Study On Command Filter Backstepping Control For Markov Jump Nonlinear System

With the vigorous development of information technology,as one of the core theories of intelligent automation technology,control science must also be comprehensively and deeply studied.Considering the complexity and variability of the actual industrial control systems,the system model parameters or structure can change accordingly.Therefore,there are some limitations in the single mode system control design and research of the controlled object,and the switched system modeling can solve this problem well.Markov jumping process is a random process.For some actual control systems with mode switching caused by the unknown random factors,Markov jumping nonlinear systems can model and analyze them well.In this paper,the tracking control problem of Markov jumping nonlinear systems is studied.For the case that the system states are not measurable,an observer based adaptive finite-time control scheme is proposed.For the control systems with external disturbances,an error-based gainvarying backstepping control strategy is proposed.In addition,for the communication resource waste caused by frequent updates of system controller,the event-triggered control is used to reduce the resource consumption.The main contents of the paper are summarized as follows:1.For a class of Markov jumping nonlinear systems with unmeasurable states,an adaptive finite-time tracking control scheme based on state observer is proposed.A fuzzy state observer is combined with the command filtered backstepping technology,where the command filters solve the computational explosion problem of the classical backstepping,the error compensation mechanism can weaken the adverse effects caused by the filtering errors,the fuzzy logic system-based adaptive technology approximates the unknown dynamic functions,and the fuzzy state observer uses the output information to reconstruct the system state.The mean-square stability theory is used to make the tracking error converge to a small neighborhood of the origin in finite time.The effectiveness of the control scheme is verified by simulation.2.For a class of nonlinear systems with external disturbances,an error-based gainvarying backstepping control strategy is proposed.A series of error-based gain-varying functions are constructed,and the properly constructed gain-varying functions are combined with the static gains at the corresponding positions of the virtual control signals,controller and compensation mechanism,thus establishing the relationship between error information and control gains,effectively enhancing the anti-disturbance ability and flexibility of the closedloop disturbed nonlinear system,and the system tracking error can converge to a small neighborhood of the origin.The effectiveness of the control scheme is verified by simulation.3.For a class of uncertain disturbed Markov jumping nonlinear systems,an eventtriggered adaptive gain-varying control scheme is proposed.Using neural network adaptive technology to approximate the unknown nonlinear functions of the system,further considering the waste of communication resources caused by frequent updates of the system controller,an event-triggered adaptive gain-varying control scheme is constructed,and the relative threshold event-triggered control technology used effectively reduces the update frequency of the controller.The command filtered backstepping and finite-time control technology are combined,even if there is a strong external disturbance,the system tracking error can still be maintained in a small neighborhood of the origin under the gain-varying control scheme.Finally,the simulation verifies the effectiveness of the constructed control scheme.