Adaptive Fuzzy Full-State and Output-Feedback Control for Uncertain Robots With Output Constraint

This article focuses on the tracking control issue of robotic systems with dynamic uncertainties. To enhance tracking accuracy in a robotic manipulator with uncertainties, an adaptive fuzzy full-state feedback control is proposed. In view of output-feedback control with unknown states, a high-gain observer is employed to estimate unknown states. Considering the particular requirement that output of systems should be constrained in some practical working fields, we further design adaptive fuzzy full-state and output-feedback control schemes with output constraint to ensure that output maintains in constrained regions. By applying the Lyapunov theory, it is guaranteed that closed-loop systems are semiglobally uniformly ultimately bounded (SGUUB). Tangent-type barrier Lyapunov function is used for the controller design with output constraint and ensure stability. Finally, the effectiveness of our proposed methods is shown through both simulation examples and experimental results, comparative experiments in Baxter robot are proposed for evaluating the practicability of our proposed methods in actual applications.

Automated summary

This article introduces an adaptive fuzzy full-state feedback control approach for robotic systems with uncertainties, estimating unknown states using a high-gain observer and designing control schemes with output constraint. The effectiveness of the proposed methods is demonstrated through simulation examples and experimental results, showing practicality in applications like in Baxter robot.