Rapid Control Prototyping Simulation of Particle Swarm Optimization-Tuned Backstepping Tracking Control for A Rotary Inverted Pendulum.

Abstract

The primary objective of this protocol is to provide a reproducible fixed-step simulation framework for evaluating Particle Swarm Optimization (PSO)-based gain tuning in nonlinear control systems. The implementation begins with the formulation of a Furuta-type pendulum model, followed by the integration of a backstepping controller within a 2 ms fixed-step execution environment. The methodology involves a systematic four-stage process: characterizing non-ideal implementation constraints, defining a multi-objective Particle Swarm Optimization search space, executing automated offline tuning, and evaluating the resulting parameters through a standardized suite of trajectory-tracking and disturbance-rejection scenarios. This setup, utilizing high-performance industrial workstations and standardized signal interfaces, supports consistent repeated-trial comparisons within the same control architecture. The design compares a baseline manually tuned backstepping controller with a PSO-optimized variant that shares the exact same control structure, thereby isolating the impact of gain selection. Controller performance is assessed across three distinct operational scenarios: step-trajectory following, mixed-frequency sinusoidal tracking, and disturbance rejection. Statistical analysis of 10 repeated trials showed that PSO-based optimization reduced the step-tracking RMSE from 0.065 to 0.050 rad and attenuated peak pendulum excursions by 33.1%. These improvements were achieved alongside a 22.1% reduction in RMS control effort, indicating that the optimized parameters facilitated more efficient energy distribution within the Lyapunov-based framework. Ultimately, this methodology provides a structured simulation framework to evaluate nonlinear control strategies before any subsequent physical hardware implementation.