Distributed adaptive tracking control of nonlinear multi-agent systems with input saturation

This paper comes up with a distributed consensus tracking control strategy for second-order agents with input saturation in the presence of nonlinear model uncertainties. The control protocol is composed of two parts: a nearest- neighbor rule based adaptive controller to conduct the convergence with smooth motion paths, and a neural network (NN) term to compensate the nonlinear components in the agent’s dynamics. Additionally, actuator saturation is considered and applied by adding saturation functions in each component of the control input which is finally limited to a measurable value. Efforts are made to provide that the input saturation does not affect the achievement of the consensus tracking based on the Lyapunov stability theories. Simulation results show that , under the proposed control law with input saturation, the tracking errors converge to zero, and the NN estimation errors are uniformly ultimately bounded.