This paper proposes a neural approximation based model predictive control approach for tracking control of a nonholonomic wheel-legged robot in complex environments, which features mechanical model uncertainty and unknown disturbances. In order to guarantee the tracking performance of wheel-legged robots in an uncertain environment, effective approaches for reliable tracking control should be investigated with the consideration of the disturbances, including internal-robot friction and external physical interactions in the robot’s dynamical system. In this paper, a radial basis function neural network (RBFNN) approximation based model predictive controller (NMPC) is designed and employed to improve the tracking performance for nonholonomic wheel-legged robots. Some demonstrations using a BIT-NAZA robot are performed to illustrate the performance of the proposed hybrid control strategy. The results indicate that the proposed methodology can achieve promising tracking performance in terms of accuracy and stability.

中文翻译：

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基于神经逼近的非完整轮腿机器人模型预测跟踪控制

本文提出了一种基于神经逼近的模型预测控制方法，用于复杂环境下非完整轮腿机器人的跟踪控制，该方法具有机械模型不确定性和未知干扰。为了保证轮腿机器人在不确定环境下的跟踪性能，需要考虑机器人动力系统中的机器人内部摩擦和外部物理相互作用等干扰，研究可靠跟踪控制的有效方法。在本文中，设计并采用基于径向基函数神经网络（RBFNN）近似的模型预测控制器（NMPC）来提高非完整轮腿机器人的跟踪性能。使用 BIT-NAZA 机器人进行了一些演示，以说明所提出的混合控制策略的性能。结果表明，所提出的方法可以在准确性和稳定性方面实现有希望的跟踪性能。