115 results about "Neural network controller" patented technology

The invention relates to the field of power plant control, discloses a virtual power plant control method, system and equipment based on a neural network, and is used for solving the core problems of high data dependence, low topology safety and difficulty in multi-scale collaboration in traditional virtual power plant control. According to the virtual power plant control method based on the neural network, a correction instruction set, a joint estimation value and a topology constraint matrix are input into a neural network controller, and a cooperative control signal is output through singular perturbation decoupling of a fast-varying subsystem and a slow-varying subsystem. And the cooperative control signal is issued to the distributed power supply inverter, the energy storage converter and the intelligent switch, and meanwhile, an execution result is monitored in real time and fed back to the phase space reconstruction module, so that closed-loop control is formed. By constructing the Lyapunov candidate function and calculating the virtual damping coefficient, the transient stability, real-time persistent homologous analysis and topology self-healing instruction generation of the system are enhanced, and the self-healing capability and the fault-resistant capability of the system are improved.

The invention belongs to the technical field of laser welding, and discloses a real-time feedback control method and system for laser welding penetration stability, and the method comprises the steps: obtaining plasma electron temperature characteristics in a laser welding process in real time through a spectrum monitoring system, and enabling the plasma electron temperature characteristics to be associated with penetration fluctuation as a core input signal of feedback control. The signal has better real-time performance and more accurate feature extraction capability in real-time feedback control of laser welding by virtue of broadband coverage and multi-dimensional information acquisition capability of the signal in combination with a more efficient data analysis mode. A parallel type self-learning fuzzy neural network controller is used for executing real-time feedback control output of laser welding penetration fluctuation. On a control architecture, a traditional PD controller and a fuzzy neural network are connected in parallel and are respectively used as a PD control module and a fuzzy neural network control module. And the process database is embedded into the forepart structure of the neural network control module in a fuzzy rule form.

An adaptive impedance-based multi-mobile-robot collaborative transportation control method. Each mobile robot estimates the actual pose and ideal pose of a reference point and the first and second derivatives of the ideal pose by means of two finite-time fully-distributed observers, respectively; and then, on the basis of the estimated poses of the reference point, the pose of an end-effector of a mechanical arm, and closed-chain constraints for collaborative transportation, an ideal trajectory of the end-effector of the mobile robot, and an estimated value of a pose deviation between the end-effector of the mobile robot and the reference point are obtained. An adaptive impedance system of each mobile robot is interconnected with a virtual energy tank, and the energy tank is used to guide the updating of impedance parameters, thereby ensuring the passivity of the entire collaborative adaptive impedance system. To process unknown system dynamics of mobile robots, an asymptotic tracking adaptive neural network controller is designed using a neural network, thereby asymptotically achieving an ideal adaptive impedance relationship. The operational accuracy of multi-robot collaborative transportation systems is improved while ensuring safe collaboration.

The invention provides an exoskeleton robot self-adaptive control method and related equipment, and relates to the technical field of exoskeleton robots. The method comprises the following steps: acquiring motion information and position information of each movable joint in the exoskeleton robot; inputting the motion information and the position information into a pre-trained angle prediction model to obtain a target angle of each movable joint; the difference value between the target angle and the actual angle output by the exoskeleton controller serves as an angle error; constructing a virtual control quantity based on the angle error; the virtual control quantity is combined with motion parameters of the exoskeleton robot to be input into a disturbance observer, and estimated disturbance is generated; the angle error, the virtual control quantity and the estimated disturbance are input into an adaptive neural network controller, and the control torque of each movable joint is obtained; and based on the control torque of each movable joint, generating a control signal of each movable joint. The method and the device have good control precision.

The invention provides an ultrasonic sensitivity detection method based on a large workpiece, relates to the technical field of detection, and aims to solve the problems of inconsistent sensitivity in a full thickness range and insufficient deep defect detection precision of traditional ultrasonic detection. According to the method, multiple groups of depth-adaptive equivalent reflectors are arranged on a test block, and a gain value is dynamically adjusted in combination with a fuzzy neural network controller, so that sensitivity adaptive compensation in a full thickness range is realized, and a detection error is ensured to be stabilized within + / -1%; according to the dynamic reflector interval design based on the material attenuation coefficient and the acoustic parameter, the reflector distribution is optimized, and the manual calibration complexity is remarkably reduced. And fitting a TCG curve through a segmented weighted least square method and carrying out simulation verification to generate a high-precision DAC curve, so that high-confidence output of quantitative defect evaluation is realized. The method improves the sensitivity, accuracy and engineering applicability of ultrasonic detection of large workpieces, and is especially suitable for defect detection of workpieces with complex curvatures.

The invention relates to a tractor clutch test bench cooperative control method based on MPC-BP neural network PID, and belongs to the technical field of tractor clutch performance testing. The method specifically comprises the steps of establishing an enhanced mathematical model of a tractor clutch test bed system; an MPC prediction controller is designed, and an optimal control sequence is generated through rolling optimization of an objective function; a BP neural network PID controller is improved; a multi-unit cooperative control strategy is designed, MPC prediction and BP neural network PID control are combined, and cooperative adjustment of the torque of the loading unit, the rotating speed of the driving unit and the displacement of the clutch separation mechanism unit is achieved; a self-adaptive adjustment and load compensation mechanism is introduced, and the adaptability of the system to different working conditions and clutch models is improved through online model correction, heat fading modeling and fault diagnosis. The control precision, the dynamic response speed and the robustness of the test bed are remarkably improved, and the test bed can be widely applied to research, development and performance testing of the tractor clutch.

The invention discloses an asymptotic tracking control method for a mobile double-flexible mechanical arm network. The method comprises the following steps: constructing the mobile double-flexible mechanical arm network; the uncertainty of a leader is considered, and a self-adaptive distributed switching observer is constructed; a servo system is introduced, transverse displacement is generated, and a tracking error model is constructed; an adaptive neural network controller is constructed by considering unknown gain faults and parameter uncertainty; on the basis of a distributed switching observer and a neural network controller, asymptotic consistency tracking control over a mobile double-flexible-mechanical-arm network is achieved. According to the method, asymptotic fault-tolerant consistency tracking control of the mobile double-flexible mechanical arm network can be effectively realized under heterogeneous linear leader and denial of service attacks, and the problems of unknown gain faults and unknown parameters are solved by utilizing a self-adaptive method and a neural network technology; the moving position of the moving double-flexible mechanical arm and the angle position of the two flexible mechanical arms reach the specified transient performance, and asymptotic consistency tracking is achieved.

The invention discloses a novel device and method for intelligently disassembling a waste photovoltaic silicon cell panel. The device comprises two layers of transmission rollers and a conveying belt penetrating through the two layers of transmission rollers. The main control board is integrated with a fuzzy controller and a PID (Proportion Integration Differentiation) controller, a hierarchical decision algorithm and a BP (Back Propagation) neural network are deployed on the main control board, the PID controller is connected with the fuzzy controller, and the BP neural network is used for dynamically adjusting PID parameters of the PID controller; the support is fixedly provided with a six-axis servo motor, a back plate collecting bin, an air cooling fan and a copper solder strip absorbing device, the six-axis servo motor is fixedly connected with a vacuum suction cup array, the air cooling fan is fixedly provided with an infrared imager, and the support is provided with a double-layer heat cutter head. A resistance heating module and an electromagnetic induction heating module are nested on the outer surface of the double-layer hot cutter head, and a pressure sensor, a laser sensor and a temperature sensor which are fixedly connected with the main control board are fixedly arranged on the outer surface of the double-layer hot cutter head. According to the invention, the waste photovoltaic silicon cell panel can be intelligently and accurately segmented, the dependence on manpower is reduced, and high-quality recovery of the silicon cell panel is realized.

The invention provides a motion control method of a coupled output limited Mecanum wheel trolley based on a neural network and event triggering, a storage medium, equipment and a computer program product. The motion control method comprises the following steps: adopting a conversion matrix of Mecanum wheels; establishing a dynamic model of the Mecanum wheel trolley; decoupling the coupling output limitation, and converting the coupling output limitation into time-varying non-coupling limitation; improving the design of a sliding mode surface by adopting an error conversion function and a barrier function, and constructing a sliding mode controller of a trolley with limited coupling output; constructing a neural network controller; introducing an event triggering mechanism, and constructing a neural network sliding mode controller for the coupled output limited trolley based on the neural network and sliding mode control; and verifying the stability of the designed controller. According to the invention, the trajectory tracking problem of the Mecanum wheel trolley with limited coupling output based on the neural network and event triggering is solved.

The invention belongs to the technical field of lower limb rehabilitation, and discloses a man-machine coupling system intelligent cooperative control method and device based on deterministic learning. Establishing a state-space equation of the lower limb exoskeleton robot, defining a tracking error, and designing a self-adaptive neural network controller of the lower limb exoskeleton robot; defining an expected position state vector and a real position state vector of the walking robot, solving a position state vector error, and designing a self-adaptive neural network controller of the walking robot; the method comprises the following steps: constructing a time-varying linear matrix, verifying the exponential stable convergence characteristic of the time-varying linear matrix under a continuous excitation condition on the basis of an adaptive neural network controller, storing nonlinear dynamic knowledge of a man-machine coupling system, and constructing a learning controller by using the learned knowledge. According to the method, while the unknown dynamic state of the man-machine coupling system is accurately modeled, the continuous excitation condition is ensured, so that accurate and stable trajectory tracking control is realized.

The invention provides a rotor unmanned aerial vehicle bidirectional thrust control method and device based on deep reinforcement learning, and the method comprises the steps: S01, constructing an unmanned aerial vehicle bidirectional thrust dynamics model which comprises an unmanned aerial vehicle dynamics model and a motor-blade model for achieving the bidirectional thrust control; s02, using a deep reinforcement learning model to construct a neural network controller for controlling the action of the unmanned aerial vehicle based on the unmanned aerial vehicle bidirectional thrust dynamical model, the input of the neural network controller being the difference between the current state and the target state of the unmanned aerial vehicle, and the output being the expected thrust of each motor of the unmanned aerial vehicle; and step S03, training the neural network controller, and using the trained neural network controller to control the unmanned aerial vehicle, so that the unmanned aerial vehicle stably hovers to a target state. The method has the advantages of being high in control precision, good in stability and high in adaptability, and stable hovering of the unmanned aerial vehicle in severe states such as a large posture, a large speed and a large angular speed is achieved.

The invention discloses a position-limited master-slave heterogeneous bilateral teleoperation control method based on an improved wave variable, a storage medium and a robot, and the method comprises the steps: building an improved wave variable algorithm framework, and adding a compensation wave to a slave input wave; a master robot task space reference trajectory and mapping of positions in a master-slave robot task space are constructed, and a master-slave robot joint space reference trajectory is solved based on a closed-loop inverse kinematics algorithm; based on a human operator, a far-end environment and master-slave robot characteristics, a combined robot dynamic model in a joint space is constructed; and aiming at the combined robot dynamic model, designing an adaptive neural network controller under limited joint positions based on a state transfer function. By means of the method, high-precision safety control over teleoperation of the master-slave heterogeneous robot with the limited position is effectively achieved, the stability and transparency of a teleoperation system are improved based on the designed improved wave variable algorithm, and a new safety operation method is provided for teleoperation control of the robot.

The present invention relates to a machine learning-based MMC model predictive control method and system. First, data is collected using an MPC-MMC simulation platform and preprocessed, and then neural network training is performed to obtain a neural network-MPC controller. To improve the neural network training efficiency, random forest is used to optimize the initial weight threshold of the neural network. Finally, a random forest-neural network-MPC controller is obtained to simulate the MPC controller. The results show that RF-NN-MPC is superior to NN-MPC in terms of learning efficiency and learning accuracy; while maintaining good control effects, MPC-MMC is not restricted by the number of sub-modules, and the online calculation amount is always 1 time. The calculation amount is greatly reduced, which is suitable for engineering applications.

The invention relates to the technical field of industrial automation control, in particular to a boiler combustion stability intelligent control method based on a self-adaptive neural network, and the method comprises the steps: collecting multi-source heterogeneous data of a boiler system, the multi-source heterogeneous data comprises a high-frequency vibration data flow and a low-frequency process data flow, carrying out space-time alignment preprocessing on the multi-source heterogeneous data; and calculating a self-adaptive penalty factor based on the current load characteristics and the vibration energy, and performing variational mode decomposition on the high-frequency vibration data stream by using the self-adaptive penalty factor to obtain a combustion characteristic component. According to the method, the gain of the neural network controller is dynamically adjusted through the comprehensive stability risk index, smooth switching of a control strategy between steady-state fine adjustment and fault strong control is achieved, and therefore the fast inhibition capacity for combustion instability is remarkably improved while the steady-state precision of a combustion system is guaranteed.

The invention provides a ship path planning and tracking control method based on an improved differential evolution algorithm, and the method comprises the steps: constructing a collision risk degree model, and improving the calculation precision of the collision risk degree through combining with parameters of a quaternary ship domain optimization model; determining a fitness function Fitness to evaluate the quality of the path points according to the constraints of the ship collision risk degree, the voyage, the steering angle, the international marine collision avoidance rule and the optimal collision avoidance distance; a self-adaptive cross factor dynamic adjustment strategy based on individual fitness and population statistical indexes is introduced, candidate path points are screened according to an improved differential evolution algorithm, and collision with obstacles and dynamic ships is avoided; and establishing a ship course motion mathematical model, taking the reference path as an input instruction of a ship motion control subsystem, and tracking a ship planning path by using an adaptive neural network controller. According to the invention, the ship collision avoidance path planning is more in line with the actual navigation, and the safety of the collision avoidance path and the accuracy of tracking control are improved.

The invention relates to the technical field of distributed rendering synchronization, and discloses a distributed rendering node display picture synchronization method. The method comprises the following steps: acquiring real-time display picture data and timestamp information of a plurality of nodes, extracting picture display delay characteristics of each node and determining a delay change period through time domain statistical analysis of timestamps, and further constructing a synchronous fluctuation degree of each node; calculating the current overall network interference degree by combining the relevance of the synchronization fluctuation degree in the historical data and the historical mean value, and analyzing the delay growth mode of the display timestamp in the current continuous time point sequence to obtain the synchronization lag trend degree; and combining the two to obtain a synchronization offset index, calculating a feedback synchronization adjustment amount by using the variation of the synchronization offset index, the current synchronization control intensity and a preset adjustment increment, and finally performing dynamic synchronization adjustment on a display image of the distributed rendering node based on the adjustment amount and an actual image synchronization measurement value by applying a neural network controller.

The invention provides a learning rate adaptive bearingless magnetic flux switching motor neural network PID suspension method, which is used for PID magnetic suspension control of a motor PID controller on a rotor, and adjusts a neural network weight coefficient in real time according to a rotor radial displacement control error so as to realize real-time adjustment of parameters of the neural network PID controller. Establishing a neural network PID parameter learning rate value range according to a neural network PID closed-loop control stability requirement; within a learning rate value range, designing a self-adaptive learning rate adjustment algorithm based on fuzzy reasoning for high-steady-state control precision and high-dynamic response of the dynamic eccentric magnetic suspension of the rotor in a wide range; the method can meet the requirements of magnetic suspension high-steady-state control precision and high dynamic response of wide rotor dynamic eccentricity.

The invention discloses an electric vehicle hybrid energy storage system energy management method fused with a Hemma particle swarm algorithm LSTM, and the method comprises the steps: constructing a multi-layer LSTM neural network controller to learn the characteristics of a driving condition, and minimizing the stress of a battery; constructing a fractional order integral derivative (FOID) controller, outputting a power correction component, and decomposing the component into a battery power reference component and a supercapacitor power reference component; according to the method, an improved He-Marx particle swarm algorithm is utilized, LSTM weight and FOID controller parameters are optimized in a combined mode through a multi-objective optimization function (including battery root-mean-square current and voltage errors), optimization training is carried out on the weight and bias of a neural network controller, and power distribution between a battery and a supercapacitor in the hybrid energy storage system of the electric vehicle is controlled in real time; an LSTM power distribution result is adaptively corrected through a dynamic weight mechanism, and bus voltage fluctuation is controlled within a range of + / -3%; and the final power reference value is synthesized through dynamic weighting, so that the peak current of the battery is remarkably reduced.

The method is a neural network-based online control method for a single-switch DC-DC converter, comprising: constructing a neural network model comprising a neural network training structure, a neural network training mode and a neural network training algorithm; constructing a neural network controller with the single-switch DC-DC converter as a control object and with a converter output voltage as a control target; collecting running state data and control data of the single-switch DC-DC converter in real time, obtaining gradients online, and realizing online training of the neural network and online control of the converter. The method does not need to model the converter, nor does it need a large number of data samples to perform offline training of the neural network, but realizes online acquisition of a control law (weights and biases) in the running process of the converter, achieves the purpose of real-time control, and the neural network controller can cope with the influence of step changes in input voltage or output load, has good robustness and dynamic response performance.

The invention belongs to the technical field of motor speed regulation control, and designs a permanent magnet synchronous motor speed regulation control method based on an improved cerebellum model neural network controller, a storage medium and equipment. Secondly, designing an improved cerebellum model neural network controller to be applied to a speed ring in a vector control structure of the permanent magnet synchronous motor, improving a weight adjustment mode and a learning rate of an original network by the controller, and updating the weight of the cerebellum model neural network by adopting a gradient descent method in combination with a reciprocal relationship of learning times; a calculation mode of dividing by average distribution of a network generalization parameter C is replaced, and the learning efficiency of the network is improved; meanwhile, the value of the network learning rate is dynamically adjusted according to the running state of the motor, and the rapidity and stability of the system are improved. The result shows that the improved cerebellum model neural network controller effectively improves the response speed of the system and greatly improves the dynamic stability of the permanent magnet synchronous motor.

This invention relates to the field of spacecraft orbit control technology, and particularly to a spacecraft approach control method based on the fusion of imitation learning and reinforcement learning. The method includes: S1: generating initial and target states, solving for the optimal fuel transfer trajectory offline using a direct method, and constructing an expert dataset; S2: based on the expert dataset, training a neural network controller through imitation learning to obtain a pre-trained model; S3: loading the weights of the pre-trained model, constructing a composite reward function, and training the policy network using a near-end policy optimization algorithm to obtain an optimized policy network model; S4: acquiring relative state data, optimizing the policy network model, and outputting the control quantity for the optimal fuel consumption trajectory transfer in real time. This invention rapidly masters an approximately optimal policy through imitation learning and surpasses the performance of expert data through reinforcement learning, completing control command solutions in milliseconds. It balances terminal control accuracy and fuel consumption optimization, offering advantages such as high solution efficiency, excellent control accuracy, and strong generalization ability.

The invention relates to a mechanical arm controller design method considering full-state constraint and disturbance suppression. The mechanical arm controller design method comprises the steps that firstly, a mechanical arm system state space model is established, and error variables are defined; 2, defining a tangent full-state constrained obstacle Lyapunov function, and carrying out virtual control design; 3, designing a known controller of the model, and introducing a Moore-Pengos inverse matrix and a lemma 1 to prove stability; and 4, designing a neural network controller, inhibiting bounded disturbance, and then verifying the stability of the neural network controller. According to the neural network controller designed by the method, the position error can be limited in the time-varying constraint boundary, and the speed error can be limited in the constant static constraint boundary, so that the purpose of limiting the tracking error in the predefined range can be realized.

The invention discloses a multi-valve equipment continuous variable mach number test control method and system, and belongs to the technical field of wind tunnel aerodynamic force tests. The invention aims to solve the problem of regulation and control strategies of test key parameters of multi-valve equipment. The method comprises the following steps: establishing an initial physical relationship model of valve opening and Mach number, and correcting based on a machine learning algorithm; a traditional PID controller, a fuzzy PID controller and a neural network PID controller are integrated, a simulation control system is constructed, and the output of the simulation control system is the valve opening degree; building a combined control system; defining multi-objective optimization parameters of the combined control system; optimizing the multi-objective optimization parameters by adopting a particle swarm algorithm or a genetic algorithm, and solving an optimal multi-objective optimization parameter set; and outputting a control result of the continuous variable Mach number test of the multi-valve equipment based on the multi-target optimization parameter set, and realizing adaptive control of the continuous variable Mach number test. According to the invention, adaptive regulation and control of key parameters are realized, the test efficiency, precision and safety are improved, and support is provided for verification of a combined power technology.

This invention provides an integrated method for pruning and controlling large-scale models of unmanned aerial vehicles (UAVs), belonging to the field of model lightweighting technology. The proposed method significantly reduces model size and computational load, greatly improves inference speed and control response performance, and enables the deployment of complex neural network controllers on resource-constrained airborne platforms. Simultaneously, the lightweight model maintains or approaches the control accuracy and stability of the original uncompressed model. This method has wide applicability and strong scalability, covering various scenarios from basic flight control to complex mission control, providing a feasible, efficient, and cost-effective solution for applying large models to airborne controllers of UAVs, and possesses high engineering practical value.

The utility model discloses a household medicine taking auxiliary device which comprises a neural network controller and a database connected with the neural network controller. The household medicine taking auxiliary device further comprises a base, a connecting rod, a shell, a touch display screen, a timing module, a voice broadcast module, a recognition camera and a medicine placing tray. Wherein the shell is arranged above the base, and the shell is connected with the base through a connecting rod; the neural network controller, the database, the timing module and the voice broadcast module are all arranged in the shell, the touch display screen is arranged on the side wall of the shell, and the recognition camera is arranged on the lower surface of the shell; the touch display screen, the timing module, the voice broadcast module and the recognition camera are electrically connected with the neural network controller; and the medicine placing tray is arranged on the base. The system is based on the neural network technology and is used for assisting the elderly at home to accurately take medicine on time.

The application provides a flexible joint robot arm tracking control method and device based on an adaptive neural network, and relates to the technical field of robot intelligent control. The method comprises the following steps: establishing a robot kinematics model, defining a trajectory tracking error, and designing a control input based on the robot kinematics model; adopting a neural network compensation model to compensate for model uncertainty, and adopting a disturbance observer to estimate unknown disturbance, so as to obtain an adaptive bounded neural network control based on state feedback; building a robot system platform to verify the feasibility and effectiveness of the method, and defining a Lyapunov function to prove the stability of the closed-loop system. The application constructs an adaptive bounded neural network controller, which can estimate the uncertainty in the model parameters and adjust the controller gain to adapt to the actuator limit, so as to ensure the effective trajectory tracking of the system. Subsequently, in order to further enhance the stability and robustness of the system, the application also designs a disturbance observer to estimate and compensate for unknown external disturbance.

To provide a decoder, an encoder, a neural network controller, and a method which allow efficient representation and transmission of neural network parameters for learning or updating processes.SOLUTION: A decoder for decoding parameters of a neural network is configured to: obtain (1010) a plurality of neural network parameters of the neural network on the basis of an encoded bitstream; obtain (1020), from an encoded bitstream, node information describing a node of a parameter update tree, the node information including a parent node identifier and parameter update information; and derive (1030) one or more neural network parameters using parameter information of a parent node identified by the parent node identifier and using the parameter update information.SELECTED DRAWING: Figure 10

The invention provides a method for generating a maneuvering instruction of a fixed-wing unmanned aerial vehicle, and the method comprises the steps: 1, carrying out the generation of a three-dimensional track, and forming a training data set; step 2, establishing a deep deterministic policy gradient algorithm (DDPG)-based actor network as a neural network of an off-line training network structure, and performing off-line training by using the training data set to obtain initial actor network parameters; step 3, establishing an unmanned aerial vehicle simulation model and a lower controller, establishing a corresponding network structure based on a depth deterministic strategy gradient algorithm DDPG, and designing different maneuvering trajectory lines for online training to obtain a neural network controller; and 4, deploying the neural network controller as an upper-layer controller in flight control, and performing combined control by combining with an overload controller and a rolling controller of a lower-layer controller. According to the method, the multi-task requirement of the unmanned aerial vehicle in a training scene is met, development time is shortened, and task adaptability is improved.

The invention provides a power system neural network controller design method and system, and the method comprises the steps: carrying out the dynamic modeling of a power system, and introducing and employing a Lyapunov stability theory; performing neural network control input and stability verification; and controller optimization and controller design are carried out. The technical problems that large-range nonlinear system behaviors cannot be processed due to the fact that the degree of dependence on system linearization is high, only stability of the system in a local area can be guaranteed due to the fact that the system attraction domain is small, and the adaptability to uncertainty and disturbance is weak are solved.

The invention belongs to the technical field of power system control, and particularly relates to a low-communication-cost neural network controller of a nonlinear power system and a design method of the low-communication-cost neural network controller. According to the method, the event-driven regulation and control algorithm is designed for the nonlinear power system by using a machine learning method, so that optimal triggering is realized while calm control is performed, and effective control under limited communication resources is realized. An event-driven mechanism is considered, that is, whether the control strategy is updated or not is determined according to the real-time state of the target system, so that the communication cost generated by updating the control signal is reduced; the optimal triggering problem in event-driven regulation is considered, that is, the event triggering frequency in specified time is minimized; according to the invention, two schemes of a direct method and an indirect method are designed to realize optimal trigger control under an event-driven mechanism; and a strict stability and optimality guarantee is provided for a control strategy obtained by neural network training by using an approximate projection method. Finally, the superiority of the technical scheme is verified by taking an industrial heat exchanger as an example.