Adaptive dynamic programming based robust control of nonlinear systems with unmatched uncertainties
Abstract This paper proposes a new approach to address robust control design for nonlinear continuous-time systems with unmatched uncertainties. First, we transform the robust control problem into an equivalent optimal control problem, which allows to simplify the control design. A critic neural network (NN) is then adopted to reformulate the derived Hamilton–Jacobi–Bellman (HJB) equation based on the optimal control methodology. Then, a novel adaptation algorithm is used to online directly estimate the unknown NN weights, so as to achieve the guaranteed convergence. The control system stability and the convergence of the derived control action to the optimal solution can be rigorously proved. Finally, two simulation examples are provided to illustrate the validity and efficacy of the proposed method.
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Adaptive dynamic programming based robust control of nonlinear systems with unmatched uncertainties
Semantic Scholar · Engineering · 2020
Abstract
Abstract This paper proposes a new approach to address robust control design for nonlinear continuous-time systems with unmatched uncertainties. First, we transform the robust control problem into an equivalent optimal control problem, which allows to simplify the control design. A critic neural network (NN) is then adopted to reformulate the derived Hamilton–Jacobi–Bellman (HJB) equation based on the optimal control methodology. Then, a novel adaptation algorithm is used to online directly estimate the unknown NN weights, so as to achieve the guaranteed convergence. The control system stability and the convergence of the derived control action to the optimal solution can be rigorously proved. Finally, two simulation examples are provided to illustrate the validity and efficacy of the proposed method.