针对无人水下机器人(unmanned underwater vehicle,UUV)工作中存在的执行器故障,在系统不确定性与外界干扰下,提出一种基于有限时间扰动观测器(finite time disturbance observer,FTDO),并结合改进模型的自适应鲁棒容错控制方法。一方面...针对无人水下机器人(unmanned underwater vehicle,UUV)工作中存在的执行器故障,在系统不确定性与外界干扰下,提出一种基于有限时间扰动观测器(finite time disturbance observer,FTDO),并结合改进模型的自适应鲁棒容错控制方法。一方面,FTDO能在有限时间内对外界环境干扰进行估计;另一方面利用滑模控制加上径向基神经网络(radial basis function neyral network,RBF)的万能逼近特性,建立带有执行器故障的输入补偿;其中改进模型的引入解决了系统不确定性导致的输入饱和,提高了稳定性与鲁棒性;其次采用一种新型的双幂趋近律使滑模量在更短时间收敛到稳态误差界内;仿真与水池实验结果表明了所提方法相对于滑模控制有着更好的容错效果。展开更多
An improved nonsingular fast terminal sliding mode manifold based on scaled state error is proposed in this paper.It can significantly accelerate the convergence rate of the state error which is initially far from the...An improved nonsingular fast terminal sliding mode manifold based on scaled state error is proposed in this paper.It can significantly accelerate the convergence rate of the state error which is initially far from the origin and achieve the fixed-time convergence.In addition,conventional double power term based reaching law is improved to ensure the convergence of sliding state in the presence of disturbances.The proposed approach is applied to the hovering control of an unmanned underwater vehicle.The controller exhibits both fast convergence and strong robustness to model uncertainty and external disturbances.展开更多
文摘针对无人水下机器人(unmanned underwater vehicle,UUV)工作中存在的执行器故障,在系统不确定性与外界干扰下,提出一种基于有限时间扰动观测器(finite time disturbance observer,FTDO),并结合改进模型的自适应鲁棒容错控制方法。一方面,FTDO能在有限时间内对外界环境干扰进行估计;另一方面利用滑模控制加上径向基神经网络(radial basis function neyral network,RBF)的万能逼近特性,建立带有执行器故障的输入补偿;其中改进模型的引入解决了系统不确定性导致的输入饱和,提高了稳定性与鲁棒性;其次采用一种新型的双幂趋近律使滑模量在更短时间收敛到稳态误差界内;仿真与水池实验结果表明了所提方法相对于滑模控制有着更好的容错效果。
文摘An improved nonsingular fast terminal sliding mode manifold based on scaled state error is proposed in this paper.It can significantly accelerate the convergence rate of the state error which is initially far from the origin and achieve the fixed-time convergence.In addition,conventional double power term based reaching law is improved to ensure the convergence of sliding state in the presence of disturbances.The proposed approach is applied to the hovering control of an unmanned underwater vehicle.The controller exhibits both fast convergence and strong robustness to model uncertainty and external disturbances.
