Robust Data-Induced Learning for Nonlinear Robot Control

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.