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Adaptive neural dynamic surface control of output constrained non-linear systems with unknown control direction

Adaptive neural dynamic surface control of output constrained non-linear systems with unknown control direction

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This study investigates the adaptive neural dynamic surface control (DSC) of output constrained non-linear systems, subject to unknown system dynamics and uncertain control direction. A Nussbaum-type dynamic gain algorithm is used to handle the effect of unknown control direction. Integral barrier Lyapunov functions (iBLFs) are directly utilised to tackle the effect of output constraint. The prominent feature of iBLFs is that the feasible initial output signals are relaxed to the whole constraint range compared with pure tracking errors-based barrier Lyapunov function. Also, the DSC technology is developed to avoid the explosion of complexity in traditional control design, and adaptive neural networks are adopted to estimate the uncertainty that comprises the unknown system dynamics and parametric uncertainties. By utilising Lyapunov synthesis, it is proven that the proposed control is able to guarantee semi-global uniformly ultimately bounded of all signals in the closed-loop system. Simulation results are provided to verify the effectiveness of the proposed approach.

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