Learning-Based Tracking Control of Soft Robots
Learning-Based Tracking Control of Soft Robots
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DOI：
10.1109/lra.2023.3303724
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发表时间：
2023-10
影响因子：
5.2
通讯作者：
Jingting Zhang;Xiaotian Chen;P. Stegagno;Mingxi Zhou;C. Yuan
中科院分区：
文献类型：
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作者：
Jingting Zhang;Xiaotian Chen;P. Stegagno;Mingxi Zhou;C. Yuan
本文提出了一种基于自适应径向基函数神经网络（RBF NN）的软躯干机器人动力学学习和跟踪控制方案。具体地说，一个低阶近似模型描述的软机器人的动力学首先推导出有限元法和适当的正交分解技术。在此基础上，利用RBF神经网络设计了一种自适应学习控制方案，不仅可以为柔性机器人提供稳定、精确的跟踪控制，而且在在线控制过程中实现了对机器人动力学的精确学习。该控制器能有效地处理柔性机器人复杂的非线性不确定动态和外部干扰，从而保证了良好的跟踪精度和控制适应性。机器人的动力学知识可以获得和存储在一个常数的RBF神经网络模型。在此基础上，提出了一种新的基于知识的控制器，为软机器人提供理想的控制性能，而不需要重复任何在线参数自适应，这显着提高了整个系统的运行效率，降低了计算复杂度和更容易控制实现。通过物理实验验证了所提方法的有效性和优越性。
This letter proposes an adaptive radial basis function neural network (RBF NN) based scheme for the dynamics learning and tracking control problems of a soft trunk robot. Specifically, a low-order approximate model describing the soft robot's dynamics is first derived with the finite element method and proper orthogonal decomposition technique. Based on this model, an adaptive learning control scheme is developed with RBF NN, which can not only provide stable and accurate tracking control for the soft robot, but also achieve accurate learning of the robot's dynamics during the online control process. The proposed controller can effectively handle the soft robot's complex nonlinear uncertain dynamics and external disturbances, it thus can guarantee desirable tracking accuracy and control adaptability. The learned knowledge of robot's dynamics can be obtained and stored in a constant RBF NN model. Based on this, a novel knowledge-based controller is further proposed to provide desirable control performance for the soft robot without needing to repeat any online parameter adaptations, which significantly improves the overall system's operational efficiency with reduced computational complexity and easier control implementation. Effectiveness and advantages of the proposed methods are validated through physical experiments.