Distributed adaptive iterative learning control for nonlinear multiagent systems with state constraints

This paper addresses the consensus problem of nonlinear multiagent system with state constraints. A novel γ-type barrier Lyapunov function is adopted to handle with the bounded constraints. The iterative learning control strategy is introduced to estimate the unknown parameter and basic control signal. Five control schemes are designed, in turn, to address the consensus problem comprehensively from both theoretical and practical viewpoints. These schemes include the original adaptive scheme, projection-based scheme, smooth function-based scheme and its alternative, and dead-zone–like scheme. The consensus convergence and constraints guarantee are strictly proved for each control scheme by using the barrier composite energy function approach. Illustrative simulations verify the theoretical analysis.