146 results about "Linear matrix inequality" patented technology

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 discloses a data-driven event trigger control method for a linear variable-parameter nonlinear system, which comprises the following steps: for a discrete-time nonlinear system with an unknown model, expressing the nonlinear system as a parameter-dependent linear form by using a linear variable-parameter embedding method; constructing a convex polyhedron-based noise matrix set for unknown bounded noise in the measurement data, and proposing polyhedral data-driven LPV system representation by using offline collected state, input and scheduling variable data; designing a bounded dynamic event trigger transmission mechanism, and optimizing the use of communication resources by adjusting a trigger threshold in real time and setting the upper and lower bounds of a transmission interval; based on a discrete time cycle functional analysis method, controller gain and trigger parameters are jointly designed through a linear matrix inequality tool. The method does not depend on an accurate system model, stable control over a nonlinear system is achieved only through historical data, the communication frequency is remarkably reduced, and meanwhile the system performance and robustness are guaranteed.

The invention relates to the technical field of industrial robot control, and discloses a single-connecting-rod mechanical arm system fault-tolerant asynchronous control method based on double-variable decision, which comprises the following steps: establishing a semi-Markov jump system dynamic model for describing the operating characteristics of a single-connecting-rod mechanical arm; based on the dynamic model, designing a fault-tolerant asynchronous controller structure based on a double-variable decision and an experience-machine cooperative operation strategy; establishing sufficient conditions which enable the closed-loop system to be randomly stable and meet H infinity performance indexes; and the numerical value of the gain of the machine controller is obtained by solving the linear matrix inequality set, and a final control law is generated to conduct fault-tolerant asynchronous control on the single-connecting-rod mechanical arm. According to the fault-tolerant asynchronous control method for the mechanical arm, fault-tolerant asynchronous control of the mechanical arm is achieved through a double-variable decision and an experience-machine cooperation strategy in combination with modal transfer rate boundary processing, and the random stability and the H-infinity performance of the system are guaranteed under the modal mismatching and fault working conditions.

The invention discloses a dynamic self-triggering filtering method under incomplete semi-Markov kernel information, relates to the technical field of automatic control and signal processing, and aims to solve the problems that full acquisition of semi-Markov kernel information is difficult, communication burden is heavy and peak control is insufficient. A self-triggering mechanism containing a dynamic variable is designed to determine a data transmission moment by classifying a known / unknown set of semi-Markov information and utilizing known parameters and an unknown lower bound, a modal-dependent filter is constructed, and the mean square index stability and robust performance of a filtering error system are verified in combination with the Lyapunov theory and a linear matrix inequality technology. The problems that an existing method is high in conservative property and wastes resources are solved.

The invention provides a time mark event triggering reinforcement learning multi-agent consistency control method and system, and relates to the technical field of multi-agent cooperative control. Establishing a multi-agent system dynamics model comprising a leader and a plurality of followers on the time mark; based on the state information of the leader and the follower, constructing a tracking error and consistency error dynamic equation between the intelligent agents; a reinforcement learning controller based on a neural network is constructed, and an optimal control strategy and a performance index function are approached; an event triggering mechanism is designed, and the controller is triggered to update only when the local error of the intelligent agent exceeds a dynamic threshold value; by constructing a Lyapunov function on a time scale and solving a linear matrix inequality condition, bounded consistency of the system is ensured, and a Zeno phenomenon is eliminated. Unified modeling and distributed safety control of the time scale multi-agent system in the non-ideal environment are achieved, communication resources are saved, the convergence speed is increased, and the robustness and practicability of the system are enhanced.

The invention provides a preset time fuzzy control method for a reentrant manufacturing system under intermittent state feedback, and the method comprises the steps: constructing a nonlinear error dynamic model, and employing a fuzzy modeling technology to represent the system as a combination of a plurality of linear subsystems; introducing a time scaling function, and transforming the error state of the global fuzzy model by using the time scaling function to obtain an error scaling state; an event triggering mechanism is designed to realize intermittent state feedback so as to reduce the communication cost; based on the Lyapunov stability theory, a sufficient condition that the system is stable within preset time is deduced, and a controller gain is obtained by solving a linear matrix inequality of the sufficient condition, so that the system still has robustness under external interference. Agile response and low-communication-cost operation of the reentrant manufacturing system within the preset deadline are realized.

The invention relates to the technical field of multi-agent systems, in particular to a multi-agent system anti-network attack security cooperative control method, which comprises the following steps of: establishing a multi-agent system model which comprises the steps of describing the relation between agent nodes by using a directed graph and establishing an agent state space equation; a proper communication mode is selected, and a dynamic memory event triggering strategy is adopted to optimize communication resource allocation; the method comprises the following steps: constructing a partially unknown semi-Markov switching topology model by adopting a processing technology with Markov switching characteristics; selecting a controller adapted to the multi-agent system model and the security topology requirement, and designing a control protocol in a non-attack scene and an attack scene; and based on the controller, the semi-Markov switching topology model and the communication mode, a linear matrix inequality is obtained, the linear matrix inequality is solved, and anti-network attack security cooperative control of the multi-agent system is realized. According to the invention, high-reliability control of multi-agent cooperation in a complex network is realized.

The invention relates to the technical field of hydroelectric engineering, in particular to a T-S fuzzy robust control method for a hydroelectric generating set adjusting system based on a linear matrix inequality, which comprises the following steps: establishing a system nonlinear mathematical model considering modeling uncertainty and external interference under a rigid water attack condition, and converting the system nonlinear mathematical model into an affine nonlinear state space form; a T-S fuzzy model is constructed, and precise approximation of global nonlinear characteristics is realized through linearization of a linear triangular membership function and representative working condition points; a fuzzy controller is designed based on a parallel distribution compensation strategy, and a robust control problem is converted into a linear matrix inequality form to solve a feedback gain matrix in combination with a Lyapunov stability theory and an H-infinity performance index. The method effectively improves the adaptability of the system to a complex operation environment, enhances the anti-disturbance capability, can significantly reduce the system oscillation, shortens the stabilization time, guarantees the safe and reliable operation of the hydroelectric generating set, and has important engineering application value.

The invention discloses an industrial robot motion pose error calibration method based on constraint solution. The industrial robot motion pose error calibration method comprises the steps that S1, joint angles, tail end coordinates and temperature data of multiple measurement points are collected; s2, correcting the kinematic model by using the thermal elongation and constructing a parameter identification model; s3, constructing a mixed integer nonlinear constraint pool according to mechanical limitation; s4, introducing a semi-definite programming slack variable to convert the non-convex constraint into a linear matrix inequality; s5, combining a primal-dual interior point method and an alternating direction multiplier method to iteratively solve the optimal kinematics parameter correction; s6, introducing a square root information filtering improvement mechanism to correct a state vector square root factor, and calculating an updated nominal kinematics parameter; and S7, based on the condition number of the error Jacobian matrix, judging observability and repeatedly calibrating. According to the method, the problem of precision reduction caused by thermal errors is solved, and the identification precision, the numerical stability and the self-adaptive calibration capability of the kinematics parameters of the robot are improved.

The invention discloses a networked vehicle queue safe cruise method for coping with hybrid network attacks, the vehicle queue comprises a head vehicle and a following vehicle, the following vehicle collects the running state data of the vehicle, obtains the running state data of other vehicles through a communication network, continuously detects the state of the communication network, and sends the detected state to the vehicle queue. Detecting whether a network attack is currently suffered or not and the type of the suffered attack, and calculating the minimum characteristic value of the topological matrix in a normal communication state, a spoofing attack state or a replay attack state according to the identified attack type; and then solving the constructed linear matrix inequality which enables the vehicle closed-loop system to be stable to obtain a feedback gain, and generating a control input signal according to the feedback gain obtained by calculation to realize accurate control of the vehicle. The feedback gain is dynamically adjusted according to the real-time state of the vehicle, so that the safety cruise control of the vehicle queue system is more effectively realized.

The application relates to a sequence-dependent plug-in island direct-current micro-grid distributed control method and system, first, local electrical parameters and interconnection line parameters of each DGU are acquired, an island direct-current micro-grid state space model is established, and a distributed controller model is constructed based on local state variables of the DGU; second, aiming at the access or cut-off request of the target DGU, the cut-off request is equivalently converted into the access request in the sequence-dependent plug-in scene, a to-be-judged insertion sequence or a candidate insertion sub-sequence is generated; finally, a linear matrix inequality constraint is constructed according to a Lyapunov asymptotic stability condition, controller parameters meeting the stability requirement are solved, and pre-stability judgment and parameter optimization of the plug-in operation are realized. The application can effectively guarantee the asymptotic stability of the island direct-current micro-grid in the dynamic plug-in scene, and improve the plug-and-play capability of the system.

The invention discloses an island micro-grid load frequency stability control method considering communication time lag and a virtual inertia link, and the method comprises the steps: firstly building an island micro-grid load frequency control model containing the virtual inertia link, then constructing a Lyapunov functional on the basis, carrying out the derivation of the constructed functional, and carrying out the calculation of the load frequency stability of the island micro-grid. Derivative integral terms are defined by means of a generalized free-weight integral inequality, then a time-delay correlation stability criterion based on a linear matrix inequality is obtained, then the linear matrix inequality under different PI parameter conditions is solved, a maximum allowable time-delay upper limit (MDSM) for ensuring system stability is obtained, the time-delay upper limit is improved, system conservative property is reduced, and the stability of the system is improved. On the basis of the prior art, the robustness of the system is further improved, and the system has a larger stable operation area.

This invention discloses an asynchronous, non-fragile H∞ control method and system for a rotating inverted pendulum, belonging to the field of robust control technology for DC motor rotating inverted pendulums. Addressing the problems of modal asynchrony, controller gain perturbation, time-varying delay, and quantization error in DC motor rotating inverted pendulum systems, this invention proposes an asynchronous, non-fragile H∞ control framework. The main steps include: constructing a Markov jump singular perturbation system model with time-varying delay and variable quantization density; designing an asynchronous, non-fragile output feedback controller for the rotating inverted pendulum, compatible with controller gain perturbation and quantization uncertainty; and deriving the linear matrix inequality conditions through modal-dependent Lyapunov functions to ensure the system's exponential stability in finite time and to meet the preset H∞ performance index. This invention can effectively improve the robustness and stability of DC motor rotating inverted pendulums under attack and parameter perturbation scenarios.

This invention belongs to the field of information security technology, specifically relating to a robust FDI attack detection method for ship navigation systems. The invention constructs a linear discrete-time system model and designs an attack detector based on this model. The detector parameters are optimized using the linear matrix inequality method, and an adaptive detection threshold mechanism based on historical mean and standard deviation is designed. This mechanism can dynamically sense changes in system state, making the dynamic attack detection system asymptotically stable and meeting performance indicators.

The invention discloses a crisscross optimization robust model predictive control method for an automatic sweeping robot. The crisscross optimization robust model predictive control method is adaptive to a trajectory tracking system with parameter uncertainty. According to the method, a discrete time controlled system with parameter uncertainty, a reference trajectory system and a tracking error system are constructed, performance evaluation indexes are defined, the stable control gain range of a basic RMPC controller is determined by solving a minimum and maximum optimization problem and linear matrix inequality constraints, and a multi-objective optimization function is combined in the range to achieve the stable control of the RMPC controller. The optimal additional control gain is iteratively searched by means of a crisscross algorithm, an optimized controller is formed, the stability and the dynamic performance are considered, the influence caused by parameter uncertainty can be overcome, the rising time and the adjusting time of trajectory tracking are remarkably shortened, the tracking error and the energy consumption are reduced, and the tracking efficiency is improved. And the dual requirements of the automatic sweeping robot on real-time response and control precision are met.

The invention belongs to the technical field of micro-grid frequency control, and particularly discloses a time-delay-dependent low-inertia micro-grid frequency control system robust control method, which comprises the following steps: establishing a micro-grid frequency control system model considering time-varying and time-delay influence; constructing a linear matrix inequality condition for ensuring system stability and robust performance, wherein a freestyle matrix is introduced to process a controller gain design dependent on time delay; solving a linear matrix inequality to obtain a secondary frequency controller gain and a virtual inertia controller gain; and applying the secondary frequency controller gain and the virtual inertia controller gain to the micro-grid frequency control system so as to coordinate and control secondary frequency control and virtual inertia control, thereby maintaining the frequency stability of the micro-grid. The robust frequency control method of the time-delay dependent micro-grid is designed under the condition that the overall system coordination, the time-varying and time-delay influence and the coordination of secondary frequency control and virtual inertia control requirements are considered, so that the frequency stability of the micro-grid is maintained.

The invention discloses a load frequency coordination optimization control method, and belongs to the technical field of power system load frequency control technologies. The method comprises the following steps: obtaining a frequency response state space model of a hybrid energy storage-containing two-region interconnected power system through mechanism analysis, performing discretization processing, converting the model into a discretization model, and further establishing a discretization prediction model of the system; analyzing constraints existing in the system, and converting the constraints into a linear matrix inequality; and finally, establishing an objective function of the system, performing online rolling optimization on the control quantity, and designing a multi-region controller, thereby realizing a load frequency coordination optimization control effect of the interconnected power system containing hybrid energy storage. The problems of insufficient flexible resources and poor adjusting characteristics of frequency modulation of an electric power system are solved, coordination and cooperation of battery energy storage, pumped storage and a traditional thermal power generating unit are achieved through coordination control, the frequency response performance of the system is improved, the nonlinear constraint problem existing in the system is solved, and potential safety hazards caused by overload work of equipment are avoided.

The invention provides an ISAC near-field channel robust beam forming optimization method and device based on MMSE estimation. The method comprises the following steps: carrying out system modeling and signal configuration on an ISAC system; accurately modeling a near field communication channel by adopting a spherical wave model, calculating to obtain an SINR (Signal to Interference plus Noise Ratio) of a user, and introducing an outage probability constraint; establishing a sensing model under a far-field condition, and estimating a target response matrix by adopting an MMSE criterion to obtain a sensing MSE; introducing an enhanced waveform to improve the perception DoF, and updating the SINR; an enhanced covariance matrix is introduced, the minimum perception MSE is used as an optimization target, the outage probability constraint, the total power constraint and the positive semi-definite constraint updated according to the updated SINR are used as constraint conditions, and an ISAC near-field channel robust beam forming optimization problem is constructed; a non-convex problem is converted into a convex form through combination of semi-definite relaxation SDR and an optimization framework of a spherical boundary and S-lema, and a beam forming optimization matrix is solved and obtained by obeying the constraint of a linear matrix inequality LMI. According to the invention, beam forming can be optimized.

The invention provides a wind generating set fault diagnosis method and device based on an intermediate observer and a storage medium, and relates to the technical field of wind power generation system fault diagnosis. According to the method, multiple key parameters of the wind generating set are collected and preprocessed, core time-varying parameters are screened to form a set, convex polyhedron vertex matrix parameters are determined based on the set, a convex polyhedron linear variable parameter model is established, an intermediate variable is derived, and the model and the intermediate variable are combined to construct an intermediate observer. Constructing a fault reconstruction model according to the proportion and integral combination of an observer output error, deducing an error system stability condition, converting the error system stability condition into a linear matrix inequality, solving to obtain an observer gain matrix, substituting the gain matrix into an intermediate observer, and outputting a fault estimation value through the observer and the fault reconstruction model. According to the method, the intermediate observer and the convex polyhedron linear variable parameter model are integrated, the dependence of a traditional method on strict state-fault matching is broken through, and the whole flow of fault estimation is standardized.

The present invention relates to a data-driven state feedback control method and device for a periodic piecewise linear circuit system. The method comprises: constructing a periodic piecewise linear closed-loop system model to obtain basic system information; performing input-output experiments on each subinterval to obtain input-output data that satisfies a response rank condition; describing the system dynamics of the linear system on the subinterval based on the input-output data with sufficient excitation, and obtaining the closed-loop characteristics of the system represented by the input-output data; constructing a Lyapunov function of the closed-loop periodic piecewise linear system, and using the Schur complement equivalence to construct the stability condition of the linear matrix inequality for the Lyapunov function; substituting the obtained input-output data into the stability condition to solve the controller gain matrix; and substituting the controller gain matrix back into the closed-loop system model to converge the state variables to an equilibrium point. The present invention adopts a data-driven approach to achieve effective control under unknown system parameters and achieve fast and flexible convergence.

The present application relates to unmanned vehicle safety control method and system resisting hidden false data injection attack. It includes: based on unmanned vehicle information physical system, constructing normal system not attacked and system attacked by hidden executor, constructing difference system with state difference value of two systems; obtaining steady state Kalman filter gain of normal system; constructing linear matrix inequality condition guaranteeing normal system stable and difference system state bounded based on non-escape set, and obtaining controller gain satisfying the condition; according to robust model prediction control, constructing augmented vector and prediction equation, and giving minimum maximum problem of difference system bounded and stable and converting into linear matrix inequality to obtain input signal of difference system, and finally obtaining input signal of attacked system and applying to attacked system, so that difference system is bounded and stable, and stable and safe when attacked. The present application can make the state of attacked system stable and the system safe.

The invention relates to a matrix type differential passivity index determination method and device, equipment and a storage medium. The method comprises the following steps: acquiring a linear system model corresponding to target equipment in a power system, wherein the power system is a multiple-input multiple-output system; determining a corresponding differential passivity equivalent system model according to the linear system model; based on the linear matrix inequality, a matrix type passivity index of the differential passivity equivalent system model is solved, the matrix type passivity index is used as a matrix type differential passivity index of the linear system model, the matrix type differential passivity index is in a matrix form, and the matrix type differential passivity index is used as a differential passivity index of the linear system model; matrix elements in the ith row and the jth column in the matrix represent coupling passivity characteristics between an input channel i and an output channel j in the power system. By adopting the method, the passivity characteristics in the MIMO dynamic system can be represented more accurately and comprehensively.

The application provides a kind of data-driven DC microgrid fault detection method, it is related to the fault detection and safety control technical field of DC microgrid.The application obtains input and output data by system operation under the condition that the parameters of microgrid system are unknown, and constructs unknown input observer to carry out fault detection.Compared with single system, the application estimates the interconnection item that cannot be measured in the data-driven framework, and identifies the system matrix.In addition, on the basis of designing unknown input observer, the design of observer is converted into solving linear matrix inequality, and the robust performance index and sensitive performance index are considered, and finally the fault detection of each DC motor is realized.The detector designed by the application can still complete the detection task under the condition that only input and output data are obtained.In addition, the method proposed by the application uses a large amount of system operation data, and can be put into use online by programming directly on the computer, and the cost is very low.

The application discloses a vehicle lateral control method and device, a vehicle and a medium. The method comprises the following steps: acquiring a general vehicle model constructed in advance; acquiring configuration parameters of a target vehicle, inputting the configuration parameters into the general vehicle model to obtain a target vehicle model corresponding to the target vehicle; constructing a linear matrix inequality according to the target vehicle model and a preset robustness expectation index, and solving the linear matrix inequality to obtain an LPV controller corresponding to the target vehicle; acquiring driving parameters of the target vehicle and inputting the driving parameters into the LPV controller to output a front wheel steering angle of the target vehicle, and converting the front wheel steering angle into a steering wheel steering angle of the target vehicle to perform lateral control on the target vehicle. The application can improve the lateral control performance of the vehicle under different road conditions and improve the stability of the lateral control of the vehicle by designing the LPV controller to perform lateral control on the target vehicle.

The invention discloses a positioning error tolerant unmanned aerial vehicle communication deployment method and system, and belongs to the technical field of wireless communication, and the method comprises the steps: quantifying the spatial positions of an unmanned aerial vehicle and a ground user in a three-dimensional rectangular coordinate system; the channel capacity of each link is quantitatively analyzed based on the Shannon theorem, and the user rate reliability is introduced into a network average transmission rate maximization framework in a probability constraint form. Further, the Bernstein inequality is utilized to convert the probability guarantee constraint which is difficult to directly solve into a deterministic linear matrix inequality related to the positioning error covariance, and a solvable deterministic problem is constructed. Then, an iterative algorithm based on continuous convex approximation is designed, and a non-convex part is linearized through first-order Taylor expansion, so that the model is rapidly converged. According to the method, the high transmission rate can be stably supported within the preset error range, and the disturbance rejection capability and the spectrum utilization efficiency of the system are effectively enhanced.

The invention discloses a state estimation method for a denial of service attack Lipschitz nonlinear system, which comprises the following steps of: aiming at DoS attack possibly occurring in network transmission and gain perturbation during implementation of an estimator, firstly constructing an event triggering communication mechanism, and only sending sampling data when triggering error energy exceeds a set threshold value; then, Bernoulli distribution is adopted to model whether single data transmission is attacked and blocked or not, and a non-fragile state estimator containing additive perturbation is designed; a state estimation error system is defined, a sufficiency matrix inequality which guarantees random stability and has H infinity performance is derived based on the Lyapunov stability theory, then the sufficiency matrix inequality is converted into a linear matrix inequality, an event triggering weight matrix and estimator gain parameters are solved at a time, and joint design of a triggering condition and estimator gain is achieved. Simulation shows that even under the condition that DoS attack and gain perturbation exist at the same time, the method can still accurately and rapidly estimate the system state, and the non-vulnerability of the system and the network resource utilization rate are remarkably improved.

The application discloses a kind of soft package battery fault detection method and system based on adaptive neural network, constructs the battery temperature spatial distribution model considering thermal anomaly and sensor fault, utilizes auxiliary function decomposition method to make non-homogeneous boundary condition homogeneous, establishes ODE dimension reduction system in combination with Galerkin method;Collect battery surface temperature, current and terminal voltage data, design stable parameters through linear matrix inequality, estimate nonlinear heat generation item using neural network observer with adaptive weight matrix, and construct error system;Introduce lumped residual evaluation mechanism, generate dynamic fault threshold based on kernel density estimation technology, complete online detection by comparing residual evaluation value of output estimation error with threshold value in real time.Break through the limitation of traditional lumped parameter model, only a small amount of thermocouple is needed to realize the distributed thermal fault detection of two-dimensional soft package battery, while ensuring accuracy, significantly reduce hardware cost, provide a new technical path for power battery safety management.

The application discloses an output feedback control method for a mechanical arm against random jump interference, firstly establishes a discrete-time mechanical arm Markov jump linear system model, analyzes the discrete-time mechanical arm system model based on the Markov jump process, then designs a state observer and a measured disturbance observer, constructs a compound output feedback controller, estimates the system state to realize state feedback control, and finally guarantees the finite time boundedness and performance of the system through a Lyapunov method and linear matrix inequality technology. The application solves the problem of not considering the influence of the measured interference on the mechanical arm system in the prior art, thereby causing poor stability and control performance of the designed scheme.

The invention discloses a construction and control method of a land pollution abatement system, and belongs to the technical field of environmental pollution abatement, and the method comprises the steps: constructing a land pollution abatement model described by a singular partial differential equation; the model is converted into a control system with a movable sprayer as an original point through coordinate transformation; carrying out approximate linearization on a nonlinear term in the system by utilizing a T-S fuzzy method, and designing an interval observer and a controller capable of estimating upper and lower bounds of a system state based on the approximate linearization; the bounded tolerance of the system is analyzed based on the Lyapunov stability theory, the gain of an interval observer and the gain of a controller are obtained by solving a linear matrix inequality, and the gains are applied to the designed observer and the designed controller to complete closed-loop control. According to the method, unknown soil conditions and external interference can be effectively handled, the control robustness is ensured through interval estimation, the limitation that a traditional model only depends on a partial differential equation is avoided, and accurate and stable treatment on polluted land is achieved.

The invention discloses a data-driven unmanned aerial vehicle trajectory tracking control method based on probability selection event triggering, and belongs to the technical field of unmanned aerial vehicle intelligent control. According to the method, under the conditions of an unknown accurate dynamic model and existence of noise, input and output data of an unmanned aerial vehicle are collected offline to construct data driving representation of an error system, a traditional event triggering mechanism is introduced to obtain triggering packets, the continuous triggering packets are divided into groups, probability selection is performed by using Bernoulli random variables, and the probability of the error system is calculated. Only the real transport packets in each packet are sent, thereby forming a probabilistic selection event trigger mechanism. And on the basis, establishing a linear matrix inequality which only depends on data, cooperatively designing controller gain and event triggering parameters, and realizing trajectory tracking control of the four-rotor unmanned aerial vehicle according to the control law during online operation. According to the method, the data release rate and the communication load are remarkably reduced on the premise of ensuring the tracking performance and the mean square stability, and the method is suitable for unmanned aerial vehicle application scenes with limited communication resources.