Robust State/Output-Feedback Control of Robotic Manipulators: An Adaptive Fuzzy-Logic-Based Approach With Self-Organized Membership Functions
针对数学模型不确定且无法测量关节速度的机器人操作器，提出一种自组织自适应模糊逻辑控制器，先设计全状态反馈控制器，再结合高增益观测器实现输出反馈，保证跟踪误差有界，并通过平面机器人实验验证效果。
This article aims to design a joint space tracking controller for robotic manipulators having uncertainties in their mathematical representations under the additional constraint that joint velocity sensing not being available. A two-part design is followed where in the first part, the modeling uncertainties are dealt with a self-organized adaptive fuzzy-logic (AFL)-based controller where full-state feedback (FSFB) is assumed. The stability analysis yields semiglobally uniformly ultimately bounded tracking results. In the second part, a high-gain joint velocity observer is designed followed by replacing error vectors in the FSFB controller with their saturated versions obtained from the observer design to arrive at a self-organized AFL-based robust output-feedback controller. The stability analysis is performed via a multiple-step Lyapunov-type method where the semiglobal uniform ultimate boundedness of the tracking error is ensured. Comparative experiment results obtained from a planar robotic manipulator are presented to demonstrate the efficacy of the proposed control methodology.