Adaptive Control Of High-order Stochastic Nonlinear Multi-agent Systems

In recent years,there has been an upsurge of research on multi-agent systems.Under the background of industry,more and more researches have been done on nonlinear multi-agent systems.For the problem of controller design and stability of nonlinear multi-agent systems with leadership,abundant theoretical results have been obtained.It is inevitable that there will be stochastic noises in the industrial process,but there is not enough theoretical research on the nonlinear multi-agent system with stochastic noises.This paper studies the controller design and tracking performance analysis of two important stochastic nonlinear multi-agent systems.The main contents are as follows: 1.Adaptive tracking control for high order stochastic nonlinear multi-agent systemsFor the unknown nonlinear function,the radial basis function neural network is used to approximate it,and the output feedback controller is designed by the adaptive backstepping method.By selecting proper parameters,it is proved that the adaptive controller designed in this paper can realize that the output tracking error of the system converges to a small neighborhood around the origin,and that all states of the system are bounded,so are the adaptive parameter.The Matlab simulation further proves the effectiveness of the designed controller.2.Adaptive fuzzy tracking control for a class of non-affine stochastic nonlinear high-order multi-agent systems with non-lower triangular structureFuzzy logic system and mean value theorem are used to overcome the design difficulties in unknown nonlinear and non-affine structures,respectively.A direct adaptive fuzzy control method is proposed by using adaptive control and backstepping method.According to Lyapunov stability theory,the proposed controller can ensure that all signals in the closed-loop system are bounded in the sense of mean quartic value,and the output signals of the subsystems can track the output signal of the leader well.Simulation results show the effectiveness of the proposed control scheme.