Adaptive Fuzzy Sliding-Mode Control for Trajectory Tracking of Six-Joint Robot Manipulators

This paper addresses the high-precision trajectory tracking control problem for robotic manipulators operating in uncertain environments by proposing a novel fuzzy adaptive gain-tuning sliding-mode control (FAGT-SMC) algorithm. While conventional sliding-mode control offers strong robustness against matched uncertainties, its fixed-gain switching mechanism inevitably induces severe chattering phenomena, causing actuator wear and performance degradation in practical implementations. To overcome this fundamental limitation, this paper designs an intelligent gain adaptation framework that dynamically regulates the sliding-mode switching gain through a fuzzy inference system. The control system structure integrates a nominal equivalent control component derived from the robotic dynamics model with an adaptively tuned discontinuous switching term. Theoretical analysis establishes global stability through Lyapunov-based methods, proving uniform ultimate boundedness (practical stability) of tracking errors under bounded uncertainties and residual fuzzy approximation errors. The proposed FAGT-SMC algorithm effectively balances robustness and control smoothness; therefore, numerical simulations demonstrate effectiveness for advanced robotic applications requiring both precision and adaptability in dynamic operating conditions.