中文摘要：

使用径向基函数（Radial basis function,RBF）神经网络为参数完全未知的机械臂在关节空间设计一种新的自适应神经控制算法。以前的自适应神经控制算法对神经网络能否学习到系统未知动态很少进行研究,因为不满足持续激励（Persistent excitation,PE）条件,神经网络权值的收敛性不能得到保证,以至对于重复执行相同的工作任务,自适应神经控制器也不得不进行冗余而繁琐的重新训练,浪费时间和能量。所设计的自适应神经控制器不仅实现了闭环系统所有信号的最终一致有界,而且在稳定的控制过程中,沿着周期或回归跟踪轨迹实现了部分神经网络权值的收敛以及未知闭环系统动态的局部准确逼近,即确定学习。学过的知识以时不变且空间分布的方式表达、以常值神经网络权值的方式存储,可以用来改进系统的控制性能,也可以应用到后续相同或相似的控制任务中,节约时间和能量。仿真验证了所设计控制算法的正确性和有效性。

英文摘要：

A new adaptive neural control approach is proposed by using Radial basis function （RBF） network for the robot manipulator with completely unknown parameters. In previous adaptive neural control, the problem of whether adaptive neural controllers indeed learn the unknown system dynamics has less been investigated. For dissatisfying the persistent excitation （PE） condition, the convergence of neural weights to their optimal values can not be guaranteed, as a consequence, the adaptive neural controller has to be retrained redundantly even for repeating the same control task, which may waste time and energy. The designed adaptive neural controller not only achieves uniformly ultimately boundness of all signals in the closed-loop system, but also achieves the convergence of partial neural weights and locally-accurate approximation of unknown closed-loop system dynamics along periodic or recurrent tracking orbit, i.e., deterministic learning. The learned knowledge represented in a time-invariant and spatially distributed manner and stored as constant neural weights can be used to improve control performance, and can also be recalled and reused in the same or similar control task, so that the robot can be easily controlled with little effort. Simulation studies are included to demonstrate the effectiveness of the approach.