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为了更好地解决机器人系统中存在的参数不确定和外部干扰的鲁棒控制问题,提出一种基于耗散性理论的神经网络自适应鲁棒控制器,首先应用无源性理论对名义模型设计镇定控制器,然后利用RBF神经网络自适应学习系统的不确定部分,将神经网络逼近误差作为外部干扰,基于H∞控制理论使干扰对系统输出的影响抑制到所要求的最小程度,并用Lyapunov稳定性理论推导出RBF神经网络的权重矩阵调节律以及相关的鲁棒控制器,证明了系统的全局稳定性。仿真结果表明,这种控制器对机器人系统可能受到的干扰具有较好的抑制能力,提高了系统的鲁棒性,实现了系统轨迹的快速准确跟踪,又能很好地消除控制器的抖振,进而提高机器人工作性能。
In order to solve the problem of robust control of parameter uncertainties and external disturbances in robotic system, a neural network adaptive robust controller based on dissipative theory is proposed. Firstly, the nominal model design Then the RBF neural network is used to adaptively learn the uncertain part of the system. The approximation error of the neural network is regarded as external disturbance. Based on the H∞ control theory, the influence of the disturbance on the output of the system is suppressed to the minimum required. The Lyapunov stability The theory of sexuality deduces the weight matrix adjusting law of RBF neural network and the related robust controllers, and proves the global stability of the system. The simulation results show that this kind of controller can restrain the possible disturbance of the robot system, improve the robustness of the system, realize the fast and accurate tracking of the system trajectory, and eliminate the chattering of the controller , And then improve the working performance of the robot.