Robust Learning Control for Shipborne Manipulator With Fuzzy Neural Network - Xu, Zhiqiang; Li, Wanli; Wang, Yanran (Frontiers in Neurorobotics, 2019)
Shipborne manipulator plays an important role for autonomous collaboration between marine vehicles. In real applications, conventional proportional-derivative (PD) controller is not suitable for the shipborne manipulator to conduct safe and accurate operations under ocean condition due to its bad tracing performance. This paper presents a real-time and adaptive control approach for the shipborne manipulator to achieve the position control. This novel control approach consists of a conventional PD controller and fuzzy neural network (FNN), which work in parallel to realize PD+FNN control. Qualitative and quantitive tests on the simulation and real experiments show that the proposed PD+FNN controller has achieved better performance in comparison with the conventional PD controller in the presence of uncertainty and disturbance. The presented PD+FNN eliminates the requirements for precise tuning of the conventional PD controller under different ocean conditions as well as accurate dynamics model of the shipborne manipulator. In addition, it effectively implements a sliding mode control (SMC) theory-based learning algorithm for fast and robust control which does not require matrix inversions or partial derivatives. Furthermore, the simulation and experimental results show that the angle compensation deviation of the shipborne manipulator can be improved in the range of ±1°.