Adaptive Command Filtered Control of Strict Feedback Systems With Uncertain Control Gains
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摘要: 针对一类具有不确定控制增益的严格反馈系统, 提出一种基于命令滤波反推技术的自适应神经网络控制方法. 该方法采用神经网络对系统中的未知非线性函数进行逼近, 并引入命令滤波反推技术克服“计算膨胀”的问题. 与现有的命令滤波反推控制文献相比, 本文通过构造自适应误差补偿系统, 同时消除滤波器产生的边界层误差和不确定控制增益对系统性能造成的影响. 仿真结果验证了所提控制方法的有效性.Abstract: In this paper, a command filtered-based adaptive neural control scheme is developed for strict-feedback systems with uncertain control gains. In the developed scheme, neural networks are adopted to approximate the unknown nonlinear system functions and command filtered backstepping technique is utilized to solve the "explosion of complexity" problem. Compared with the literature on command filtered backstepping control, in this paper, an adaptive error compensating system is constructed to eliminate the impacts of the boundary layer errors generated by the filters and the uncertain control gains simultaneously. Simulation results are presented to verify the effectiveness of the proposed control scheme.
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