Robust Data‐Induced Learning for Nonlinear Robot Control

ABSTRACT

High‐precision trajectory tracking for robotic manipulators in repeatable tasks is often challenged by significant dynamic uncertainties. Unlike conventional adaptive control that relies on heuristically chosen projection bounds, and hard‐constraint robust methods that risk actuator saturation under severe uncertainty, this paper presents a novel Data‐Induced Learning Control (DiLC) approach. The primary novelty lies in the systematic interpretation and embedding of high‐level expert knowledge into a robust control architecture. Linguistic expert knowledge about physical parameter uncertainties is formally modeled by a fuzzy inference system. This system guides a Monte Carlo simulation to derive a statistically robust high‐confidence interval for a lumped uncertainty parameter, elevating bound selection from empirical guesswork to a rigorous statistical methodology. The controller subsequently features a unique pointwise, iterative adaptive law constrained by this interval via the projection operator, which mathematically guarantees graceful degradation of performance rather than system instability. A rigorous stability analysis, founded on an iteration‐domain energy‐like functional, formally proves that all closed‐loop signals are bounded and that the tracking error converges asymptotically to zero. Simulation results on a two‐link robotic manipulator validate the effectiveness of the proposed method and demonstrate the substantial performance benefits derived from this systematic integration of expert knowledge.