This paper presents a rigorous experimental evaluation of a particle swarm optimization (PSO)-optimized linear quadratic regulator (LQR) controller deployed on a physical Quanser 3-degree-of-freedom (3-DOF) gyroscope platform. To address the limitations of manual trial-and-error parameter tuning under real-world constraints, a multi-criteria optimization framework was established using three distinct performance indices: integral of absolute error (IAE), integral of time-weighted absolute error (ITAE), and integral of squared error (ISE). Real-time hardware experiments under multi-frequent square-wave and continuous sinusoidal trajectories, alongside impulse disturbance scenarios, demonstrated that the proposed PSO-LQR configured via the IAE criterion achieves the most superior balance between dynamic tracking agility and actuator energy efficiency. Specifically, the proposed PSO-IAE framework yields a settling time of 1.68 s, zero overshoot, and a minimal steady-state error of 0.10°. Crucially, quantitative energy analysis revealed that the proposed framework effectively circumvents motor voltage saturation, operating at an optimal control torque root mean square (RMS) value of 0.0962 Nm, which significantly outperforms the ISE configuration (0.3184 Nm) and the ITAE variant (0.1446 Nm). These empirical findings provide valuable practical benchmarks for constraint-enforced automation in high-level mechatronic mechanisms.
Citation: Hoai-Thuong Luong, Thanh-Tung Pham, Minh-Thanh Le, Chi-Ngon Nguyen. Experimental evaluation of PSO-optimized LQR control for a Quanser 3-DOF gyroscope: A multi-criteria comparative study under practical constraints[J]. AIMS Electronics and Electrical Engineering, 2026, 10(4): 627-647. doi: 10.3934/electreng.2026026
This paper presents a rigorous experimental evaluation of a particle swarm optimization (PSO)-optimized linear quadratic regulator (LQR) controller deployed on a physical Quanser 3-degree-of-freedom (3-DOF) gyroscope platform. To address the limitations of manual trial-and-error parameter tuning under real-world constraints, a multi-criteria optimization framework was established using three distinct performance indices: integral of absolute error (IAE), integral of time-weighted absolute error (ITAE), and integral of squared error (ISE). Real-time hardware experiments under multi-frequent square-wave and continuous sinusoidal trajectories, alongside impulse disturbance scenarios, demonstrated that the proposed PSO-LQR configured via the IAE criterion achieves the most superior balance between dynamic tracking agility and actuator energy efficiency. Specifically, the proposed PSO-IAE framework yields a settling time of 1.68 s, zero overshoot, and a minimal steady-state error of 0.10°. Crucially, quantitative energy analysis revealed that the proposed framework effectively circumvents motor voltage saturation, operating at an optimal control torque root mean square (RMS) value of 0.0962 Nm, which significantly outperforms the ISE configuration (0.3184 Nm) and the ITAE variant (0.1446 Nm). These empirical findings provide valuable practical benchmarks for constraint-enforced automation in high-level mechatronic mechanisms.
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Figures(14) / Tables(4)
Hoai-Thuong Luong, Thanh-Tung Pham, Minh-Thanh Le, Chi-Ngon Nguyen. Experimental evaluation of PSO-optimized LQR control for a Quanser 3-DOF gyroscope: A multi-criteria comparative study under practical constraints[J]. AIMS Electronics and Electrical Engineering, 2026, 10(4): 627-647. doi: 10.3934/electreng.2026026
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