A robust neural network approximation-based prescribed performance output-feedback controller for autonomous underwater vehicles with actuators saturation. (February 2020) - Elhaki, Omid; Shojaei, Khoshnam
Abstract: A robust neural network approximation-based output-feedback tracking controller is proposed for autonomous underwater vehicles (AUVs) in six degrees-of-freedom in this paper. The prescribed performance technique is employed to obtain some pre-defined maximum overshoot/undershoot, convergence speed and ultimate tracking accuracy for the tracking errors. A high-gain observer is used to approximate unavailable velocity vector which is crucial to design the output-feedback controller. A robust multi-layer neural network and adaptive robust techniques are combined to simultaneously compensate for the unmodeled dynamics, system nonlinearities, exogenous kinematic and dynamic disturbances, and reduce the risk of the actuator saturation. Then, the uniform ultimate boundedness stability of the closed-loop control system is proved via a Lyapunov-based stability synthesis. It is demonstrated that the posture tracking errors converge to a vicinity of the origin with a guaranteed prescribed performance during the tracking mission without velocity measurements. Finally, simulation results with a comparative study verify the theoretical findings. Highlights: A more evolved artificial intelligent control solution is introduced for AUVs. Prespecified transient and steady-state controller performances are guaranteed. A high-gain observer is efficiently used to remove all velocity sensors. A robust multi-layer neural network controller is proposed to efficaciously compensate for uncertainties. The danger of actuators saturation is reduced effectively by using neural networks.