THE INTERACTION OF PLANE SH－WAVES AND NON－CIRCULAR CAVITY SURFACED WITH LINING IN ANISOTROPIC MEDIA

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Adaptive neural network control for coordinated motion of a dual-arm space robot system with uncertain parameters

Control of coordinated motion between the base attitude and the arm joints of a free-floating dual-arm space robot with uncertain parameters is discussed. By combining the relation of system linear momentum conversation with the Lagrangian approach, the dynamic equation of a robot is established. Based on the above results, the free-floating dual-arm space robot system is modeled with RBF neural networks, the GL matrix and its product operator. With all uncertain inertial system parameters, an adaptive RBF neural network control scheme is developed for coordinated motion between the base attitude and the arm joints. The proposed scheme does not need linear parameterization of the dynamic equation of the system and any accurate prior-knowledge of the actual inertial parameters. Also it does not need to train the neural network offline so that it would present real-time and online applications. A planar free-floating dual-arm space robot is simulated to show feasibility of the proposed scheme.     

带时滞的二元神经元网络的OPNCL控制和复杂网络的时间延迟反馈控制

针对一种带时滞的二元神经元网络和一种复杂网络的混沌控制问题,利用开环加非线性闭环(Open Plus Nonlinear Closed Loop,OPNCL)方法和时间延迟反馈控制(Time Delay FeedbackControl,TDFC)方法,分别设计了该混沌网络和混沌系统的控制器。从理论上证明了第一种控制器可使该网络系统的解稳定地传递到选定的目标,并通过数值模拟实验进一步验证了两种方法的有效性。结果表明:该方法避免了开环加非线性闭环控制的一些限制因素;对于任何目标,所控制混沌系统的传递域(Basins of Entrainment)是全局的,避免了有关确定传递域范围的繁琐计算。