Distributed Adaptive Asymptotic Consensus Tracking Control for Stochastic Nonlinear MASs With Unknown Control Gains and Output Constraints

This paper studies the asymptotic consensus tracking control problem for a class of stochastic nonlinear multiagent systems (MASs) with output constraints and unknown control gains. Firstly, the Nussbaum technique is introduced to solve the difficulty of the unknown control gains in the stochastic nonlinear MASs. Meanwhile, a $ tan$ -type nonlinear mapping (NM) function is used to ensure that the output of each agent satisfies the predefined output constraints. Furthermore, the “explosion of complexity” problem caused by the traditional backstepping design methods is handled by using the command filter technique. The developed distributed adaptive asymptotic consensus tracking control strategy ensures that all the signals in the closed-loop system are bounded in probability and the consensus tracking errors of all agents converge to zero in probability. Finally, a simulation example proves the effectiveness of the proposed control strategy. Note to Practitioners —In this paper, the asymptotic consensus tracking control problem is studied for a class of the stochastic nonlinear MASs. In nature, there are many meaningful movements of multi-agents with stochastic disturbances. It is particularly challenging to achieve the asymptotic consensus tracking control problem for stochastic nonlinear MASs, which involves the unknown control gains and output constraints. Therefore, the Nussbaum technique is used to solve the difficulty of the unknown control gains, meanwhile the command filter technique is introduce to solve the “explosion of complexity” problem in the backstepping design process. Moreover, the designed control strategy and stability analysis for the studied system is based on the nonlinear mapping technique and Lyapunov method, which makes the developed methodology more engineering-oriented.