Distributed Adaptive Prescribed Performance Control for Interconnected Euler–Lagrange Systems Under Input Constraints

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This paper proposes a novel distributed adaptive Prescribed Performance Control (PPC) scheme with low complexity for interconnected multi-input multi-output (MIMO) Euler-Lagrange systems subject to structural uncertainty in dynamics, input saturation, and external disturbances without employing any approximation structures. In addition to the coupling caused by the control protocol, uncertain nonlinear interconnection terms intrinsically existing in the dynamics are also considered. The control scheme is implemented in a distributed manner among multiple agents, utilizing only local information. Adaptive control tools are employed to introduce flexible prescribed performance functions, which serve as a trade-off between input and output constraints, effectively dampening the input saturation and eliminating the need for an auxiliary system. The proposed approach is analyzed using Lyapunov techniques, and is validated by simulation results on a leader-follower multi-agent trajectory tracking control problem.