ASSA-Based Nonlinear PID Parameter Tuning

Abstract

As a typical underactuated and strongly coupled nonlinear system, the double-payload overhead crane (DPOC) introduces complex swing dynamics while improving material handling efficiency. Traditional parameter tuning methods struggle to balance position tracking accuracy, residual swing suppression, and control energy consumption, and are highly sensitive to parameter perturbations and initial disturbances. To address the issues of premature convergence, exploration-exploitation imbalance, and rigid boundary handling in existing swarm intelligence algorithms under complex constrained optimization, this paper proposes an Adaptive Sparrow Search Algorithm (ASSA) integrating a dynamic warning threshold, Levy flight perturbation, and an adaptive penalty mechanism for the automatic tuning of nonlinear PID controller parameters. A high-precision state-space model of the double-pendulum system is established via Lagrangian mechanics, and a multi-objective composite fitness function encompassing the Integral of Time-weighted Absolute Error (ITAE), maximum swing angle, and control energy is designed. A Monte Carlo simulation protocol is constructed under three typical operating conditions: benchmark step, load mutation, and initial angle perturbation. Systematic comparisons with classical empirical rules and mainstream meta-heuristic algorithms demonstrate that the proposed ASSA exhibits superior performance in settling time, overshoot, swing suppression stability, and statistical significance. Ablation studies further verify the positive gains of the three improved modules. This research provides theoretical support and a reproducible engineering solution for the low-computational-power real-time parameter tuning of industrial underactuated systems.