摘要：AbstractA state-constrained adaptive control synthesis is presented in this paper for multi-input multi-output Euler-Lagrange nonlinear systems associated with structured uncertainties. The controller is synthesized in two steps: (i) an approximated system is constructed to approximate model uncertainties (ii) a novel nonlinear error transformation based control law is designed to ensure the desired reference command tracking. A neural network is used in the approximated system to approximate the model uncertainties, and the weights of the neural network are updated using a stable weight update rule. The proposed controller ensures that the closed-loop states of the system will remain bounded by the user-defined constraints and the steady-state errors will converge asymptotically to a predefined domain. The proposed formulation also gives the flexibility to impose independent constraints on system states and leads to an easily on-board implementable closed-form control solution. The effectiveness of the control design is demonstrated by extensive computer simulations.
关键词：KeywordsState-constrained controlError transformationBarrier Lyapunov function
