Intelligent Tracking Algorithm Based On Nussbaum Functions For Stochastic Pure-feedback Nonlinear Models

Stochastic system,as a kind of system model with stochastic disturbance,provides more choice for precise modeling various practical industrial operation processes.Therefore,the study of stochastic systems has important theoretical research value and practical application value.Theoretically,the existence of stochastic perturbations in stochastic systems increases the difficulty of controlling and analyzing the stability of stochastic systems,so it is a challenging task to design an effective control algorithm for stochastic systems.On the other hand,stochastic nonlinear systems with nonaffine structures are common in practical engineering systems such as power systems,communication systems,dynamic communication systems,and intelligent homes.Therefore,the study of stochastic pure feedback nonlinear systems is of more practical significance.Based on the above discussion,by applying graph theory knowledge,backstepping method,dynamic surface technique,and neural network technique,this paper studies the adaptive intelligent asymptotic tracking control problem of two kinds of stochastic pure feedback nonlinear systems based on Nussbaum function.Specific work are as follows:(1)The adaptive intelligent asymptotic tracking control problem for a class of stochastic pure-feedback nonlinear systems with strong interconnection terms is studied.Although stochastic nonlinear systems have been extensively studied in the existing literature,how to design effective and resource-saving adaptive intelligent controllers for stochastic nonlinear systems under the influence of nonaffine structure and strong interconnection terms is still worth further research.Therefore,to design the desired controller,this paper first uses the mean value theorem to re-model the stochastic nonlinear system with nonaffine structure in the process of backward recursive design.Secondly,the properties of neural networks are used to approximate the unknown nonlinear functions in nonlinear systems.Then,the problem of the unknown control gain is solved by using the introduced Nussbaum function,and the tracking error of the stochastic connected pure-feedback nonlinear system converges to zero asymptotically in probability.In addition,to solve the problem of resource waste caused by the communication between the controller and the actuator,the event-triggered control strategy is applied to the design process of adaptive intelligent controller for the stochastic interconnected pure-feedback nonlinear system,which greatly saves the system resources.Finally,through simulation experiments,it is proved that all signals in the closed-loop system are bounded in probability,and the tracking error converges to zero in probability,thus verifying the effectiveness of the proposed control algorithm.(2)The adaptive bipartite asymptotic tracking control problem for a class of stochastic pure-feedback nonlinear multi-agent systems based on an unbalanced sign graph structure is studied.With the rapid development of the Internet,multi-agent systems are more and more widely used in daily life.It is of great practical application value to study the stochastic pure-feedback nonlinear multi-agent systems.However,it is still a difficult problem to construct a reasonable adaptive intelligent controller and realize the bipartite asymptotic tracking control under the unbalanced symbol graph.Therefore,an agent classification optimization strategy is proposed in this paper,which makes the application of bipartite asymptotic tracking control more extensive.In the process of designing an adaptive controller by the backstepping method,the mean value theorem is used to solve the design problem of nonaffine structure of the stochastic nonlinear system,and the properties of the neural network are used to approximate the unknown nonlinear function.At the same time,to solve the complexity explosion problem caused by the derivation of virtual control,dynamic surface technology is introduced,which greatly reduces the computational burden.In addition,an adaptive controller based on the switching threshold is constructed to avoid excessive triggering between the controller and the actuator,thus reducing the waste of unnecessary resources.The algorithm designed in this paper ensures that all signals in the closed-loop system are bounded in probability,and the tracking error converges to zero in probability.Finally,the effectiveness of the proposed control algorithm is verified by simulation experiments.This paper presents a preliminary study on the adaptive intelligent asymptotic tracking control of stochastic interconnected pure-feedback nonlinear systems and stochastic pure-feedback nonlinear multi-agent systems.A reasonable and effective controller is designed to achieve the desired control objectives.At present,all kinds of related control problems of this kind of system still need to be explored in depth,such as the optimization control problem of the system,and security communication problem.