Enhanced-ZNN-Based Fixed-Time Model-Free Adaptive Control of Robotic Manipulator

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This paper presents an enhanced Zeroing Neural Network (ZNN) that is incorporated in the adaptive update law of a fixed-time model-free adaptive control scheme which improves the tracking performance of robotic manipulator. Although there are many research related to precise trajectory tracking control of robot manipulator using conventional model-based methods, it often requires high-fidelity model of the robot system, which is essential for accurately capturing its nonlinear dynamics. However, acquiring such precise models can be challenging due to parameter uncertainties, external disturbances, and unmodeled dynamics, that will lead to potential degradation in control performance or even instability when assumptions are inaccurate. To overcome this limitation, model-free adaptive control (MFAC) is gaining prominence because it directly estimates the unknown robot dynamics from the input and feedback data without the necessity for prior knowledge of robot parameters. The proposed control scheme is an extension of our previous work, which improves the tracking performance compared to the prior approach. A rigorous Lyapunov stability analysis, complemented by extensive simulations, was conducted to validate its effectiveness and demonstrates superiority in achieving precise and stable control.

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