控制理论(社会学)
反推
死区
参数统计
控制器(灌溉)
鲁棒控制
人工神经网络
跟踪误差
计算机科学
自适应控制
噪音(视频)
补偿(心理学)
控制工程
工程类
控制系统
人工智能
数学
控制(管理)
统计
地质学
电气工程
图像(数学)
海洋学
农学
生物
心理学
精神分析
作者
Yuefei Wu,Dong Yue,Zhenle Dong
标识
DOI:10.1016/j.isatra.2019.05.003
摘要
Parametric uncertainty associated with unmodeled disturbance always exist in physical electrical–optical gyro-stabilized platform systems, and poses great challenges to the controller design. Moreover, the existence of actuator deadzone nonlinearity makes the situation more complicated. By constructing a smooth dead-zone inverse, the control law consisting of the robust integral of a neural network (NN) output plus sign of the tracking error feedback is proposed, in which adaptive law is synthesized to handle parametric uncertainty and RISE robust term to attenuate unmodeled disturbance. In order to reduce the measure noise, a desired compensation method is utilized in controller design, in which the model compensation term depends on the reference signal only. By mainly activating an auxiliary robust control component for pulling back the transient escaped from the neural active region, a multi-switching robust neuro adaptive controller in the neural approximation domain, which can achieve globally uniformly ultimately bounded (GUUB) tracking stability of servo systems recently. An asymptotic tracking performance in the presence of unknown dead-zone, parametric uncertainties and various disturbances, which is vital for high accuracy tracking, is achieved by the proposed robust adaptive backstepping controller. Extensively comparative experimental results are obtained to verify the effectiveness of the proposed control strategy.
科研通智能强力驱动
Strongly Powered by AbleSci AI