| Adaptive tracking control of nonlinear systems has been widely used in industry,military and other fields.Therefore,adaptive tracking control of nonlinear systems has been a research hotspot in the control field in the past decades.With the development of science and technology and the improvement of control performance requirements,some traditional control theories and methods have been unable to meet the needs of modern social development.Inspired by previous work,this thesis conducts in-depth research on adaptive tracking control of several kinds of stochastic nonlinear systems.The main work is as follows:1.Two prescribed performance control algorithms for stochastic non-triangular structure nonlinear systems are presented.An adaptive neural prescribed performance control algorithm is proposed for stochastic non-triangular structure nonlinear time-delay systems with unmodeled dynamics.Firstly,the differential mean value theorem is employed to convert the non-triangular structure systems to the equivalent systems with affine structure.Then,a prescribed performance function is designed to ensure that the system achieve a predetermined tracking accuracy within a specified time.Finally,combining neural network approximation theory and backstepping technology,an adaptive tracking control algorithm is designed,which can ensure that the system achieve a predetermined tracking accuracy within a specified time,and the design process of the controller is simpler than that based on the finite time stability theorem.In order to reduce the communication burden caused by redundant data in the time-triggered control methods,combining prescribed performance function,fuzzy logic system approximation theory and event-triggered control technology with an improved event triggering mechanism,an event-triggered adaptive fuzzy tracking control algorithm for stochastic non-triangular structure nonlinear systems is presented,which can not only ensure that the system achieve a predetermined tracking accuracy within a specified time,but also save network resources effectively.2.Two fixed-time tracking control algorithms for stochastic non-triangular structure nonlinear systems are presented.An event-triggered fixed-time adaptive neural tracking control algorithm is presented for stochastic non-triangular structure nonlinear systems.Firstly,the practical fixed time stability theorem of stochastic nonlinear systems is proposed.Then,combining backstepping technology and event-triggered control technology,an adaptive neural tracking control algorithm is designed,which removes the limitation that the settling time depends on the initial state of the system in the existing finite-time control method.Finally,theoretical proof and simulation examples verify that the proposed algorithm can ensure that all closed-loop signals are fixed-time bounded in probability,and the system output can track the reference signal within a fixed time.In order to deal with the problems of “complexity of explosion” and“singularity” caused by repeated derivative of virtual control function in backstepping method,combining neural network approximation theory and fixed time dynamic surface control technology with a fixed time filter error compensation signal,an event-triggered fixed-time adaptive neural dynamic surface control algorithm for stochastic non-triangular structure nonlinear systems is presented,which not only ensures the fixed time stability of the closed-loop system,but also avoids the problems of “complexity of explosion” and “singularity” in the traditional methods.3.An event-triggered fixed-time adaptive fuzzy control algorithm for stateconstrained stochastic nonlinear systems without feasibility conditions is presented.Firstly,a nonlinear state-dependent function that purely rely on the system state is designed to deal with the asymmetric time-varying state constraints.Then,combining fixed-time stability theorem and dynamic surface control technique,an adaptive dynamic surface control algorithm is designed,which removes the limitation that the virtual control function depends on the feasibility condition in the traditional methods.Finally,theoretical proof and simulation examples verify that the proposed algorithm can ensure the fixed time stability of the closed-loop system without violating the state constraints.4.A decentralized fixed-time control algorithm for state-constrained stochastic interconnected nonlinear systems is presented.Firstly,a nonlinear state-independent function is introduced to deal with the asymmetric time-varying state constraints.Then,a smooth function is designed to deal with the unknown interconnections.Finally,combining the fixed time stability theorem and the dynamic surface control technology,a decentralized fixed time control algorithm is designed,which ensures the fixed time stability of the closed-loop system,and each subsystem has good tracking performance without violating the state constraints.5.A unified fuzzy control approach for stochastic high-order nonlinear systems with or without state constraints is presented.Firstly,by designing two universal-constrained functions and using coordinate transformation technology,the stochastic high-order nonlinear system with full-state constraints is transformed into an equivalent one without state constraints.Then,an adaptive event triggering mechanism that the threshold parameters can be adjusted adaptively according to the tracking error is designed to save network resources.Then,combining fixed-time stability theorem and backstepping technology,an adaptive fuzzy control algorithm is designed,which ensures the fixed time stability of the closed-loop system without violating the state constraints,and the limitations that the virtual control function depends on the feasible conditions and the constraint function need to be bounded in the traditional methods are removed. |
