Intelligent Adaptive Asymptotic Tracking Control Algorithms For Stochastic Nonlinear Systems

Linear control theories play an important role in early industrial platforms.It can control a linear system to a stable state,or control a linear system output to achieve a specified performance.In today’s society,many practical physical models can be modeled as nonlinear systems with unknown control gains,such as aerospace systems,robotic systems,etc.However,previous linear control methods can not be used for control design and stability analysis for nonlinear systems,which brings a serious challenge to the design of related controllers.With the rapid development of intelligent information processing and science and technology,the performance of systems with uncertainties,nonlinearities and external stochastic perturbations is required to be higher and higher,which requires researchers to conduct more in-depth research on control theories and intelligent algorithms.Based on the above problems,this thesis studies the intelligent adaptive asymptotic tracking control problems for several classes of stochastic nonlinear systems.The research contents are as follows:(1)The issue of intelligent adaptive asymptotic tracking control for a class of single-input single-output(SISO)stochastic nonlinear systems with unknown control gains and full state constraints.Unlike the existing works(Only known control gains are considered in the existing works),and the introduced new gain suppression inequality technology solve the design obstacle arised from unknown control gains.At the same time,Barrier Lyapunov functions(BLFs)are designed to avoid the violation of the state constraints.Then,a new adaptive controller is designed,not only can realize the tracking error converge to zero in probability,but also meet the requirement of the full state constraints imposed on the system.In the end,the simulation results for a chemical reactor system demonstrate the feasibility of the proposed control algorithm.(2)The issue of intelligent adaptive asymptotic tracking control for a class of multiple-input multiple-output(MIMO)non-strict-feedback stochastic nonlinear systems with unknown control gains and full state constraints.BLFs are designed to avoid the violation of the state constraints,and the MIMO non-strict-feedback design difficulties have been solved by the properties of neural networks.In addition,the dynamic surface control(DSC)method is adopted to ensure the computation burden is greatly reduced,and the introduced auxiliary virtual controllers solve the design obstacle arised from unknown control gains.More importantly,different from the previous works that only dealt with the bounded tracking control problem,one of the advantages of the proposed algorithm is that the asymptotic tracking control issue is achieved.In the end,the simulation results for a pendulum system demonstrate the feasibility of the proposed control algorithm.(3)The issue of intelligent adaptive asymptotic tracking control for a class of strong interconnected stochastic nonlinear systems with unknown control gains and time-varying full-state constraints.Compared with a large number of existing works focused on the stochastic nonlinear systems with known control gains,this thesis frames a control algorithm design process for a more general class of stochastic strong interconnected nonlinear systems with unknown gains.The time-varying(TV)BLFs are taken into account to deal with the design difficulties of time-varying full-state constraints,and the event-triggered control strategy is applied such that the desired controller is constructed to reduce the communication burden.The designed algorithm shows that all signals are bounded in probability without the violation of full-state constraints,and the tracking error converge to zero in probability.In the end,the simulation results for a pendulum system demonstrate the feasibility of the proposed control algorithm.