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在外界扰动为有界不可测条件下,利用径向基函数(radial basis function,RBF)神经网络在线逼近全向智能轮椅的非线性逆运动学模型,提出对轮椅轨迹跟踪的直接自适应控制方法.首先,在分析全向智能轮椅平台动力学模型的基础上,设计了基于径向基函数神经网络的全向智能轮椅自适应控制器;并进一步利用李雅普诺夫稳定性理论,证明了在外界扰动及神经网络权值误差逼近有界的条件下,该控制器在全向智能轮椅轨迹控制中跟踪误差的一致稳定且有界;最后,通过全向智能轮椅轨迹跟踪仿真实验,验证了所提出控制方法的有效性和稳定性.
Under the circumstance that the external perturbation is bounded and unpredictable, the nonlinear inverse kinematics model of omnidirectional intelligent wheelchair is approximated online by radial basis function (RBF) neural network, and the direct adaptive control method of wheelchair trajectory tracking is proposed First of all, based on the analysis of the dynamic model of omnidirectional intelligent wheelchair platform, an omnidirectional intelligent wheelchair adaptive controller based on radial basis function neural network is designed. Furthermore, by using the Lyapunov stability theory, Disturbance and neural network weight error approximation bounded conditions, the controller in the omnidirectional intelligent wheelchair trajectory control tracking error consistent and bounded; Finally, through the omnidirectional intelligent wheelchair trajectory tracking simulation to verify the proposed The effectiveness and stability of control methods.