A Fuzzy Neural Controller for Model-Free Control of Redundant Manipulators With Unknown Kinematic Parameters

In real-world robotics applications, kinematic parameters of redundant manipulators may need to be changed, thus creating difficulties in achieving precise control. To address this issue, this article proposes a fuzzy neural controller to learn kinematic parameters online and synchronously achieve the model-free control of redundant manipulators. Specifically, this controller consists of a gradient-based fuzzy (GBF) subsystem and a neural dynamics (ND) subsystem. On the one hand, the GBF subsystem is designed to achieve online learning of kinematic parameters, considering additional noise and a fuzzy parameter. Notably, the fuzzy parameter can drive the GBF subsystem to automatically terminate the learning process and convert the acquired information into usable structural knowledge once the kinematic parameters are precisely learned. On the other hand, based on the learned kinematic parameters, the ND subsystem is employed to solve a quadratic programming scheme for the kinematic control of manipulators. Such a scheme implements functions of orientation maintenance, trajectory tracking, and joint constraints in a model-free manner. Theoretical analyses confirm the effectiveness of the proposed controller's learning and control abilities. Finally, simulations, experiments, and comparisons demonstrate the feasibility and superiority of the fuzzy neural controller in controlling manipulators with unknown kinematic parameters.