Relative gain array–based interaction modification via compensator design: experimental validation on a 2-DOF helicopter

Abstract

This paper proposes a compensator design methodology based on the Relative Gain Array (RGA) for systematically modifying the steady-state interaction structure of linear multivariable systems. Unlike the conventional use of the RGA as an analysis tool for input–output pairing selection, the proposed approach exploits its structural properties to reshape the interaction pattern toward a prescribed target RGA while preserving the plant dynamics. The method is formulated from the steady-state gain matrix and provides explicit conditions for obtaining feasible interaction configurations in systems with nonsingular steady-state gains matrices. The proposed framework is validated through simulation and experimental studies on a two-degree-of-freedom laboratory helicopter. Different interaction structures are implemented and evaluated under proportional and LQR control schemes. Performance is assessed using tracking accuracy, settling time, overshoot, control effort, and cross-coupling indicators. The results demonstrate that the proposed compensator effectively modifies the interaction structure and produces predictable changes in closed-loop behavior. Furthermore, the experimental analysis reveals practical trade-offs between interaction reduction, control performance, and actuator requirements. These findings show that the proposed RGA-based methodology provides a systematic and experimentally validated framework for interaction management in multivariable control systems.