Learning-Based Tracking Control of Soft Robots

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.