140 results about "Event triggered" patented technology

The present invention belongs to the field of smart power grids and power system automation control. Disclosed is a load emergency control method based on a dynamic event triggering mechanism, which method comprises: on the basis of an indicator of safe operation of a system, performing region division on a distribution network; proposing an adaptive load level division method based on user response willingness, and by taking the minimization of the system operation cost of the distribution network as an objective, establishing a precise load control model of the distribution network; and using a dynamic event triggering mechanism to reduce communication between substations and a master station of the distribution network, so as to realize effective exchange and precise execution of load control policy information. The method of the present invention can precisely realize precise control of a load, and can effectively alleviate communication transmission pressure of a distribution network, thereby ensuring the stability of a power grid and the continuous supply of critical loads.

The invention discloses an intelligent automobile double-event trigger control method aiming at double communication networks. The method comprises the following steps: initializing; establishing intelligence to judge whether an SC event triggering condition and a CA event triggering condition are met or not; solving a front wheel steering angle control instruction; and performing intelligent automobile double-event trigger control. According to the invention, a double-event trigger condition is designed to determine whether a state signal of an intelligent automobile path tracking type 1 fuzzy model is updated to a type 1 fuzzy time-lag controller or not and determine whether a front wheel steering angle control instruction calculated by the type 1 fuzzy time-lag controller is updated to an actuator module or not; communication resources of double communication networks are effectively saved; the method has the capability of efficiently avoiding the problem of communication congestion, can save a lot of communication resources, and improves the reliability of the intelligent automobile in the path tracking control process. The fuzzy time-delay controller is designed to calculate a front wheel steering angle control instruction, and the reliability of path tracking control of the intelligent automobile is ensured.

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 provides a heterogeneous multi-agent system control method under a DoS attack based on an event triggering strategy. Robust cooperative control under a complex attack environment is realized through the following steps: S1, performing state description and topological structure modeling on a heterogeneous multi-agent system; s2, carrying out approximation on an unknown nonlinear function in the system; s3, restraining the error dynamic state through a performance function, and ensuring that the system response meets the preset precision; s4, designing a time-varying threshold function, and dynamically adjusting a trigger condition to reduce communication burden; s5, designing an anti-attack controller in combination with event triggering and a Lyapunov theory; s6, designing an adaptive law to carry out online estimation on disturbance; and S7, testing synchronization precision, communication efficiency and anti-attack performance based on a physical platform. According to the method, deep fusion of the event triggering mechanism and the preset performance constraint is realized for the first time, and the self-adaptive dynamic threshold strategy is introduced, so that malicious interference of DoS attacks on a communication link is effectively dealt with, and it is ensured that the heterogeneous nodes can still meet preset performance indexes in an attack scene.

The invention discloses a classification and grading alarm system based on multi-source data, relates to the technical field of industrial safety and workplace monitoring, and can solve the typical problems that in the prior art, alarm logic is rigid and single, data sources are static and isolated, an intelligent alarm verification mechanism is lacked, and intelligent analysis and management processes are separated. Through multi-source sensing equipment, an edge-cloud cooperative computing architecture, a multi-source event triggering engine, an innovative classification and grading alarm engine and an AI large model rechecking mechanism, event automatic handling measure execution and alarm information distribution are realized through an automatic linkage and notification subsystem; and finally, event closed-loop is completed by the event handling and closed-loop platform, so that efficient, accurate and automatic monitoring, alarming and closed-loop management of industrial and various working place security events are realized.

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 discloses a wind power generation system model prediction control method and device based on event triggering. The method comprises the following steps: establishing a wind power generation system model; based on the wind power generation system model, whether an event triggering mechanism is activated or not is judged according to the deviation between the current state and the target state of the system and the maximum sampling interval time; designing an LMPC algorithm based on event triggering: if an event triggering mechanism is activated, solving an optimization problem in a finite time domain to obtain an optimal control sequence of the system, and fusing Lyapunov function constraint to ensure the closed-loop stability of the system; wherein the optimization problem takes a minimum weighted sum of a system output tracking error and a control input variable quantity as a target; and deploying the LMPC algorithm based on event triggering into a control device of the wind power generation system so as to realize optimal control of the wind power generation system. The stability and robustness of the wind power generation control system can be effectively improved.

The invention discloses a switching system dynamic quantization control method and device based on event-triggered communication, and the method comprises the steps: building a continuous time switching linear system model for a switching system, and giving a system stability guarantee mechanism; designing a dynamic quantizer for the continuous time switching linear system model, giving a dynamic quantization parameter and a dynamic quantization updating strategy, and cooperatively designing an event triggering mechanism according to a state variable of the system and the dynamic quantization parameter; and designing a switching law according to an event triggering mechanism, and adjusting dynamic quantization parameters of the system to realize stable control of the system. On the premise of ensuring the system performance, the communication frequency and calculation burden of the system can be reduced, the resource utilization efficiency of the system can be improved, the method can be widely applied to the fields of network control systems, industrial automation systems and the like, and an effective solution is provided for solving the control problem of a complex system.

The invention relates to the field of soft-bodied robot control, in particular to a soft-bodied robot trajectory tracking control method based on event triggering model predictive control, which comprises the following steps: step 1, on the basis of a segmented constant curvature hypothesis of a soft-bodied robot, considering system uncertainty including unmodeled dynamics and exogenous disturbance; establishing a software robot dynamic model and converting the software robot dynamic model into a state space model; 2, designing an event triggering model prediction control strategy based on the state space model of the soft robot; 3, a dynamic event trigger threshold value and a dynamic prediction time domain method are introduced to improve the calculation efficiency; 4, the recursion feasibility and closed-loop stability of a control strategy are ensured through parameter design; and 5, performing simulation analysis on the end tracking of the soft robot by applying the designed event triggering model prediction control strategy.

The invention provides a 5G communication optimization method and system of an urban rail transit train operation control system based on event trigger control. The method comprises the following steps: modeling communication interference based on the frequency and duration of the communication interference in the operation of an urban rail transit train to obtain a hybrid system of the communication interference; establishing a hybrid system model of a train group in a random transmission delay scene by combining a preset 5G communication optimization algorithm based on event trigger control based on the hybrid system of communication interference; based on the hybrid system model, a new communication period and a new control strategy are obtained, and train group operation control optimization in a random transmission delay scene is realized. The communication interference model in the 5G communication optimization method based on event trigger control is closer to the actual characteristics of communication interference, the random transmission delay caused by uncertain communication interference is reproduced, and the method is closer to the actual initiation scene.

The invention discloses a multi-mechanical-arm system random predefined time control method based on event triggering, belongs to the technical field of control, is used for time control of a multi-mechanical-arm system, and comprises the steps that a kinetic equation of the multi-mechanical-arm system is introduced, a track error tracking system is introduced, and a consensus error of the multi-mechanical-arm system is obtained; an unknown function in a kinetic equation of the multi-mechanical-arm system is estimated, a virtual control signal, a self-adaption rate and an event triggering mechanism are designed according to a self-adaption backstepping method, and stability analysis is conducted in combination with the Lyapunov stability theory. An event triggering mechanism is adopted at the same time, the updating frequency of the controller is reduced, and consensus errors of the experimental multi-mechanical-arm system can be converged into a small neighborhood within predefined time of 1 s in combination with the consensus errors of the experimental multi-mechanical-arm system.

The invention discloses an energy countercurrent prevention method based on fuzzy control and event trigger control, and the method comprises the following steps: 1, collecting system data, including a photovoltaic inverter and a related ammeter, an energy storage device and a related ammeter, and a main incoming line ammeter; 2, performing fuzzy control based on the current total incoming line power and upper and lower limits, and determining that the system enters an overload mode, a safety mode or an event trigger module; step 3, preventing countercurrent through an event trigger control module, and judging whether event trigger exists or not by integrating minimum trigger interval control judgment: if yes, entering step 4, and if not, entering step 5; otherwise, continuing to maintain the existing state, and skipping the control strategy; 4, dispatching a photovoltaic power generation and energy storage module, and compensating the residual power gap; and 5, recording and updating a system state, and circularly executing the steps. The real-time performance and the like of the anti-reflux control system are guaranteed.

The invention relates to the technical field of event-driven control, in particular to an event-driven control method for a rescue robot, which comprises the following steps of: extracting event triggering conditions, triggering time sequence data and state parameters based on identification and serial number management of various events in a rescue environment; event type classification, spatial region partitioning and triggering probability modeling are carried out in combination with an SPFA algorithm, the corresponding relation between an event and a response state can be accurately mapped in the space-time dimension, the structural expression and state scheduling capability of a complex environment is enhanced, path optimization is carried out in combination with weights, and the path optimization efficiency is improved. Quantitative allocation of response resources and quantitative evaluation of response execution efficiency can be realized, a response path cost model is constructed by adopting a Dijkstra algorithm, and a mapping relation between path suitability and a triggering probability is compared, so that dynamic screening of an optimal path can be realized under an environment constraint condition; and the execution accuracy of the response path and the task completion collaboration are improved.

The invention discloses a multi-agent cooperative tracking control method based on dual-channel event triggering. The method comprises the following steps: constructing a nonlinear multi-agent system mathematical model with uncertain dynamics; defining a tracking error function and constructing an error system by utilizing coordinate transformation; designing a virtual controller of each order by using a backstepping control method; designing a dual-channel event triggering mechanism, actual controller input and an adaptive law; and performing multi-agent cooperative tracking control according to the designed dual-channel event triggering mechanism and the controller. By adopting the control signal updating and parameter updating control strategy based on the dual-channel event triggering mechanism, unnecessary communication and calculation can be reduced, and the effect of remarkably reducing the burden of communication resources and calculation resources is achieved. By adopting an event triggering mechanism and an extended Hamming code redundant coding technical means, the security protection of control signal transmission can be realized, and the technical effects of improving the anti-attack capability of the system and guaranteeing the data integrity in an open network environment are achieved.

The invention provides a multi-unmanned ship formation collision and obstacle avoidance control method with event triggering and signal quantification, which comprises the following steps: acquiring sea condition information of surrounding environment and other surrounding ships, and establishing a trajectory control mathematical model of an under-actuated unmanned ship formation; a uniform quantizer is adopted to quantize control input and state variables in the control system; estimating quantization state information of each USV and uncertain items existing in the model by using an ESO technology; a linear model is introduced to describe a quantization process, a hierarchical design method is adopted, a kinematics guidance law and a dynamics quantization tracking control law are designed, a controller does not need priori information of quantization parameters, and an event-triggered formation control strategy is designed; and the stability of the multi-unmanned ship formation collision and obstacle avoidance control system with event triggering and signal quantization is proved by using an input state stability theory. According to the invention, while an event triggering mechanism and signal quantization are considered, formation collision avoidance and obstacle avoidance of multiple USVs are considered.

The invention relates to the technical field of industrial automation control, and discloses an event-triggered self-tuning PID temperature control system and method. According to the system, a parameter updating loop is activated when specific conditions are met through an event triggering mechanism, and game theory self-learning and a preset performance control method are integrated into an extended PID controller structure through the loop, so that dynamic adjustment of parameters is achieved. The system can respond to temperature deviation and external disturbance in real time, and therefore multiple performance indexes such as overshoot, stabilization time and energy consumption are optimized at the same time. All the modules jointly optimize a control strategy through data interaction and function cooperation so as to reduce the computing resource consumption of the system. In a resource limited scene, the mechanism improves the robustness of the system. Compared with a traditional PID algorithm, the method is higher in adaptability in an uncertain environment. Through innovative combination of event triggering and reinforcement learning, the method aims at overcoming the limitations of response delay, remarkable overshoot, resource waste and the like of the existing algorithm in a complex scene.

The invention discloses a self-adaptive event trigger tracking control method for a complex system under sensor faults, which belongs to the technical field of event trigger control, and comprises the following steps: firstly, acquiring output signals of a controlled object by using a sensor, and establishing a system dynamics and fault mathematical model by combining nonlinear characteristics, external interference and sensor gain change and measurement errors; based on a model, a nonlinear constraint characteristic is determined, a dynamic gain and state observer is constructed by applying a nonlinear boundary conclusion under a sensor fault, and an unmeasurable state of the system is estimated in real time. And estimating a state by combining the dynamic gain and a state observer, determining a triggering moment by applying integrated event triggering judgment logic, generating a feedback self-adaptive event triggering control signal and outputting the feedback self-adaptive event triggering control signal to an actuator. According to the method, system nonlinearity, sensor faults and external interference can be compensated, it is ensured that accurate tracking reference signals are output, it is ensured that closed-loop signals are bounded and free of the sesame phenomenon, and communication and computing resources are effectively saved.

The present application relates to a kind of multi autonomous guide vehicle (AGV) cluster security path tracking control method of resisting false data injection attack, belong to control engineering technical field.For the problem that system stability and safety are threatened by false data injection attack in unreliable network, the present application introduces dynamic event triggering mechanism, only updates control input when necessary, reduces communication burden and energy consumption;Design adaptive state estimator, recover normal system signal from tampered sensor and actuator signal;Adaptive attack compensation mechanism is built, to inhibit the negative influence of attack on system performance.The present application fully considers the application requirement of multi-AGV cluster system in unreliable network environment, by introducing dynamic event triggered control, designing adaptive state estimator and building adaptive attack compensation mechanism, false data injection attack can be effectively resisted, and strong guarantee is provided for multi-AGV cluster system security path tracking control.

The application discloses an integrated management and control system and method for a smart industrial park based on the Internet of Things, and relates to the technical field of park management and control, which comprises the following steps: collecting park event data, equipment operation data and management and control resource state data, generating event trigger states and management and control resource available states; generating an event influence window according to the event trigger states and the equipment operation data; generating an event resource matching result and an event disposal path based on the event influence window and the management and control resource available states; generating an event disposal instruction according to the event disposal path, collecting corresponding event disposal feedback of the event disposal instruction, rewriting the event influence window and the management and control resource available states according to the event disposal feedback, and generating an integrated management and control result for the park; and the application improves the adaptation degree among event disposal, resource linkage and feedback rewriting under the condition that multiple types of events in the park continuously and concurrently trigger and management and control resources are scattered and occupied.

The application belongs to the technical field of aerospace, and relates to a distance-keeping game control framework and method for a maneuvering non-cooperative target. The framework comprises a rolling control window, an event perception window arranged in each rolling control window, and an event triggering mechanism arranged in each event perception window. The event perception window predicts an orbit state through a two-body dynamics model, the event triggering mechanism calculates a relative distance between an active satellite and a target satellite at each discrete point according to an orbit prediction result, and judges whether the relative distance constraint is met. If the relative distance constraint is met, an impulse maneuver strategy of the active satellite as a decision output is calculated through a game strategy optimization algorithm. Simulation results show that the application can realize long-time distance keeping, and the success rate reaches 98% in a 12-hour maintenance task, which is increased by 308.3% compared with a traditional algorithm.

The invention relates to the technical field of power systems, in particular to an event-triggered proportional-integral repetitive controller and method and a related device. Two channels of the event triggering block are respectively provided with two adjusting coefficients, and tracking error signals are respectively input into a repetitive controller and a proportional-integral controller after being adjusted; when a triggering condition is met, two adjustment coefficients of a forward channel of the event triggering block change along with time; the tracking error is increased, the adjustment coefficient of the repetitive control channel is reduced, and the adjustment coefficient of the proportional-integral channel is increased; and after a trigger period, the two adjustment coefficients of the event trigger block are gradually recovered to default values along with the reduction of the tracking error signal. According to the invention, decoupling of the proportional-integral repetitive controller is realized, the dynamic performance is improved, and output distortion of multiple periods caused by mismatching of the dynamic performance of the proportional-integral controller and the repetitive controller is avoided.

The application discloses a kind of unmanned aerial vehicle trajectory tracking methods based on reinforcement learning and event triggering, belongs to unmanned aerial vehicle automatic control technical field.Establish the six degrees of freedom model of unmanned aerial vehicle, given desired tracking trajectory, according to the desired position, attitude angle, speed and angular velocity obtained according to given trajectory;Trajectory tracking error calculation: the error between actual trajectory and desired trajectory is calculated, filter tracking error is defined, and it is derived;Event-triggering mechanism design is carried out to input, state quantity is designed, and reasonable event-triggering parameter is designed;Evaluator neural network design, design the tracking performance of evaluation system, for tracking performance improvement;Actuator neural network design, approximation uncertain term in model;Model nonlinear control law calculation is obtained in combination with neural network.The method of the application is based on reinforcement learning and event triggering method, realizes optimal control, solves the problem of long-time hovering actuator loss and communication burden of unmanned aerial vehicle under parameter uncertainty and external disturbance.

The application discloses a tension anti-interference control method based on an event triggering mechanism, which comprises the following steps: S1, a surface-mounted permanent magnet synchronous servo motor model is established, and a mathematical model is described to obtain state variables in a speed loop, a current loop, speed and position output of the motor; S2, the obtained data is analyzed to design a sliding mode controller and a fractional order PID controller; S3, a disturbance observer is designed to estimate and compensate the disturbance in the system through the state variables of the double loop, and the robustness of the system is improved; S4, an event triggering mechanism based on nonlinear sliding mode control is designed to reduce the calculation amount of the sliding mode control, reduce the operation demand of the overall system, and complete the control target. The application not only improves the accuracy and response speed of motor control, but also improves the energy efficiency ratio and application range of the system by reducing the calculation amount and optimizing the control algorithm; in the application occasion of high-precision tension control, the stability of the production process and the quality of the product can be remarkably improved.

The invention discloses an event triggering super-torsion compound control system for a high-order direct-current converter, and relates to the technical field of intelligent control of high-order direct-current boost converters, and the event triggering super-torsion compound control system comprises a converter main circuit, a signal acquisition module, a digital controller DSP and a PWM driving module, the system is composed of a general model construction module, a power feed-forward compensation module, a super-torsion sliding mode control module, an event trigger judgment module and a signal synthesis and driving module. In the design of the data automatic controller, a traditional double-closed-loop cascade control architecture is abandoned, and a flattened composite control strategy is adopted: a power feed-forward path based on physical energy conservation is taken as the main force of quick response, so that the undershoot problem of a non-minimum phase system is solved; a sliding mode feedback loop based on a standard hyper-torsion algorithm is used as the core of high-precision adjustment, and steady-state errors and buffeting are eliminated; and an anti-deadlock mixed event triggering mechanism is supplemented, so that consumption of computing resources is reduced, and meanwhile, static error-free tracking is realized.

The application belongs to the field of robot control, and particularly relates to a mobile robot dynamic event triggering control method based on reinforcement learning. In view of the unknown wheel slip effect of the robot and the communication resource constraint problem, a kinematics model and an Euler-Lagrange dynamics model of a four-wheel Mecanum wheel mobile robot are established, the motion deviation caused by four-wheel slip is characterized as non-matching disturbance, the trajectory tracking problem is converted into an extended dimension system adjustment problem constrained by the worst case disturbance by combining the tracking error and the expected trajectory and introducing the steady state control, a dynamic event triggering mechanism is introduced to reduce the calculation burden and transmission load, a Critic reinforcement learning algorithm is designed to solve the Hamilton-Jacobi-Isaacs equation to obtain an approximate optimal controller under the dynamic event triggering condition, and the consistent ultimate bounded stability of the closed loop system is analyzed based on the Lyapunov theory, so that the high-precision trajectory tracking performance of the robot is ensured on the premise of saving communication and calculation resources.

The application discloses a kind of networked switching system security control method based on encryption and decryption switching Q learning replay attack detection, comprising: constructing switching system model and controller model, obtain state signal and mode signal;Event-triggered mechanism based on replay attack detection is constructed, including error detection condition, mode matching condition and attack detection condition;According to Q learning algorithm, replay attack detection mechanism based on switching Q learning is constructed, event-triggered mechanism and replay attack detection mechanism are used to process the mode signal and state signal of triggered time subsystem, state estimation value, mode estimation value and state estimation residual norm are obtained, the detection result of attack is determined;State estimation value and mode estimation value are substituted into controller model, and safety controller is obtained, the equation of closed-loop system is obtained by simultaneously solving the equation of safety controller, switching system model, state estimator and mode estimator;Switching rule based on replay attack detection is constructed, according to replay attack detection result, the switching behavior of closed-loop system under replay attack is analyzed, and the judgment condition of system safety and stability is obtained, the present application can accurately analyze whether replay attack is carried out, and give the safety control discrimination condition for ensuring that switching system under replay attack, realize the safety control of switching system.

The application relates to a heterogeneous vehicle cooperative control system and method based on a dynamic event triggering mechanism and belongs to the technical field of vehicle cooperative control. The method comprises the following steps: establishing a nonlinear heterogeneous vehicle system dynamics model; calculating a vehicle-to-vehicle tracking distance error, fixed-time funnel control, error conversion and constructing a sliding mode surface; constructing a fixed-time-based auxiliary system to compensate for the nonlinear error of an actuator and the control input error caused by an event trigger; constructing an adaptive law for external and internal disturbances; designing a fixed-time formation controller; designing a dynamic event triggering control mechanism; judging whether an event triggering condition is met, and executing the method flow according to the triggering condition; and judging whether an ending condition is met, and executing the method flow according to the ending condition. The system and method can guarantee the cooperative control performance of heterogeneous vehicles in complex scenes, significantly reduce the communication and calculation costs, and enhance the adaptability and flexibility of the heterogeneous vehicle system.

The application discloses a DoS attack-based event triggering voltage control method, device and equipment, relates to the technical field of power system automatic control, triggers control on demand through an event triggering mechanism, reduces invalid communication and frequent operation of equipment, and significantly reduces consumption of communication resources; moreover, a state evolution model is innovatively constructed by combining a DoS attack period and communication delay, compound influence of the attack and the time delay on voltage is predicted, and a differentiated control strategy is executed based on whether the attack blocks a control channel, namely, local emergency is guided based on prediction when the attack is active, precise regulation and control are quickly switched when communication is restored, power fluctuation and network attack threats under high-proportion new energy grid connection are effectively coped with, communication load of the system and operation frequency of voltage regulation equipment are effectively reduced while control performance is ensured, and voltage stability and safe operation of the power distribution network are ensured.

The invention belongs to the field of intelligent control of mining equipment, and provides a self-adaptive asymptotic tracking control method for an uncertain electro-hydraulic servo system based on event triggering in order to solve the problem that the performance of the electro-hydraulic servo system is poor under complex working conditions. The core of the technical scheme is that unknown parameters and disturbance in the system are uniformly estimated and compensated through a single-parameter adaptive law, and a time-varying integral bounded function is introduced to ensure asymptotic convergence of tracking errors; a nonlinear dynamic surface filter is adopted to simplify the controller structure, and differential explosion is avoided; dynamically updating a control signal in combination with an event trigger mechanism to save communication resources; and an unknown control gain direction problem is processed by using a Nussbaum function. According to the method, nonlinearity and uncertainty of the system are effectively overcome finally, high-precision and high-robustness asymptotic tracking control is realized while network bandwidth and computing resource consumption are remarkably reduced, an accurate mathematical model is not needed, and the method has very high engineering practicability and universality.

The application relates to an event-triggered optimal line-of-sight (LOS) formation control method and system for an underactuated unmanned surface vehicle (USV) under unknown disturbance, and relates to the technical field of USV control. The application aims to solve the technical problem of realizing event-triggered optimal control while considering actuator saturation by establishing an RL-driven LOS guidance scheme, so as to achieve optimal state regulation and tracking control. The technical points are as follows: the application establishes an RL-driven LOS guidance scheme, decouples the trigger decision and the actuator allocation, and introduces a new event-triggered mechanism. The overall structure of the ETO-LOS control scheme proposed by the application is as follows: firstly, the expected trajectory and external disturbance are processed in the LOS guidance layer to generate a virtual control signal. Then, optimal control is realized through a DSC and a two-stage IAC network, and then an event-triggered mechanism is constructed in the actuator layer. The ETO-LOS scheme proposed by the application realizes stable learning and closed-loop performance while significantly reducing the control update frequency.