摘要
本文针对具有全状态时变约束的非线性电机系统,提出了自适应神经网络容错控制策略。为了保证系统控制性能,构建了容错补偿控制器消除执行器失效故障和偏移故障的影响。引入时变障碍李雅普诺夫函数,保证全部系统状态不超出指定时变约束范围,尤其是在执行器发生故障情况下也未违反状态约束限制。利用神经网络作为逼近器处理系统中未知函数。基于李雅普诺夫稳定性分析,证明了闭环系统中全部信号的有界性,以及系统输出良好的跟踪性能。仿真结果进一步说明所提出控制策略的有效性。
This paper proposes an adaptive fault-tolerant control method for nonlinear electromechanical system with full-state time varying constraints. In order to ensure the control performance of the system, a fault tolerant compensation controller is constructed to eliminate the influence of the actuator loss of effectiveness and bias fault. The time-varying barrier Lyapunov functions are in-troduced to ensure that all the system state does not exceed the specified time-varying constraint range;especially in the case of actuator failure, the full-state constraints are not violated. Neural network as the approximator is employed to approximate unknown function in the processing system. Based on the Lyapunov analysis, it is proved that all the signals in the closed-loop system are bounded and the good tracking performance of the system is achieved. The simulation results further illustrate the effectiveness of the proposed control strategy.
出处
《动力系统与控制》
2019年第4期230-241,共12页
Dynamical Systems and Control
基金
资助项目国家自然基金(61803189, 61622303, 61603164, 61803190)。
