In this paper, a hybrid position/force tracking control scheme based on neural network observer is proposed for robotic systems with uncertain parameters and external disturbances. First, an observer based on neural network is designed to estimate joint velocities. Then, a neural network-based adaptive hybrid position/force controller is proposed based on the observed joint velocities. By using strict positive real method and Lyapunov stability theory, it is proved that all the signals of the closed-loop system are ultimately uniformly bounded. Finally, the simulation tests on a two-link manipulator are conducted. The simulation results show the feasibility and effectiveness of the control scheme.

中文翻译：

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无速度测量的机器人系统基于神经网络的混合位置/力跟踪控制

针对不确定参数和外部干扰的机器人系统，提出了一种基于神经网络观测器的混合位置/力跟踪控制方案。首先，基于神经网络的观察者被设计为估计关节速度。然后，基于观测到的关节速度，提出了一种基于神经网络的自适应混合位置/力控制器。通过使用严格的正实方法和李雅普诺夫稳定性理论，证明了闭环系统的所有信号最终都是均匀有界的。最后，在两连杆机械手上进行了仿真测试。仿真结果表明了该控制方案的可行性和有效性。