Extensive research efforts have been made to address the motion control of rigid-link electrically-driven (RLED) robots in literature. However, most existing results were designed in joint space and need to be converted to task space as more and more control tasks are defined in their operational space. In this work, the direct task-space regulation of RLED robots with uncertain kinematics is studied by using neural networks (NN) technique. Radial basis function (RBF) neural networks are used to estimate complicated and calibration heavy robot kinematics and dynamics. The NN weights are updated on-line through two adaptation laws without the necessity of off-line training. Compared with most existing NN-based robot control results, the novelty of the proposed method lies in that asymptotic stability of the overall system can be achieved instead of just uniformly ultimately bounded (UUB) stability. Moreover, the proposed control method can tolerate not only the actuator dynamics uncertainty but also the uncertainty in robot kinematics by adopting an adaptive Jacobian matrix. The asymptotic stability of the overall system is proven rigorously through Lyapunov analysis. Numerical studies have been carried out to verify efficiency of the proposed method.

在文献中已经进行了广泛的研究以解决刚性链接电动（RLED）机器人的运动控制问题。但是，大多数现有结果都是在联合空间中设计的，由于在其操作空间中定义了越来越多的控制任务，因此需要将其转换为任务空间。在这项工作中，使用神经网络（NN）技术研究了具有不确定运动学特性的RLED机器人的直接任务空间调节。径向基函数（RBF）神经网络用于估计复杂的和校准的重型机器人运动学和动力学。NN权重通过两个适应律在线更新，而无需离线培训。与大多数现有的基于NN的机器人控制结果相比，所提出方法的新颖之处在于，可以实现整个系统的渐近稳定性，而不仅仅是统一的最终有界（UUB）稳定性。此外，通过采用自适应雅可比矩阵，所提出的控制方法不仅可以容忍执行器动力学的不确定性，而且还可以容忍机器人运动学中的不确定性。通过Lyapunov分析严格证明了整个系统的渐近稳定性。已经进行了数值研究以验证所提出方法的效率。