Cooperative Tracking Control for Multiple Robots Based on self-structuring neural networks

Abstract

In this paper, a cooperative tracking control strategy is proposed for multiple robots with uncertainties. The barrier Lyapunov function (BLF) is adopted to solve the problem of time-varying consistency error constraints and improve the robustness of the cooperative control. The self-structured method combined with the adaptive neural network (NN) is used to estimate the complex uncertainty dynamics and avoid the excessive computational burden. Under the proposed control strategy, the consistency error does not violate the predefined time-varying constraint bounds, and the asymptotic cooperative tracking can be achieved. All signals of the closed-loop system are proved to be bounded via Lyapunov analysis. The effectiveness of the proposed method is demonstrated by simulation results.