705 results about "Lyapunov function" patented technology

The invention discloses a power grid dispatching decision-making method based on a graph neural network and a large model, and the method comprises the steps: collecting an original power grid data set, and generating a fusion feature tensor through adaptive distribution mapping and recursive decomposition; importing a fusion feature tensor and a power grid topology matrix, and generating a dynamic graph embedding matrix through spectral decomposition and matrix manifold analysis; performing Markov jump process analysis and optimal transmission fusion on the dynamic graph embedded matrix to generate an integrated feature matrix; a multi-target Lyapunov function is constructed on the basis of the integrated feature matrix, and a robust decision strategy is generated by solving a Hamilton-Jacobi-Bellman equation; and performing multi-dimensional performance monitoring and statistical manifold learning on the robust decision strategy to realize strategy optimization. According to the method, the uncertainty in power grid operation can be effectively processed, and the accuracy and reliability of scheduling decisions are improved.

The invention provides a micro-grid cooperative scheduling method and device, and relates to the technical field of smart grids, and the method comprises the steps: obtaining historical operation data and real-time operation data of a micro-grid system, and data of an external information system; generating load demand and energy equipment output prediction information based on the historical operation data and the data of the external information system; constructing a layered multi-time-scale decision architecture, and performing decision optimization on each layer of agents by adopting a reinforcement learning algorithm; constructing a plurality of heterogeneous agents, and carrying out cooperative scheduling on the plurality of heterogeneous agents by adopting a centralized training and distributed execution multi-agent reinforcement learning algorithm; inputting the prediction information and the real-time operation data into a decision framework, and outputting a real-time control instruction; and setting a security constraint condition, and realizing optimization of the security constraint in combination with a Lyapunov function, a Lagrange multiplier method, a security layer mechanism and a reinforcement learning algorithm. According to the method provided by the invention, the safe, efficient and reliable operation of the micro-grid in the grid-connected / off-grid mode can be realized.

The invention provides a power grid frequency modulation power energy coordination control method and system based on a super capacitor, and relates to the technical field of power grid frequency modulation, and the method comprises the steps: carrying out the time-frequency analysis of a collected power grid frequency signal, obtaining a frequency disturbance characteristic value, and determining power control data through combining with an electrical parameter value of a super capacitor group; and performing nonlinear programming calculation based on the power control data to obtain a power distribution sequence, dividing the power distribution sequence into a quick response section, a dynamic transition section and a steady state maintenance section according to a preset threshold value, and solving a control value by adopting an augmented Lagrange equation, recursive least square operation and a Lyapunov function respectively. Obtaining a power control instruction; and inputting a power control instruction into the disturbance observer, calculating a dynamic error value, constructing a nonsingular terminal equation, and outputting a power regulation signal in combination with recursive minimum entropy calculation and a superhelix algorithm.

The invention discloses a neural network adaptive iterative learning control method for a flexible joint robot, and the method comprises the steps: S1, building a constrained flexible joint robot, and initializing a system state, sampling time and control parameters; s2, compensating input saturation constraint; s3, calculating a system tracking error, and constructing an expected error trajectory; and S4, defining an error variable, and designing a neural network adaptive iterative learning controller. According to the method, the uncertainty of the flexible joint robot is estimated and compensated through the neural network, the error convergence precision is improved, by constructing the quadratic fraction type symmetric constraint Lyapunov function, it is guaranteed that the system output is always within the constraint range, the transient performance of the system is improved, and the reliability of the system is improved. According to the method, the flexible joint robot can safely and stably run in an iterative change task, when the number of iterations is increased, the tracking error is consistently converged to zero, and the method has the advantages of less required priori knowledge, no need of an accurate system model, high control precision and the like.

The invention provides an unmanned aerial vehicle bionic group trajectory emergence method and system of a space-time gradient field, and relates to the technical field of unmanned aerial vehicle group collaborative navigation and adaptive control, and the method comprises the steps: generating a four-dimensional space-time gradient field tensor matrix through a dynamic field coupling convolutional network; in combination with density-speed response data of fish school escape behaviors and unmanned aerial vehicle dynamics constraints, a bionic group interaction rule set is constructed through nonlinear differential game modeling; adopting a space-time finite element discretization method and combining with GPU acceleration parallel solving to determine an individual trajectory basis function of the unmanned aerial vehicle; performing optimization processing through a Lyapunov function to generate a group recombination instruction; and generating a space-time gradient field enabled unmanned aerial vehicle bionic group trajectory emergence scheme by integrating the updated space-time gradient field, the group recombination instruction and the historical trajectory data. According to the invention, high-precision, low-delay and high-robustness autonomous navigation capability is provided for the unmanned aerial vehicle group, and the task success rate is improved.

The self-adaptive switching control method for the unmanned surface vehicle comprises the following steps: establishing a mathematical model of the unmanned surface vehicle, and determining a control target of the unmanned surface vehicle; designing a virtual control law and a first Lyapunov alternative function for controlling the unmanned surface vehicle based on a backstepping method; designing a second Lyapunov alternative function, establishing an adaptive neural network controller model and a robust controller model, and designing control input of the unmanned surface vehicle switching control system; designing updating laws of an adaptive neural network controller and a robust controller, and designing a third Lyapunov function to perform stability analysis on the system; and testing and simulating the unmanned surface vehicle system in a simulation environment by using the adjusted control strategy. According to the control method, on the premise of ensuring the stability of the system, the path tracking error of the unmanned surface vehicle is reduced, and the energy consumption of the controller is reduced.

The invention discloses a prepreg tape tension detection method and device based on an extended state observer and a medium, and the method comprises the steps: carrying out the kinetic analysis of an unreeling shaft of an automatic tape laying machine, and constructing a tension model; converting the tension model into a state-space equation, and constructing a tension state observer according to the state-space equation; optimizing the tension state observer through a genetic algorithm based on an error between a tension estimated value output by the tension observer and a tension target value; the current corresponding to the unwinding torque, the real-time radius of the unwinding shaft and the conveying speed of the unwinding shaft are obtained and input into the optimized tension state observer, and a tension estimation value is obtained; the tension estimation value is decoupled through a controller to obtain a control signal, and the unwinding torque is controlled through the control signal; inputting the unwinding torque into a tension model to obtain an actual tension value; and setting a Lyapunov function, and judging whether the actual tension value is accurate or not through the Lyapunov function.

The invention discloses a multi-agent cooperative control system based on side event triggering and a method thereof. The method comprises the following steps: establishing each multi-agent optimal cooperative control model; calculating the optimal cooperative control model through a Bellman optimal method and a cost function of each agent to obtain an optimal cooperative control strategy of each agent; constructing an evaluation neural network, and obtaining a cooperative control strategy based on an edge event by using a generalized strategy iterative algorithm; a Lyapunov function based on a local cost function is constructed, and a multi-agent edge event triggering rule is established; the cooperative control strategy based on the edge event and the edge event triggering rule are applied to the multiple agents to solve the cooperative consensus control problem of the multiple agents under the condition that communication is limited; according to the method disclosed by the invention, a good collaborative effect can be ensured, and meanwhile, a side event trigger control protocol is utilized to realize data sampling and control updating of a point-to-point level between nodes, so that the communication loss and calculation burden of multiple agents are effectively reduced.

The invention discloses a self-adaptive safety control method of a nonlinear MIMO system under intermittent DoS attack, which comprises the following steps: establishing a nonlinear MIMO system model suffered from intermittent DoS attack, and the system model is in a strict feedback form and comprises an unknown nonlinear function and bounded external disturbance; the DoS attack causes periodic interruption of a communication link from a sensor to a controller, so that actual measurement data is unavailable in an attack activation period; designing a switching type adaptive state estimator; coordinate transformation is carried out by introducing a filter, and a system error is decomposed into a tracking error and a dynamic surface filtering error; a Lyapunov function is constructed; performing stability analysis in stages; designing an adaptive law to update unknown parameter estimation values of the FLS; and a final event triggering controller is designed, and the event triggering controller is combined with a virtual controller to track an error compensation term and an FLS approximation term. The problem that the state cannot be measured is solved.

The invention discloses a space robot finite time anti-interference trajectory tracking control method based on performance constraint and actuator saturation, and the method comprises the steps: firstly solving a state-space equation of a flexible joint free flight space robot, constructing a system error vector in combination with a finite time instruction filter and an anti-saturation auxiliary system, and carrying out the finite time instruction filter and anti-saturation auxiliary system; a novel asymmetric logarithmic obstacle Lyapunov function with an asymmetric fixed time constraint boundary is constructed; and finally, a novel finite time advanced activation anti-saturation auxiliary system and a finite time adaptive controller with a performance constraint effect are constructed, so that finite time anti-interference trajectory tracking control of the space robot is realized. According to the method, the influence of sudden disturbance is considered in the design of the flexible joint free flight space robot controller for the first time, and under the limitation of the saturation performance constraint of the actuator, the trajectory tracking rapidity and accuracy of the space robot are ensured; and the influence of sudden disturbance in a space robot system on the trajectory tracking transient performance can be effectively inhibited.

The invention belongs to the related technical field of multi-agent control, and particularly discloses a predetermined performance consistency tracking control method and system for a nonlinear multi-agent system, and the method comprises the steps: constructing a Lyapunov function based on the consistency tracking error of the multi-agent system, and obtaining a control law; consistency tracking control of the multi-agent system is realized according to the control law; wherein the system state quantity in the control law is estimated through a distributed state observer; the distributed state observer is designed through the following method: according to a universal approximation principle, a nonlinear function in a multi-agent system dynamic model is approximated based on a fuzzy logic system, and then the distributed state observer is obtained by combining the multi-agent system dynamic model. According to the method, the precision and robustness of the consistency tracking process of the multi-agent system can be remarkably improved.

The invention relates to the field of fault diagnosis, and discloses a drying machine fault diagnosis method based on deep reinforcement learning, which is used for improving the accuracy and control efficiency of drying machine fault diagnosis. A physical dynamic model fusing thermodynamics and mechanical dynamics is constructed, real-time quantitative evaluation is performed on system stability in combination with a Lyapunov function, and a fault propagation path is visually displayed by using a directed topology network. Firstly, dryer multi-source parameters are obtained to establish a dynamic model; positioning a fault source component and calculating the network influence degree of the fault source component; and a self-adaptive control instruction and a topology suppression instruction are generated, control parameters are dynamically adjusted, fault propagation is suppressed, the control instruction is converted into an actuator driving signal, and accurate control over the drying machine is completed. Through deep fusion of a physical model and data driving, the fault diagnosis accuracy and the control efficiency are remarkably improved, the troubleshooting time is effectively shortened, the production safety is guaranteed, and the method is suitable for intelligent operation of the drying machine under the complex working condition.

The invention provides a distributed formation control method driven by a recursive balance network under a communication attack, and relates to the technical field of cooperative control of a multi-agent system, and the method comprises the steps: constructing a distributed multi-agent system model, and constructing an RBN controller heterogeneous communication network architecture and a DoS attack model; the RBN controller design is optimized, the distributed RBN controller architecture is realized, the stability of the RBN controller is analyzed based on the shrinkage mapping theory, and the exponential convergence of the system state difference is proved by constructing a Lyapunov function; training the optimized RBN controller by adopting a difficulty priority confrontation training strategy, designing a multi-objective loss function, and dynamically adjusting the training weight of each difficulty level; and simulating the distributed multi-agent system optimized by the above steps, testing the robustness of the distributed multi-agent system under incremental DoS attack intensity, and performing comparative analysis of heterogeneous and isomorphic communication network configuration and comprehensive performance comparison of an RBN controller and a traditional MPC method.

The invention provides a mechanical arm predefined time trajectory tracking control method and system based on time delay estimation, and relates to the technical field of multi-degree-of-freedom mechanical arm trajectory tracking control. Constructing a predefined time error conversion function to define a system error and conversion error system, designing a barrier function and a feedback control rate based on the conversion error system, and designing a Lyapunov function to prove the stability of the system so as to track an expected trajectory; according to the method, time delay information is deeply fused, dependence on a complex model approximate structure is not needed, stable convergence of the mechanical arm system state within predefined time can be achieved when a mathematical model is not accurate, the system control performance and robustness are effectively improved, and the requirement of modern industry for mechanical arm high-performance control is met.

The invention relates to the technical field of power plant intelligent adjustment, and discloses a power plant load intelligent adjustment method and system. The method comprises the following steps: distributing and deploying sensors according to field equipment of a power plant, and constructing a distributed sensor network based on the sensors; establishing a single time scale control framework according to the distributed sensor network; a heterogeneous delay compensation mechanism is constructed based on a distributed sensor network and a single time scale control framework; constructing a local linear matrix inequality constraint condition of each node, obtaining an optimal solution of the local linear matrix inequality constraint condition according to a heterogeneous delay compensation mechanism, and then generating an optimal control strategy of each node; the method comprises the following steps: constructing a global Lyapunov function based on a distributed sensor network and a single time scale control framework; and the change rate of the global Lyapunov function is monitored in real time, and corresponding equipment is intelligently and dynamically adjusted according to the optimal control strategy. The method achieves the intelligent regulation and control of the power plant load, is good in stability, is high in adaptability, and is short in regulation and control period.

The invention discloses a nonlinear multi-agent system sampling consistency control method and system and a medium, and the method comprises the following steps: S101, obtaining system model parameters, including a nonlinear function # imgabs0 #, a pulse gain # imgabs1 #, a sampling period # imgabs2 #, a communication topological graph # imgabs3 # and a leader-follower kinetic equation; s102, verifying an assumed condition; s103, constructing a Lyapunov function # imgabs4 #, and analyzing the stability of an error system in combination with pulse disturbance; s104, deriving a consistency condition through a Lyapunov method and a pulse theory, calculating key parameters, and determining a pulse gain and a sampling period constraint; s105, selecting parameters meeting conditions; s106, constructing a sampling controller containing pulse disturbance; and S107, implementing the controller and performing numerical simulation. According to the invention, the anti-pulse disturbance capability is strong: through the design of the pulse gain # imgabs5 #, the instantaneous interference of an input channel is effectively counteracted, and the robustness of the system is improved; a sampling data control strategy is adopted, a control signal is updated only at a discrete sampling moment, and the communication burden is reduced.

The invention relates to the field of nonlinear system control, and provides a preset time reinforcement learning method and system of a continuous nonlinear system, and an electronic device, and the method comprises the steps: constructing a zero-sum game framework based on a kinetic model and a performance index of the nonlinear system; determining a value function and a Hamiltonian function based on a zero-sum game framework; applying a preset neural network model to carry out approximation on the value function, and determining an approximation error; based on the Hamiltonian function and the approximation error, an approximate optimal control strategy and a worst interference strategy are determined; constructing a Lyapunov function based on the value function and the weight error of the value function; and based on a Lyapunov function, an approximate optimal control strategy and a worst interference strategy, a reinforcement learning result is verified. The method and the device are used for overcoming the defects that convergence time cannot be dynamically adjusted, parameter complexity is high and robustness is insufficient in the prior art, and the scheme of the invention can meet dual requirements of a continuous nonlinear system on dynamic convergence and anti-interference performance.

The invention discloses a method and a system for designing network control by utilizing a genetic algorithm and self-adaptive event triggering. The method comprises the following steps of: establishing a state space model and an observer model of a networked system and a mathematical model of transmission data which is possibly subjected to spoofing attack in a network in the system; designing a Lyapunov function, and designing a controller constraint condition matrix theta capable of realizing robust stability performance of the system; performing decoupling processing on the theta matrix, and converting the theta matrix into an LMI form; the designed controller conditions are solved in computer software, controller parameters meeting the system performance are calculated, and control of the control system under the event trigger control strategy is achieved; a designed controller is combined with a genetic algorithm, genetic algorithm parameters are set according to an obtained initial solution, a gamma minimum value is obtained according to optimized parameters needed by the system, a self-adaptive event trigger control strategy is adopted, more flexible and more reliable safety control can be achieved, and the method is suitable for various different types of control systems and application scenes.

The invention relates to the technical field of information monitoring, in particular to a remote monitoring method and system for intelligently sensing situation, vibration and orientation. Identifying and acquiring a dynamic construction behavior mode, constructing a space-time dependent topological graph of the dynamic construction behavior mode by monitoring a traceability pattern and an orientation tracking community, and inferring state potential energy of the space-time dependent topological graph according to evolution of the vibration sensing signal and the horizontal sensing data so as to determine a global construction sensing situation of the target monitoring area; and performing phase space evolution trajectory evaluation on the global construction perception situation through an evaluation phase space of a spatial evolution evaluation criterion to obtain a stable node pattern, and introducing a Lyapunov function to analyze whether the divergence degree of the stable node pattern interferes with the buried facility or not so as to control remote monitoring equipment to send out a monitoring early warning signal. According to the invention, all-around real-time monitoring can be carried out on construction behaviors around the buried facility, early warning of potential risks is realized through active perception, and the safety and operation efficiency of the buried facility are improved.

The invention discloses a decentralized differential game cooperative guidance method for a missile cluster under a switching communication topological structure, and the method comprises the steps: introducing a virtual leading missile-slave missile architecture, enabling each missile to construct a virtual leading missile from an initial position, enabling nodes in a simplified virtual leading missile group system to interact through the state information of a topological structure, coordinating a virtual state, and achieving the cooperative guidance of the missile cluster under the switching communication topological structure. And generating a virtual consensus state track so as to realize decentration control. Communication topological structure switching is considered, an optimal control scheme for tracking an expected trajectory is solved in combination with a differential game theory and self-adaptive dynamic programming, and a network weight updating mechanism is designed in the process of solving an optimal control strategy for a nonlinear system line. According to the scheme, the average residence time limiting condition of switching signals is put forward, hypotheses and conditions needing to be met by part of parameters and variables are given, under the scheme, the designed multi-Lyapunov function can achieve exponential convergence, and the stability performance of a missile cluster using the guidance scheme under the topology switching condition is guaranteed.

The invention is suitable for the technical field of multi-unmanned aerial vehicle cooperative control, and provides a multi-quadrotor formation maneuvering flight event triggering cooperative control method and system, and the method comprises the steps: firstly building a kinetic model of a quadrotor unmanned aerial vehicle in a distributed formation system under an inertial coordinate system, designing a multi-quadrotor unmanned aerial vehicle cooperative formation synchronization error, and carrying out the cooperative formation of the quadrotor unmanned aerial vehicle; establishing a preset performance control function to constrain the formation synchronization error, and performing corresponding equivalent error transformation; designing a dynamic event trigger track subsystem controller; designing a robust attitude subsystem controller; and constructing a Lyapunov function, and verifying whether a closed-loop system is stable or not and whether an expected formation effect can be achieved or not. The method can effectively restrain the synchronization error of the multi-quadrotor unmanned aerial vehicle formation system, reduces the error overshoot, improves the coping capability for uncertainty, environmental interference and abrupt trajectory change, and guarantees the stable formation flight of the multi-quadrotor unmanned aerial vehicle system.

The invention discloses a reinforcement learning automatic driving decision control method based on a Lyapunov function, and relates to the technical field of automatic driving automobile safety control. The method comprises the steps that driving state information of a self-vehicle and driving state information of surrounding environment vehicles are received, the driving state information of the self-vehicle comprises the position, the speed, the acceleration and the course angle, and the driving state information of the surrounding environment vehicles comprises the position, the speed, the course angle and lane line information of the environment vehicles relative to the self-vehicle; and a decision control model of the automatic driving task based on reinforcement learning is constructed, and the decision control model takes the driving state information of the vehicle and the surrounding environment vehicles as output and takes the optimal automatic driving strategy as output. According to the method, the reinforcement learning strategy updating process is constrained through the stability condition proved by theories, the unification of safety and decision-making efficiency is realized in dynamic scenes such as highway lane changing and emergency obstacle avoidance, and the method has a good application prospect for training the safety and strategy stability of automatic driving by adopting reinforcement learning.

The invention belongs to the field of aircraft control, and particularly relates to a helicopter adaptive command filtering backstepping tracking control law design method, which comprises the following steps: firstly, decomposing a helicopter kinetic model into a plurality of strict feedback systems with uncertainty, and then designing a controller by using a backstepping method; a hybrid command filter is adopted to solve the problem of calculation explosion of backstepping, and a filtering compensation mechanism is introduced. In addition, an uncertain part in the model is approximated by adopting a neural network, and network parameters are updated online by adopting an adaptive law. And finally, designing a Lyapunov function to prove the effectiveness of the method.

The invention discloses an unknown environment-oriented rotor unmanned aerial vehicle fixed time control method, which comprises the following steps of: 1, simultaneously considering output constraint and unknown external interference under the condition of system uncertainty, and constructing a rotor unmanned aerial vehicle position ring and attitude ring nonlinear system model; 2, designing a safety expectation tracking signal # imgabs0 #, obtaining other expectation signals # imgabs1 # through inverse solution, calculating an error # imgabs2 # of a position ring and an error # imgabs3 # of an attitude ring, combining nonlinear system model characteristics of the position ring and the attitude ring of the rotor unmanned aerial vehicle, designing a barrier Lyapunov function, and selecting proper upper and lower bounds; and 3, designing a tracking controller based on the obstacle Lyapunov function in combination with a fuzzy logic system, a fixed time disturbance observer, a filter and a self-adaptive backstepping method. The method has the advantage that the rotor unmanned aerial vehicle can complete tracking of the preset expected tracking signal within fixed time on the basis of ensuring the system safety and stability under the condition that the influence of output constraint, system uncertainty and unknown external interference is considered.

The invention discloses a VSG-DFIG subsynchronous oscillation adaptive suppression method based on a Hamiltonian model, and belongs to the technical field of subsynchronous oscillation suppression of a power system. The method aims at solving the problem that an existing method for suppressing subsynchronous oscillation existing in grid-connected operation of the doubly-fed induction generator is poor in suppression effect. Comprising the following steps: constructing a port controlled Hamiltonian dissipation model; constructing a closed-loop system dynamic equation based on interconnection and damping distribution under strict passivity; an expected energy function in a unit closed-loop form is constructed, a DFIG unit feedback control law based on a Hamiltonian model is obtained, and further deformation is carried out to obtain a deformed DFIG unit feedback control law; the port controlled Hamiltonian dissipation model is corrected, and a corrected port controlled Hamiltonian dissipation model is obtained; constructing a dynamic equation of the anti-interference adaptive observer, and establishing an error dynamic equation; and constructing a Lyapunov function, and designing an estimation error feedback control law according to the stability condition of the Lyapunov function. The subsynchronous oscillation suppression circuit is used for suppressing subsynchronous oscillation.

The invention belongs to the field of flight control, and particularly relates to a tilt-rotor aircraft transition state undisturbed switching control method based on linear active disturbance rejection. Longitudinal dynamics of the tilt rotorcraft is modeled into a switching system, a nacelle inclination angle is combined to serve as a switching signal, a switching active-disturbance-rejection controller is designed, internal / external disturbance of the system is estimated in real time and compensated, and multi-channel decoupling is achieved; and a variable gain continuous switching controller is further introduced, and a time interval is set and adjusted after a switching moment, so that jolt at the switching moment is inhibited, and the influence of controller switching on the working process of the conversion process of the flight mode of the tilt-rotor aircraft is effectively reduced. Based on a dynamic error system and a multi-Lyapunov function, in combination with an average residence time constraint, a linear matrix inequality condition for ensuring the asymptotic stability of the system is deduced, and a simulation experiment of the transition state of the tilt-rotor aircraft is carried out.

The invention relates to an aluminum pipe local conduction regulation and control method, system and device based on dynamic electromagnetic field modulation and a medium, and the method comprises the steps: driving an electromagnetic coupling array to scan a target conduction region of an aluminum pipe, so as to construct a three-dimensional dynamic electromagnetic model; analyzing and generating a multi-band excitation parameter combination in a bandwidth constraint range according to the target conductivity gradient parameter; the method comprises the following steps: driving an electromagnetic coupling array by a multi-band excitation parameter combination, collecting an electromagnetic response spectrum of a target conductive area in real time, extracting an eddy current characteristic component set, calculating an obtained phase compensation amount, and dynamically adjusting an excitation phase difference and a power ratio of the electromagnetic coupling array to form a controllable standing wave interference field; and solving the Lyapunov function gradient difference between the node distribution characteristics of the standing wave interference field and the predicted value of the three-dimensional dynamic electromagnetic model so as to iteratively update the multi-band excitation parameter combination. According to the invention, high-precision dynamic regulation and control of the conductivity gradient of the aluminum tube are realized, the energy consumption is effectively reduced, and the stability of a standing wave interference field is enhanced.

The invention belongs to the field of complex network synchronous control, and discloses a complex network fixed time event trigger pinning synchronous control method based on aperiodic intermittent control, which aims at solving the problem that a pinning strategy of a traditional fixed pinning node set requires that the node set is fixed and cannot flexibly change along with a network state. Constructing a dynamical model of the master system, the slave system and the synchronization error; designing a fixed-time non-periodic intermittent containment controller based on an event triggering containment mechanism; constructing a Lyapunov function, and solving the synchronization completion time of the complex network; the controller is applied to the slave system, so that the synchronization error between the slave system and the master system is reduced to 0 within the fixed time, and the fixed time synchronization control of the complex network is completed; according to the method, the control condition is controlled based on event triggering, the communication cost is reduced, and meanwhile, the non-periodic intermittent control of the complex network is completed within fixed time. The effectiveness of the control method is verified through numerical simulation.

The invention relates to the technical field of unmanned aerial vehicle group intelligent control, in particular to a multi-agent unmanned aerial vehicle cooperative control method and system, and the method comprises the steps: obtaining multi-sensor data to estimate a relative pose, generating an environment occupation model and a path homotopy identifier, and building an adjacent set; based on a control barrier function and a control Lyapunov function, writing security and convergence constraints into a quadratic programming solution control instruction, and exporting a constraint dual multiplier, a barrier redundancy and a conditional risk value; constructing a task graph, carrying out bidding distribution according to energy consumption, time, safety cost and risk cost, generating a task intention identifier and taking the task intention identifier as a nominal reference trajectory; and triggering minimum change control correction and local redistribution through a sequential logic monitoring completion condition, a barrier redundancy and a synchronous residual error based on adjacency set evaluation. According to the invention, stable, safe and risk-controllable group collaboration is realized in a dense environment.

The invention provides a distributed driving vehicle preset performance fault-tolerant control method and system based on an adaptive neural network observer. The method comprises the following steps: constructing a vehicle dynamics model containing motor faults and unknown disturbance; in order to realize real-time estimation of faults and disturbance and break through the limitation on disturbance derivative boundaries, a self-adaptive neural network observer is designed; aiming at the problem that the control direction is uncertain, an upper-layer controller is designed based on a preset performance control theory, a Nussbaum function and an obstacle Lyapunov function are introduced, it is ensured that system dynamic errors meet preset performance constraints all the time, and accurate tracking control over the transverse force, the longitudinal force and the yawing moment of a vehicle is achieved; in order to improve the system coordination capability under the multi-motor fault working condition, an optimal torque distribution model fusing the motor torque constraint and the tire force limit is constructed, collaborative optimization control of the driving and steering system is realized through a quadratic programming algorithm, and the stability and safety of the whole vehicle in the fault state are improved.