This article proposes a novel approach combining exponential-reaching-law-based equivalent control law with radial basis function (RBF) network-based switching law to strengthen the sliding mode control (SMC) tracking...This article proposes a novel approach combining exponential-reaching-law-based equivalent control law with radial basis function (RBF) network-based switching law to strengthen the sliding mode control (SMC) tracking capacity for systems with uncertainties and disturbances. First, SMC discrete equivalent control law is designed on the basis of the nominal model of the system and the adaptive exponential reaching law, and subsequently, stability of the algorithm is analyzed. Second, RBF network is used to f...展开更多
文摘This article proposes a novel approach combining exponential-reaching-law-based equivalent control law with radial basis function (RBF) network-based switching law to strengthen the sliding mode control (SMC) tracking capacity for systems with uncertainties and disturbances. First, SMC discrete equivalent control law is designed on the basis of the nominal model of the system and the adaptive exponential reaching law, and subsequently, stability of the algorithm is analyzed. Second, RBF network is used to f...
文摘为提高无人水面艇(unmanned surface vehicle,USV)对复杂海况的适应性,针对欠驱动USV的路径跟踪控制问题,设计基于改进的自适应积分视线(improved adaptive integral line-of-sight,IAILOS)制导方法和径向基神经网络(radial basis function neural network,RBFNN)的积分滑模路径跟踪控制器。在IAILOS制导方法中,引入降阶的扩张状态观测器估计未知时变洋流速度,从而使得该制导方法不仅可以估计时变漂角,而且可以补偿未知时变洋流的扰动。利用RBFNN的无限逼近特性来估计USV动力学模型中的不确定项和未知的外部环境干扰。通过稳定性分析和仿真对比实验,验证了本文所设计的控制器的准确性和鲁棒性。