电力线所处环境恶劣,工况复杂,具柔索特性,对巡线机器人的稳定性和可靠性提出较大挑战,因此以飞走巡线机器人(flying-walking power line inspection robot,FPLIR)为研究对象,提出了一种多模式切换混杂控制方法。在FPLIR巡检工作原理基...电力线所处环境恶劣,工况复杂,具柔索特性,对巡线机器人的稳定性和可靠性提出较大挑战,因此以飞走巡线机器人(flying-walking power line inspection robot,FPLIR)为研究对象,提出了一种多模式切换混杂控制方法。在FPLIR巡检工作原理基础上,建立4种控制模式的混杂自动机模型和相互切换的监测器模型;利用Lyapunov函数法和力角稳定性判据(force-angle stability margin,FASM)法分析FPLIR多模式切换和力学特性的稳定性;基于各模式的控制目标,提出了对应的控制策略,尤其结合FPLIR的结构和工况特点,设计变论域模糊控制器,提高FPLIR线上行走的稳定性,设计模型预测控制器,提高FPLIR落线的安全性。最后通过仿真和实验验证了多模式切换混杂控制方法的有效性和可行性,提升了FPLIR在复杂电力线环境下的适应性,为未来机器人智能巡检提供理论参考。展开更多
Since the state of hybrid systems is determined by interacting continuous and discrete dynamics, the state estimation of hybrid systems becomes a challenging problem. It is more complicated when the discrete mode tran...Since the state of hybrid systems is determined by interacting continuous and discrete dynamics, the state estimation of hybrid systems becomes a challenging problem. It is more complicated when the discrete mode transition information is not available, and the modes of hybrid systems are nonlinear stochastic dynamic systems. To address this problem, this paper proposes a novel hybrid strong tracking filter (HSTF) for state estimation of a class of hybrid nonlinear stochastic systems with unknown mode transition, the method for designing HSTF is presented. The HSTF can estimate the continuous state and discrete mode accurately with unknown mode transition information, and the estimation of hybrid states is robust against the initial state. Simulation results illustrate the effectiveness of the proposed approach.展开更多
文摘电力线所处环境恶劣,工况复杂,具柔索特性,对巡线机器人的稳定性和可靠性提出较大挑战,因此以飞走巡线机器人(flying-walking power line inspection robot,FPLIR)为研究对象,提出了一种多模式切换混杂控制方法。在FPLIR巡检工作原理基础上,建立4种控制模式的混杂自动机模型和相互切换的监测器模型;利用Lyapunov函数法和力角稳定性判据(force-angle stability margin,FASM)法分析FPLIR多模式切换和力学特性的稳定性;基于各模式的控制目标,提出了对应的控制策略,尤其结合FPLIR的结构和工况特点,设计变论域模糊控制器,提高FPLIR线上行走的稳定性,设计模型预测控制器,提高FPLIR落线的安全性。最后通过仿真和实验验证了多模式切换混杂控制方法的有效性和可行性,提升了FPLIR在复杂电力线环境下的适应性,为未来机器人智能巡检提供理论参考。
文摘Since the state of hybrid systems is determined by interacting continuous and discrete dynamics, the state estimation of hybrid systems becomes a challenging problem. It is more complicated when the discrete mode transition information is not available, and the modes of hybrid systems are nonlinear stochastic dynamic systems. To address this problem, this paper proposes a novel hybrid strong tracking filter (HSTF) for state estimation of a class of hybrid nonlinear stochastic systems with unknown mode transition, the method for designing HSTF is presented. The HSTF can estimate the continuous state and discrete mode accurately with unknown mode transition information, and the estimation of hybrid states is robust against the initial state. Simulation results illustrate the effectiveness of the proposed approach.
