Adaptive Neural Output Feedback Control of Uncertain Nonlinear Systems With Unknown Hysteresis Using Disturbance Observer


In this paper, an adaptive neural output feedback control scheme is proposed for unsure nonlinear systems that are subject to unknown hysteresis, external disturbances, and unmeasured states. To accommodate the unknown nonlinear operate term within the uncertain nonlinear system, the approximation capability of the radial basis operate neural network (RBFNN) is employed. Using the approximation output of the RBFNN, the state observer and the nonlinear disturbance observer (NDO) are developed to estimate unmeasured states and unknown compounded disturbances, respectively. Based mostly on the RBFNN, the developed NDO, and therefore the state observer, the adaptive neural output feedback control is proposed for unsure nonlinear systems using the backstepping technique. The first-order sliding-mode differentiator is used to avoid the tedious analytic computation and the matter of “explosion of complexity” in the conventional backstepping method. The stability of the full closed-loop system is rigorously proved via the Lyapunov analysis technique, and therefore the satisfactory tracking performance is guaranteed beneath the integrated impact of unknown hysteresis, unmeasured states, and unknown external disturbances. Simulation results of an example are presented to illustrate the effectiveness of the proposed adaptive neural output feedback control scheme for unsure nonlinear systems.

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