A robust trajectory tracking control scheme for quadrotor unmanned aircraft vehicles under uncertainties is proposed herein. A tracking controller combined with the sliding mode and integral backstepping is performed for position and attitude tracking. The stability of the trajectory tracking controller of the quadrotor is investigated via Lyapunov stability analysis. By incorporating force and torque disturbances into numerical simulations, the results demonstrate the effectiveness of the proposed quadrotor trajectory controller. Finally, the experiments validate the feasibility of the proposed controller.

Owing to simple structure and strong robustness characteristics, the traditional (proportional-integral-derivative) PID controllers have been widely used in designing control systems of industrial process. However, due to complex nonlinear properties in some dynamics plants, it is difficult to obtain accurate mathematical models, and vulnerable to the object and environment. So, it makes difficult to setting controller parameters, especially no online self-tuning. Addressing an ionic polymer metal composite (IPMC) artificial muscle with high nonlinear properties and model uncertainties, an IPMC position tracking control system based on single neuron adaptive-PID control approach is proposed by using neural network selflearning and nonlinear mapping ability. The designed system not only can achieve position tracking and online self-tuning, but also guarantee robust stability in the presence of effect of uncertaintiesthe effectiveness of the proposed method is confirmed by simulation results.

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