase grid-connected inverter and a model-free prediction control method thereof. The method comprises the following steps: constructing a first-order current super-local prediction model based on an inverter model; constructing an extended state observer based on the first-order current super-local prediction model, and performing current prediction and lumped disturbance prediction by using the extended state observer; calculating an output voltage reference value according to the current prediction value and the lumped disturbance prediction result; and constructing a cost function by comprehensively considering the candidate voltage vectors and the neutral point potential according to the output voltage reference value, screening from the candidate voltage vectors to obtain an optimal voltage vector by taking the minimum cost function as a target, and controlling the three-phase grid-connected inverter based on the switching state corresponding to the optimal voltage vector. According to the method, a model-free prediction framework is combined with disturbance compensation and a parameter adaptive mechanism, so that the influence of parameter mismatch is effectively overcome, the robustness is improved, and low-harmonic output, rapid dynamic response and stable midpoint potential control are realized.

The present invention relates to a read threshold optimization system and method. A controller optimizes read thresholds of a memory device using model-free regression. The controller performs a read operation on a cell using a read threshold voltage value. The controller measures a probability value for a plurality of read threshold voltage values and estimates a threshold voltage distribution curve based on the plurality of read threshold voltage values and the measured probability values using a set regression formula. The controller determines a read threshold voltage value corresponding to a set point on the threshold voltage distribution curve and performs a read operation on the cell using the read threshold voltage value.

This invention discloses a model-free adaptive control method for radio frequency (RF) power control, comprising: real-time acquisition and preprocessing of raw incident and reflected power; dynamic correction of the reference incident power using a piecewise function; calculation of the raw tracking error based on the reference incident power and the incident power, and calculation of the composite error signal by introducing a reflected power penalty term; estimation of the pseudo-gradient vector reflecting the instantaneous influence of each control quantity on the incident power using a projection algorithm based on historical input and output data; calculation of the increment of each control quantity based on the composite error signal and the pseudo-gradient vector; and calculation of the control quantity at the next moment based on the increment. This RF power control method does not require a precise mathematical model of the system; it achieves multi-input adaptive cooperative control by online estimation of the pseudo-gradient vector reflecting the influence of each actuator, effectively improving the system's control accuracy and response speed; and simultaneously achieving smooth and continuous protection of RF power.

This invention provides a model-free control system and method for permanent magnet synchronous motors (PMSMs) with neural network compensation for strong nonlinear disturbances. The system includes a real-time data acquisition module, a dynamic pseudo-partial derivative estimator, an adaptive neural network compensator, a control law synthesis module, and an inverse Park and SVPWM modulation module. The real-time data acquisition module acquires the actual speed of the PMSM; the dynamic pseudo-partial derivative estimator estimates the pseudo-partial derivatives of the system's dynamic behavior online; the adaptive neural network compensator outputs compensation control quantities; the control law synthesis module generates the final voltage control command; and the inverse Park and SVPWM modulation module drives the PMSM. This invention can quickly suppress external disturbances such as sudden load increases, improve control accuracy and anti-interference capability, thereby achieving higher speed tracking accuracy and smaller steady-state fluctuations.

This invention provides a model-free predictive current control (MFPCC) method for permanent magnet synchronous motors (PMSMs) with vector preselection, based on a hyperlocal model. Based on the hyperlocal model, this invention proposes a low-complexity multi-vector MFPCC. First, a hyperlocal model is used to replace the motor model in traditional MPC, reducing the impact of motor parameter mismatch. Then, to estimate the disturbance term in the hyperlocal model, a sliding diaphragm observer based on a variable-gain reaching law is designed. Finally, a three-vector algorithm with vector preselection is proposed. This preselection scheme evaluates the vector position based on the voltage gradient concept, directly obtaining the optimal voltage vector without traversing vectors and evaluating cost functions, thus reducing computation time.

This application provides a data-driven multivariable adaptive predictive control method for an adaptive cyclic engine, belonging to the field of aero-engine technology. The method includes: constructing an equivalent data model of the adaptive cyclic engine based on full-format dynamic linearization of the engine's input and output data to obtain multi-step forward output prediction equations; performing real-time estimation of the pseudo-gradient matrix in the prediction equations using autoregressive and projection algorithms to form a predictive control rolling optimization framework; embedding a model-free adaptive control algorithm based on single-step optimization into this framework to form a composite control strategy; designing control input performance indicators with the goal of minimizing tracking error to obtain optimal control parameters; and generating real-time optimal control parameters using the engine's real-time input and output data based on the composite control strategy to achieve precise multivariable coordinated control. This application improves the long-term prediction, constraint handling, and global optimization capabilities of the control system.

The invention discloses a model-free predictive current control method for a permanent magnet synchronous motor of a hybrid trigger mechanism. The model-free predictive current control method specifically comprises the following steps: step 1, establishing a mathematical model of the permanent magnet synchronous motor in a d-q plane; step 2, establishing a hyper-local model of the permanent magnet synchronous motor; 3, designing a second-order linear expansion state observer for estimating the current value of the permanent magnet synchronous motor; step 4, performing discretization processing on the state observer; step 5, designing an event trigger mechanism ESO; step 6, designing ESO of a hybrid trigger mechanism; step 7, acquiring a voltage vector of the current control period according to a hybrid trigger mechanism ESO; and step 8, according to the voltage vector determined in the step 7, controlling the on-off of a switching tube, and further controlling the d-q axis current actual value of the motor to change along with the reference value. According to the method, the problems of large current fluctuation and poor control effect caused by frequent updating of the estimation state in the existing motor model-free prediction current control are solved.

The invention discloses a control method of an intelligent sweeper, a storage medium, electronic equipment and the intelligent sweeper. The control method comprises the steps that operation parameters of the intelligent sweeper are acquired; on the basis of the operation parameters, control parameters of the intelligent sweeper are output through a model-free self-adaptive control law; and controlling the intelligent sweeper to advance according to the control parameters. According to the control method of the intelligent sweeper, a better control effect is achieved.