英国著名的K.H.导航公司的技术经理安迪·诺里斯博士(Dr Andy Norris)在一次演讲中认为在现代船员的许多工作范围内无人驾驶船将是可能的。诺里斯博士宣称,推动无人驾驶船概念的发展主要因素是以安全为先导的法规、减少事故的诉讼...英国著名的K.H.导航公司的技术经理安迪·诺里斯博士(Dr Andy Norris)在一次演讲中认为在现代船员的许多工作范围内无人驾驶船将是可能的。诺里斯博士宣称,推动无人驾驶船概念的发展主要因素是以安全为先导的法规、减少事故的诉讼的期望和降低营运费用的要求等三方面。展开更多
This study presents an adaptive fuzzy neural network (FNN) control system for the ship steering autopilot. For the Norrbin ship steering mathematical model with the nonlinear and uncertain dynamic characteristics, an ...This study presents an adaptive fuzzy neural network (FNN) control system for the ship steering autopilot. For the Norrbin ship steering mathematical model with the nonlinear and uncertain dynamic characteristics, an adaptive FNN control system is designed to achieve high-precision track control via the backstepping approach. In the adaptive FNN control system, a FNN backstepping controller is a principal controller which includes a FNN estimator used to estimate the uncertainties, and a robust controller is designed to compensate the shortcoming of the FNN backstepping controller. All adaptive learning algorithms in the adaptive FNN control system are derived from the sense of Lyapunov stability analysis, so that system-tracking stability can be guaranteed in the closed-loop system. The effectiveness of the proposed adaptive FNN control system is verified by simulation results.展开更多
基金Supported by Doctoral Bases Foundation of the Educational Committee of P. R. China under Grant No. 20030151005 and the Ministry of Communication of P. R. China under Grant No. 200332922505.
文摘This study presents an adaptive fuzzy neural network (FNN) control system for the ship steering autopilot. For the Norrbin ship steering mathematical model with the nonlinear and uncertain dynamic characteristics, an adaptive FNN control system is designed to achieve high-precision track control via the backstepping approach. In the adaptive FNN control system, a FNN backstepping controller is a principal controller which includes a FNN estimator used to estimate the uncertainties, and a robust controller is designed to compensate the shortcoming of the FNN backstepping controller. All adaptive learning algorithms in the adaptive FNN control system are derived from the sense of Lyapunov stability analysis, so that system-tracking stability can be guaranteed in the closed-loop system. The effectiveness of the proposed adaptive FNN control system is verified by simulation results.