Hybrid control based on sliding mode--dynamic recursive fuzzy neural network for marine electrical propulsion
滑模动态递归模糊神经网络船电推进复合控制

We propose a hybrid control(HC) strategy for the marine electrical podded propulsion system to eliminate the overshoot and obtain a fast and smooth dynamic response for the podded propulsion. HC consists of a robust sliding mode control(SMC) and a dynamic recursive fuzzy neural network control(DRFNNC). SMC uses the dead-zone nonlinearity and error band method to tackle uncertainties and external disturbances; DRFNNC which has online self-learning algorithm forces the tracking error to approach zero. We build the hardware-in-loop simulation system of Siemens-Schottel-Propulsor(SSP) based on SIMOTION; the simulation and experimental results show that HC provides a fast and smooth dynamic response in both transient state and steady state, and improves the robustness and motion precision of the SSP system.