91results about How to "Reduce communication burden" patented technology

The invention provides a low-altitude industry chain information intelligent cooperation and airspace management platform based on federal learning. Through cluster ad hoc network and decentralized federated learning, the method is divided into three stages of data acquisition, data preprocessing and analysis application, low-altitude scene data communication of dynamic topology and static topology is realized, and the efficient communication requirement in a low-altitude operation scene is met. According to the system, differential data transmission strategies are adopted, optimization design is carried out on a single machine, a co-task cluster, a cooperative cluster and a non-cooperative cluster, and the communication efficiency and the resource utilization rate are improved. In the data preprocessing stage, federated learning aggregation of multi-source data fusion, data anonymization and decentralization is combined, and data privacy protection and safe sharing of three parties of industry, university and research are ensured. In the analysis and application stage, low-altitude aircraft task scheduling, airspace flow control and environmental condition evaluation are realized through aircraft analysis, airspace analysis, environmental analysis and digital twinborn technologies.

The invention provides a collaborative decision-making system based on multi-agent reinforcement learning, and the system comprises a global decision-making coordination module, an agent local decision-making module, a state information sharing module, a conflict detection and solution module, and a security and privacy protection module. Each agent realizes autonomous decision making and real-time learning in a local environment, the dependence of the system on centralized control is reduced, the communication burden of the system during data interaction is effectively reduced in a distributed manner, the agents can directly respond based on local data, and the data interaction efficiency is improved. The state information of each agent is collected and analyzed in real time through a global decision coordination module, and the overall strategy of the system is adjusted based on a reinforcement learning algorithm, so that global target implementation in a complex environment is ensured, and the limitation that a traditional system cannot be adaptively adjusted in a dynamic environment is effectively avoided through global optimization; and the global optimization of the multi-agent cooperative system on the basis of each agent target is ensured.

The invention provides an aerial remote sensing multi-platform collaborative observation task planning and optimization method and system, and relates to the technical field of collaborative planning, and the method comprises the steps: receiving the observation demand information of a target region and the resource information of a plurality of aerial remote sensing platforms, building a multi-platform collaborative observation task model based on a deep reinforcement learning algorithm, generating an initial task planning scheme by utilizing the constructed multi-platform collaborative observation task model, optimizing the initial task planning scheme by adopting an improved multi-target particle swarm optimization algorithm, and distributing corresponding specific execution instructions to each aerial remote sensing platform through a security encrypted communication link; state information, including positions, postures, energy levels and equipment working states, fed back by the aerial remote sensing platforms is received and processed in real time, and a current task planning scheme is dynamically adjusted according to the real-time state information based on a federated learning framework by utilizing edge nodes and cloud cooperative computing capability, so that adaptive optimization of tasks is realized.

The invention discloses an adaptive fuzzy preset performance control method triggered by an under-actuated ASV dynamic event under a network attack. The method comprises the following steps: constructing an under-actuated ASV model containing model uncertainty items under the network attack; constructing an adaptive fuzzy observer according to the under-actuated ASV model to obtain a state prediction model, and obtaining a state prediction error of the under-actuated ASV according to the state prediction model; constructing a performance control function for keeping formation control of followers and pilots; constructing a ship fuzzy preset performance virtual control law according to the performance control function; according to the ship fuzzy preset performance virtual control law and the state prediction error of the under-actuated ASV, constructing an adaptive law; constructing a switching dynamic event triggering mechanism to obtain the actual input of the controller; and based on the switching dynamic event triggering mechanism and the adaptive law, constructing a formation controller triggered by the switching dynamic event according to a pre-designed N-type function. The problems that in an existing ASV trajectory tracking task, system signals are maliciously changed through information spoofing attacks, system communication resources are limited, specified performance indexes need to be achieved, disturbance of an external marine environment, underactuation of an autonomous water surface vehicle, an uncertain nonlinear model and the like are solved. And the ASV path tracking robustness is reduced, and even the whole system is unstable.

The invention discloses a multi-machine parallel operation control method for a network-forming type energy storage converter, and relates to the field of electrical engineering. Establishing a sparse communication network; obtaining the frequency, the voltage effective value, the active power and the reactive power of the node and the active power and the reactive power of a neighbor node; constructing a network construction type converter control strategy combining active support and cooperative control, and calculating the output frequency and voltage effective value of each network construction type converter; and obtaining a three-phase AC voltage waveform through dq conversion, modulating the three-phase AC voltage waveform into a three-phase PWM switch control signal, and applying the three-phase PWM switch control signal to the network-forming converter to achieve a control effect. The active support of the frequency and the voltage aims at the deviation of the frequency and the voltage to rated values in the VSG control, the contradiction between the power control and the frequency and voltage control is eliminated by the cooperative control strategy, the defects of the VSG control are made up by the proposed optimization control method, the frequency and the voltage of the network construction type converter are controlled to be the rated values, and meanwhile, the efficiency of the network construction type converter is improved. And the active power and the reactive power can be accurately and reasonably distributed.

The invention discloses an unmanned aerial vehicle cluster trajectory planning method and system under unreliable communication, and the method comprises the steps: firstly building and updating a three-dimensional map based on an airborne sensor of an unmanned aerial vehicle, then building a local map based on the data of the sensor at the current moment, and recognizing an idle area and an occupied area; generating a track for avoiding an obstacle by combining time and space constraints; the track and environment information are shared through communication, and collision is avoided through coordination; an MPC module is used to predict a trajectory when trajectory information is lacked, and collision check is performed. And in the feasible trajectory execution process, environment changes are coped with through timing collision detection, and safe navigation is ensured. According to the method, collision of the unmanned aerial vehicles due to absence of cluster real-time trajectory information for a long time is avoided by optimizing a communication network and an MPC prediction trajectory; and an MPC module is added to generate a prediction track when the interaction track information is lost so as to check whether the newly generated track is collided or not.

The invention relates to the technical field of multi-agent control, in particular to a second-order multi-agent system control method based on dynamic event triggering, and the method comprises the steps: building a kinetic model of a leader-follower second-order multi-agent system considering unknown external interference, and designing a distributed fixed time observer on the basis, so as to achieve the control of the leader-follower second-order multi-agent system. State information of the leader is estimated in real time; introducing a fixed time extended state observer to estimate external disturbance; proposing a sliding mode surface based on the observation error; a distributed fixed-time sliding mode consistency tracking control protocol is designed, so that tracking errors are accurately converged within fixed time, an ideal control effect can be achieved even if external interference exists, the communication burden is effectively reduced through introduction of a dynamic event triggering mechanism, and the sesame phenomenon is avoided; tracking consistency control of the multi-agent system can be realized in a fixed time, the robustness and reliability of the system are improved, and communication resources are saved.

The invention relates to the technical field of machine learning, and discloses a federal knowledge graph representation learning method, system and device, and a medium. The method comprises the following steps: constructing a local knowledge graph and a local knowledge graph representation model; downloading a global neural network model from the server side, determining a neighbor entity corresponding to each entity according to the local knowledge graph representation model, and aggregating each entity and the neighbor entity corresponding to each entity through the global neural network model to obtain a target representation vector of each entity; updating the global neural network model by using the target representation vector of each entity to obtain a target neural network model; and uploading the target neural network model to a server side, and aggregating the target neural network models uploaded by all the clients through the server side to obtain a target federated knowledge graph representation model. According to the invention, the communication burden of the whole model in the training process can be reduced.

The invention discloses a virtual power plant optimal scheduling method and device, a medium and a product, and relates to the technical field of power plant electric energy resource scheduling and allocation, and the method comprises the steps: obtaining basic data of a virtual power plant; respectively carrying out clustering analysis on the temperature control load data and the electric vehicle data by adopting a density clustering algorithm and a K-means + + algorithm based on an HHACOA algorithm to obtain a clustering result; according to the basic data of the virtual power plant, an internal approximation constraint space integration method is adopted, and adjustable power domain data of the whole temperature control load cluster and adjustable power domain data of the whole electric vehicle cluster are determined; establishing a planning function optimization model according to the adjustable power domain data of the temperature control load cluster, the adjustable power domain data of the electric vehicle cluster and the clustering result; and solving the planning function optimization model to obtain an optimization strategy result of the virtual power plant. The method can improve the adjustment capability of the virtual power plant to the power grid, and guarantees the safety and stability of the power system.

The invention relates to a multi-unmanned aerial vehicle double-layer collaborative navigation method based on event triggering, which comprises the following steps: when the navigation performance of any unmanned aerial vehicle in an unmanned aerial vehicle cluster does not meet the navigation performance requirement, triggering two levels of information fusion collaborative navigation steps; according to the method, each slave in an unmanned aerial vehicle cluster sends local satellite navigation position data and ranging data to a host in the unmanned aerial vehicle cluster, and the host fuses the satellite navigation position data and the ranging data and outputs position estimation and covariance of each unmanned aerial vehicle as a preliminary collaborative navigation result; and then, performing state updating on each unmanned aerial vehicle in the unmanned aerial vehicle cluster by using a state recursion result of the local filter and the preliminary collaborative navigation result to obtain a final collaborative navigation result. According to the method, the fusion strategy of the unmanned aerial vehicle cluster can be dynamically adjusted, so that the navigation performance meets the navigation performance requirement, and meanwhile, the communication burden, the calculation complexity and the system power consumption of collaborative navigation can be reduced.

The invention provides a distributed secondary privacy protection control method and system for an island alternating current microgrid. The method comprises the following steps: establishing a dynamic model of a distributed generator and a microgrid economic dispatching model; carrying out encryption protection on the real state of the island AC micro-grid to obtain an encrypted sub-state; based on the encrypted sub-state, constructing a dynamic event triggering consistency controller for voltage and frequency recovery; and constructing a dynamic event triggering consistency controller with optimal power distribution. According to the method, the secondary distributed controller is designed for the dynamic model of the distributed generator and the microgrid economic dispatching model, so that the voltage and frequency stability can be maintained, the optimal economic operation is realized, and the method has the advantages of small communication burden, high convergence speed and capability of realizing accurate consistency; the privacy information is protected by reducing the number of times of real-time state transmission and a state decomposition mechanism, so that the problem of privacy information protection when external attacks exist is solved.

The invention provides a multi-axis mechanical arm model consistency control method based on neighbor event driving, and the method comprises the steps: firstly building a nonlinear multi-mechanical arm system and a dynamic model of a corresponding mechanical arm, which comprises a leader mechanical arm and a plurality of sub-mechanical arms; the sub-mechanical arm obtains state information of a neighbor sub-mechanical arm according to the neighbor relation; when the leader mechanical arm sends out a leader signal, the sub-mechanical arms obtain state information of the leader mechanical arm and state information of the neighbor sub-mechanical arms according to the state information of the sub-mechanical arms and the connection relation between the sub-mechanical arms and the neighbor mechanical arms, and then consistency error signals of the sub-mechanical arms are calculated; and a neighbor event triggering mechanism and a sub-mechanical arm consistency controller are designed, parameters of the sub-mechanical arm consistency controller are adjusted according to the consistency error signal based on the neighbor event triggering mechanism, and a control signal of the sub-mechanical arm is generated. According to the method, the consistency control problem of the multi-agent system can be solved, a neighbor event triggering mechanism is introduced to reduce the communication load, the communication burden of the system is reduced, and the communication efficiency of the system is improved.

The invention relates to the technical field of micro-grids, and discloses an AC micro-grid distributed power control method with quantitative event triggering. According to the method, due to droop control in an island DC micro-grid, distribution of reactive power and unbalanced power in a system cannot be guaranteed, and a distributed control method with quantitative event triggering is provided for solving the problem. According to the control scheme provided by the invention, communication is reduced on the basis of realizing reactive power and unbalanced power distribution among distributed power generation units by adding a designed distributed secondary controller with a quantitative event triggering method, and parts which cannot be accurately distributed can be readjusted by a negative feedback system through PI control; in addition, the proposed method does not need global power information, each distributed generator only communicates with a neighbor when meeting a triggering condition, the communication burden of a micro-grid communication network is greatly reduced, the stability of the method is proved through a Russell invariant set principle, and Zeno behaviors are also eliminated through an anti-security method.

The invention relates to a Markov jump network system control method under DoS attack based on a phototropic growth algorithm. The method comprises the following steps: establishing a networked system model with Markov jump parameters; an energy-limited DoS attack model is constructed; introducing a dynamic event triggering mechanism, and determining a triggering moment based on a measurement error, a weighting coefficient, a design parameter and an internal dynamic variable; based on a networked system model and an energy-limited DoS attack model, a dynamic event triggering mechanism is fused to construct a closed-loop system dynamic model; establishing a hybrid anti-interference performance index; performing global optimization on gain parameters of the closed-loop system dynamic model by adopting a phototropic growth algorithm to obtain an optimal controller gain; and constructing a controller under the energy-limited DoS attack, and realizing the control of the Markov jump network system under the DoS attack. The finally constructed controller can realize high robustness, low communication burden and excellent dynamic performance control of the Markov hopping network system in an energy limited DoS attack scene.

The invention discloses a fault-tolerant state quantitative control method based on a flexible joint mechanical arm, and relates to the field of state quantitative control of flexible joint mechanical arms. Comprising the steps that a, a state space model of a flexible joint mechanical arm system is established; b, defining a radial basis function neural network of an approximate unknown term; c, defining a uniform quantizer; d, error transformation of an unquantized flexible joint mechanical arm system is defined; e, deriving an unquantized error variable; f, constructing an unquantized virtual controller and an adaptive rate; g, designing an unquantized actual anti-interference fault-tolerant controller; and h, designing a quantized controller and an adaptive rate. According to the controller designed by the invention, the tracking performance of the flexible joint mechanical arm with a quantitative state can be ensured, fault-tolerant control is realized, and the system efficiency is improved.

The invention provides a robot system distributed formation control method and device based on a U-K theory, and the method comprises the steps: building a dynamic model of a networked robot system through employing a Newton-Euler mechanics method according to system parameters in the networked robot system moving in a two-dimensional space; according to a preset algebraic graph theory strategy, a communication relation between robots is established, a communication topological graph of a directed weighted graph with a directed spanning tree is set, and an expected formation control target is converted into a constraint equation. And combining the kinetic model with a U-K equation to calculate servo control force required by the robot system under the incomplete constraint to complete an expected formation control target, and designing a distributed formation controller under the condition of no leader by adopting a preset distributed formation control algorithm based on the servo control force. According to the technical scheme, the technical problem of lack of universality in related technologies is solved, the application range is expanded, generality and universality are achieved, and formation control stability is guaranteed on the basis of simplifying operation of control force.

The invention discloses a training method and device for a federal learning decision tree model, and relates to the technical field of big data. A specific embodiment of the method comprises the following steps: acquiring first credit characteristic data and a credit label of each user in a plurality of users; determining a first dissimilarity matrix according to the first credit feature data; receiving a second dissimilarity matrix sent by the data provider; determining global perception sample selection probability distribution according to the first dissimilarity matrix and the second dissimilarity matrix; and then cyclically executing the following processes: training to obtain a decision tree model: determining a user core set of the current node according to global perception sample selection probability distribution, then determining each sub-node after the current node is split according to the user core set, and taking each sub-node as the current node. According to the embodiment, the accuracy of model prediction can be improved, the performance of the model is improved, the optimization efficiency of the model and the training speed of the model are improved, and the training cost of the model is reduced.

The present invention discloses a spacecraft formation attitude cooperative control method based on event triggering, comprising: establishing a spacecraft formation system attitude dynamics model considering external disturbance and model uncertainty; setting cooperative error auxiliary variables according to attitude and angular velocity information of formation spacecraft and communication relationship between formation spacecraft; setting measurement error variables according to cooperative error auxiliary variables; determining event-triggered attitude cooperative control law and event triggering conditions according to cooperative error auxiliary variables and measurement error variables; controlling the attitude and angular velocity of formation spacecraft according to the event-triggered attitude cooperative control law, and judging whether the event triggering conditions are met, if so, updating the event-triggered attitude cooperative control law corresponding to the formation spacecraft, and sending the state information of the formation spacecraft to adjacent formation spacecraft. The present invention can significantly save the energy, communication and computing resources of the formation system while ensuring that the formation system has good control performance.

The embodiment of the invention is suitable for the technical field of medical treatment, and provides a medical equipment voice triggering display method and device, electronic equipment and a product, and the method comprises the steps: obtaining patient information of a patient; determining a target voice set according to the patient information; according to the patient information and the target voice set, determining a target strategy for configuring and displaying a plurality of preset voice contents in the target voice set; based on the target strategy, displaying touch areas respectively corresponding to the plurality of preset voice contents in at least one voice control window; the touch area is used for responding to the touch operation and broadcasting the corresponding preset voice content. By adopting the method, the communication burden of operators can be reduced, and the working efficiency can be improved.

The invention provides a secure communication method and system for a low-power-consumption wireless network. And a global message sending cache set mechanism is provided, and the gateway can manage the messages which are not received, so that the data cannot be lost due to node dormancy. In combination with a timed task mechanism, the gateway can retransmit messages in a moderated manner at preset wake-up time or other appropriate time of the wireless nodes, so that the situation that the network load is increased due to too frequent retransmission, and then the performance of the whole system is affected is avoided; a key management system is introduced, and a gateway is responsible for applying and distributing working keys of wireless nodes in a unified manner. And the gateway obtains the working key which needs to be used at present through communication with the key management system, and distributes the working key to each node. The problem that the calculation amount is too large when the wireless node carries out asymmetric key negotiation is solved, the key management process of the node can be simplified, and the calculation and communication burden of the node is reduced; in order to prevent communication abnormity caused by secret key mismatching between the wireless node and the gateway, a secret key proofreading mechanism is designed in the process that the wireless node applies for a working secret key. The mechanism ensures that an encrypted communication link established between the node and the gateway is encrypted and decrypted using the correct key.

The invention discloses a fault detection method for a distributed grid-connected photovoltaic power generation system based on ellipsoid estimation. The fault detection method comprises the following steps: 1, establishing a state model and a sensor measurement model of the distributed grid-connected photovoltaic power generation system; 2, describing a state range through an ellipsoid geometric method; 3, correcting the predicted state according to the measurement data of the sensor; during correction, the center and the shape of the ellipsoid geometric method are adjusted according to the measurement data, so that the range of describing the state through the ellipsoid geometric method is more accurate; 4, fault detection is realized by judging whether the prediction ellipsoid set and the updated prediction ellipsoid set intersect or not; if the intersection of the predicted ellipsoid set and the updated predicted ellipsoid set is not an empty set, determining that no fault occurs; and if the intersection of the predicted ellipsoid set and the updated predicted ellipsoid set is empty, indicating that a fault occurs. According to the method, non-Gaussian noise and system model errors can be effectively processed, and the method is particularly suitable for fault detection in a complex dynamic environment.

A self-organizing swarm robot system and control method based on cellular soft and hard mechanisms are disclosed. The system includes several individual robots. Each robot includes an upper shell, a middle support, a lower support chassis, electrically driven legs, a magnetically connected actuator, a PCB control board, and light-emitting elements. The PCB control board is equipped with an ESP-NOW wireless communication module, a light sensor, and a multi-pose sensor. The magnetically connected actuator includes static and dynamic actuators alternately distributed along the circumference. This invention can simulate the adhesion force regulation and morphological expansion and contraction mechanism between biological cells, achieving dynamic control between swarm robots under mechanical action. It exhibits high flexibility and fast response speed, enabling multi-degree-of-freedom movement and posture adjustment in complex environments. By using ESP-NOW wireless communication technology in conjunction with light sensors and multi-pose sensors to construct a high-precision distributed sensing and control system, it supports the self-organization, collaboration, and task execution capabilities of swarm robots in weak information environments.

The invention relates to a multi-agent collaborative sensing method based on spatio-temporal joint selection. The method comprises the following steps: acquiring original sensing data of a plurality of agents; constructing a spatial sparse region selection mask; generating a joint sparse feature map; sparse features of the intelligent agent are obtained; obtaining a fused feature map; and carrying out intermediate-stage three-dimensional target detection, and carrying out later-stage fusion on the intermediate-stage three-dimensional target detection and an independent detection result of the collaborative agent to obtain a final collaborative perception detection result. According to the method, the space sparse region is constructed to select the mask, so that the relatively high sensing performance can still be kept in a scene with extremely limited bandwidth; the communication burden is reduced, the transmission content is more focused on a dynamic target which really contributes to collaborative perception, and the information utilization rate is improved; the detection precision is improved by using multi-view complementary information, the missed detection and false detection risks caused by time delay or shielding in intermediate fusion are reduced through a later compensation mechanism, and finally a more accurate and more complete 3D target detection result is obtained.

The invention provides an unmanned vehicle system. The unmanned vehicle system comprises a plurality of unmanned vehicles. The plurality of unmanned carriers include a first unmanned carrier and a second unmanned carrier. The first unmanned vehicle provides an information pattern, wherein the information pattern indicates control information. The second unmanned vehicle obtains the control information by identifying the information pattern. Wherein the plurality of unmanned carriers are distributed in a working range divided into a plurality of working layers, the first unmanned carrier determines a specific working layer where the second unmanned carrier is located, and the specific working layer is one of the plurality of working layers. Therefore, the management server can transmit the information pattern and / or the control information to the second unmanned vehicle through the first unmanned vehicle under the condition that the management server does not need to communicate with the second unmanned vehicle, so that the communication burden and complexity of the management server can be effectively reduced.

The invention provides a finite time cross-domain aircraft cluster formation control method based on a neural network, and relates to the field of aircraft control, and the method specifically comprises the steps: building a unified nonlinear non-strict feedback system for the dynamic characteristics of cross-domain aircrafts in different domains; collecting real-time flight states and position information of a plurality of followers; designing an improved Tan type nonlinear mapping function, and identifying an initial output constraint type of the system to obtain an unconstrained new variable; constructing a dual neural network combining a shared network and an exclusive network, designing a backstepping method, combining a switching function and a non-singular fast finite time control strategy, and controlling the output of a dual neural network structure; and a switching threshold event triggering mechanism is designed, so that the unmanned aerial vehicle group has different responses in different environments. According to the technical scheme, the problem that mode switching, dynamic coupling and cooperation consistency in cross-domain cooperation are not considered in a formation control method in the prior art is solved.

The present invention relates to the field of data security, and provides a method for secure sharing of plateau data based on blockchain. The main scheme includes: A. System initialization: Generate system parameters and initial blocks according to security parameters, and generate a private key and public key for each user who joins the blockchain; B. Data release and smart contract creation: The data owner stores the encrypted plateau data on the cloud server, and broadcasts the relevant meta information to the blockchain network to realize fast retrieval of on-chain and off-chain data. The data owner creates a smart contract to realize automatic access control of plateau data; C. Data authorization: The data requester retrieves relevant meta information on the blockchain according to demand, authenticates the authority through the smart contract, and obtains the data access authorization certificate; D. Data acquisition: The data requester obtains the re-encrypted ciphertext on the cloud server through the authorization certificate, decrypts the ciphertext locally with its own private key, obtains the plateau data, and verifies its integrity. The present invention can realize the automatic and secure sharing of data, and effectively ensures the confidentiality, authenticity, integrity, and controllability in the data sharing process.

The application discloses a quad-rotor formation obstacle avoidance control method based on an improved DDPG algorithm, and belongs to the technical field of unmanned aerial vehicle formation control. The method adopts an improved DDPG reinforcement learning algorithm to plan an obstacle avoidance path of the quad-rotor. When the improved DDPG reinforcement learning algorithm is executed, the priority weight of each quadruple experience in the experience replay pool is initialized. After the quadruple experience in the experience replay pool is sampled and trained, the priority weight of each quadruple experience is recalculated based on a TD error, and a Sum_Tree structure is updated. The method effectively alleviates the training instability caused by hyperparameter sensitivity, the misleading of policy updating caused by overestimation of Q values, and the problem that key experiences are not sufficiently learned, accelerates the convergence speed of the quad-rotor formation obstacle avoidance training, and improves the obstacle avoidance effect.

This invention discloses a hierarchical federated learning method and system based on split meta-learning. The method specifically includes: a cloud server initializing a global model and distributing it to various edge servers; the edge server layer distributing the global model to its respective clients; each client updating the feature extraction module of the global model locally based on local data, while simultaneously freezing the classifier module of the global model; the edge server layer aggregating the updated parameters of the feature extraction modules from multiple clients and performing meta-optimization on the classifier module using a local validation dataset to generate an edge local model; and the cloud server layer periodically collecting model parameters from multiple edge servers and updating the global model using a gradient-sensitive momentum aggregation strategy. This invention, through a three-layer "vehicle-edge-cloud" architecture and a two-level collaborative optimization mechanism, achieves coordinated optimization between privacy protection, communication efficiency, and model adaptability in a vehicle-to-everything (V2X) environment.

The application provides a kind of unmanned ship consistency control method and equipment, it is related to unmanned system cooperative control technical field.The method comprises: according to the real-time state of the unmanned ship and the state of the unmanned ship at last event trigger time, construct real-time local measurement error;According to the state of the unmanned ship at last event trigger time and the state of all adjacent unmanned ships of the unmanned ship, construct joint error;Based on the real-time local measurement error and joint error of each unmanned ship, judge whether event trigger condition is satisfied, when any unmanned ship satisfies event trigger condition, obtain and according to the state of the unmanned ship at event trigger condition satisfying time, the state of the adjacent unmanned ship of the unmanned ship and unmanned ship controller gain matrix, update the controller output of the unmanned ship and the adjacent unmanned ship of the unmanned ship, carry out consistency control to each unmanned ship according to controller output;The application can accurately and safely carry out multi-unmanned ship consistency control under the condition that system model is unknown.

This invention discloses an adaptive self-triggered optimal control method for catalytic rod reactors, specifically comprising: a temperature field model of the catalytic rod reaction process described by a high-dissipation partial differential equation system based on the control input of the cooling material under time triggering, and obtaining a low-order slow subsystem characterizing the high-dissipation partial differential equation system after order reduction processing; constructing a corresponding Hamiltonian function based on the performance index of the low-order slow subsystem, obtaining the optimal control strategy under time triggering control based on the Hamiltonian function, and then obtaining the optimal control strategy under self-triggered control, converting the Hamiltonian function into HJB equations; constructing a self-triggered controller based on an evaluation-execution dual-network structure using data-driven adaptive dynamic programming, and outputting the adaptive self-triggered optimal strategy based on the optimal value function of the HJB equations.