Research On Event Trigger Control Of Dynamical Systems Based On Fuzzy Theory

Due to the rapid development of sensing and communication technology,information can be transmitted through shared digital networks or wireless channels.Systems with this architecture are called networked control systems.Compared with traditional control systems,networked control systems have significant advantages in the following aspects: low cost,high flexibility,easy reconfiguration,strong reliability,strong adaptability,and robustness to failures.From the definition of a networked control system,we can see that its most notable feature is the exchange of information between the controller and the system through a shared communication network.Therefore,some communication problems in the network may cause the performance of the system to decrease.These problems mainly include: network-induced delay,data packet loss,limited channel capacity,and network security issues.The research of networked control systems is mainly to solve the above-mentioned problems.This dissertation is devoted to further improve and supplement the research of networked control system.Based on fuzzy set theory,Lyapunov stability theory,special inequality scaling and linear matrix inequality,the stability performance and event triggering control of linear and nonlinear networked control systems,and the synchronization analysis and event triggering control of neural networks with delay are studied.The specific research content of this dissertation is summarized as follows:1.The asymptotic stability problem for a class of linear networked control systems with actuator faults is studied.In order to save network communication resources,a new sum-based discrete event triggering mechanism is constructed.A dynamic output feedback control based on observer is designed to solve the problem that the state of linear networked control system is not completely known.Based on Lyapunov stability theory and special inequality scaling technique,the asymptotic stability criterion of linear networked control systems considering actuator faults is established.The accuracy and validity of the results are verified by simulation.2.The problem of sum-based discrete event triggering control is studied in nonlinear networked control systems.The interval type 2 Takagi-Sugeno fuzzy model is used to approximate the nonlinear networked control system model.A novel event trigger mechanism and dynamic output feedback controller are constructed to realize both system stability and network resource saving when system state information is not completely known.Based on Lyapunov stability theory,the stability criterion of interval type 2 fuzzy networked control system with dynamic output feedback controller is given.Simulation experiments of mass spring damping system and tunnel diode circuit show that the new method has both reliability and superiority.3.The synchronization and controller design of fuzzy neural networks with deception attacks is studied.For nonlinear networked control systems with deception attacks,a Markov stochastic process which is more general than Bernoulli stochastic process is used.A dynamic event triggering mechanism based on summation is designed,which uses historical sampling measurements and internal dynamic variables to determine the next triggering moment.A dynamic output feedback controller is constructed to ensure the stability of the system.Based on Lyapunov stability theory and linear matrix inequality theory,the synchronization criterion of fuzzy neural networks with deception attack is constructed.Two numerical examples are given to illustrate the effectiveness and superiority of the obtained results.4.The synchronization problem of fuzzy neural networks with mixed time delays and deception attacks is studied.For fuzzy neural networks,a dynamic summing event triggering mechanism is designed,which contains the historical sampling information of weight summing and internal dynamic functions.To ensure the stability of the system,a dynamic output feedback controller is designed.Considering the more general network structure,the communication channels from sensor to controller,controller to actuator are all affected by weighted sum-based dynamic event triggering mechanism,deception attack and quantizer.A synchronization criterion for fuzzy neural networks with mixed time delays and deception attacks is established by using stability analysis theory.The effectiveness and superiority of the conclusion are verified by two numerical examples with simulation.5.The finite time bounded control problem for a class of nonlinear networked control systems with mixed delays and deception attacks is studied.A new event triggering mechanism is constructed by considering weighted historical sampling information and internal dynamic functions in event triggering conditions.A dynamic output feedback controller is constructed to deal with the condition that the system state information is not completely known.Based on Lyapunov stability theory and cone-complement linearization algorithm,the gain of dynamic output feedback controller and event trigger parameters are designed and the finite time bounded criterion for nonlinear networked control systems with mixed delay and deception attack is presented.The experimental results of mass spring damping system not only proves the effectiveness of the proposed method,but also verifies its superiority by comparison.