Adaptive Neural Network Time-varying Formation Tracking Control for Multi-agent Systems via Minimal Learning Parameter Approach

This paper investigates the time-varying formation tracking control problem for multi-agent systems with consideration of model uncertainties. For each dimension of an agent, a radial basis function neural network (RBFNN) is first adopted to approximate the model uncertainties online. Taking the square of the norm of the neural network weight vector as a newly developed adaptive parameter, a novel RBFNN-based adaptive control law with minimal learning parameter (MLP) approach is then constructed to tackle the time-varying formation tracking problem. The uniformly ultimately boundedness (UUB) of formation tracking errors is guaranteed through Lyapunov analysis. Compared with other traditional RBFNN-based formation tracking control laws for multi-agent systems, very few parameters need to be updated online in our proposed one, which can greatly lessen the computational burden. Finally, comparative simulation results demonstrate the effectiveness and superiority of the proposed adaptive control law.