Spherical Pendulum with Quad-Rotor Thrust Vectoring Actuation -- A Novel Mechatronics and Control Benchmark Platform

TL;DR

This paper introduces a novel mechatronic platform combining a 2-DoF spherical pendulum actuated by a quadcopter, serving as a benchmark for nonlinear control education and research, with comparative analysis of various control strategies.

Contribution

The paper presents a new integrated platform using quad-rotor thrust vectoring for pendulum control, bridging robotics and control education with experimental validation.

Findings

Different control strategies evaluated through step response and RMS error.

System robustness tested under external disturbances.

Both simulation and experimental results demonstrate control effectiveness.

Abstract

Motor-actuated pendulums have been established as arguably the most common laboratory prototypes used in control system education because of the relevance to robot manipulator control in industry. Meanwhile, multi-rotor drones like quadcopters have become popular in industrial applications but have not been broadly employed in control education laboratory. Platforms with pendulums and multi-rotor copters present classical yet intriguing multi-degree of freedom (DoF) dynamics and coordinate systems for the control system investigation. In this paper, we introduce a novel control platform in which a 2-DoF pendulum capable of azimuth and elevation rotation is actuated through vectored thrust generated by a quadcopter. Designed as a benchmark for mechatronics and nonlinear control education and research, the system integrates detailed mechatronic implementation with different control strategies. Specifically, we apply and compare small perturbation linearization (SPL), state feedback linearization (SFL), and partial feedback linearization (PFL) to the nonlinear system dynamics. The performances are evaluated by time specifications of step response and Root-Mean-Square (RMS) error of trajectory tracking. The robustness of the closed-loop system is validated under external disturbances, and both simulation and experimental results are presented to highlight the strengths and limitations of the nonlinear model-based control approaches.