6533b7d9fe1ef96bd126d561

RESEARCH PRODUCT

Adaptive neural state-feedback stabilizing controller for nonlinear systems with mismatched uncertainty

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In this paper, an adaptive neural network (NN) state-feedback controller for a class of nonlinear systems with mismatched uncertainties is presented. By using a radial basis (RBF) neural network, a bound of unknown nonlinear functions is approximated so that no information about the upper bound of mismatched uncertainties is required. The state-feedback is based on Lyapunov stability theory, and it is shown that the asymptotic convergence of the closed-loop system to zero is achieved while maintaining bounded states at the same time. The presented methods are more general than the previous approaches, handling systems with no restriction on the dimension of the system and the number of inputs. Simulation results on dynamic equations of vertical take-off and landing (VTOL) helicopter confirm the effectiveness of the proposed methods in the stabilization of mismatched nonlinear systems.