Experimental results are presented to validate a recently developed adaptive output feedback controller for a general class of unknown MIMO linear systems. The control approach relies on three components, a predictor, a reference model, and a controller. Specifically, since the predictor is designed to predict the system’s output for any admissible control input, controlling the uncertain system is reduced to controlling the predictor, which is a virtual system with known dynamics and full state available. Subsequently, a full state feedback control law is designed to control the predictor output to approach the reference system, while the reference system tracks the desired trajectory while accounting for the actuator amplitude and rate saturation constraints. Ultimately, the control objective of driving the actual system output to track the desired trajectories is achieved by showing that the system output, the predictor output, and the reference system trajectories all converge to each other. Theorems and the step-by-step implementation of the control strategy are presented. Finally, the control’s efficacy is illustrated by a real time implementation of the proposed algorithm on an actual helicopter test bed.
- Dynamic Systems and Control Division
Adaptive Predictor-Based Output Feedback Control for a Class of Unknown MIMO Linear Systems: Experimental Results
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Nguyen, CH, & Leonessa, A. "Adaptive Predictor-Based Output Feedback Control for a Class of Unknown MIMO Linear Systems: Experimental Results." Proceedings of the ASME 2014 Dynamic Systems and Control Conference. Volume 3: Industrial Applications; Modeling for Oil and Gas, Control and Validation, Estimation, and Control of Automotive Systems; Multi-Agent and Networked Systems; Control System Design; Physical Human-Robot Interaction; Rehabilitation Robotics; Sensing and Actuation for Control; Biomedical Systems; Time Delay Systems and Stability; Unmanned Ground and Surface Robotics; Vehicle Motion Controls; Vibration Analysis and Isolation; Vibration and Control for Energy Harvesting; Wind Energy. San Antonio, Texas, USA. October 22–24, 2014. V003T38A005. ASME. https://doi.org/10.1115/DSCC2014-6217
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