In this work, a method is developed for modeling uncertainty in the frequency domain which can be used to predict, or design systems with a specified, probability of failure to meet performance objectives. The work is an application of a probability stability/performance analysis technique being developed by the authors. An example of this technique is presented using a pilot operated proportional control valve (POPCV) system. Thirty replications of the pilot stage of a proportional control valve system were obtained by the University of Missouri and tested with one main stage valve. A model of the system is developed and used in Monte Carlo simulations based on distributions of the physical variations of the pilot valve. A mixed sensitivity H-infinity control system is developed using a frequency domain uncertainty model that only bounds a fraction of specified plants. It is shown that when the controller is implemented in a closed-loop system, only the fraction of the plants bounded in the uncertainty model are able to meet specified performance objectives. This technique allows a control designer to design higher performance control systems for mass produced systems with model uncertainty at the expense of having a specified fraction of systems not achieve a performance objective.
- Dynamic Systems and Control Division
Mixed Sensitivity H-Infinity Control Design With Frequency Domain Uncertainty Modeling for a Pilot Operated Proportional Control Valve
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Carpenter, R, & Fales, R. "Mixed Sensitivity H-Infinity Control Design With Frequency Domain Uncertainty Modeling for a Pilot Operated Proportional Control Valve." Proceedings of the ASME 2012 5th Annual Dynamic Systems and Control Conference joint with the JSME 2012 11th Motion and Vibration Conference. Volume 1: Adaptive Control; Advanced Vehicle Propulsion Systems; Aerospace Systems; Autonomous Systems; Battery Modeling; Biochemical Systems; Control Over Networks; Control Systems Design; Cooperative and Decentralized Control; Dynamic System Modeling; Dynamical Modeling and Diagnostics in Biomedical Systems; Dynamics and Control in Medicine and Biology; Estimation and Fault Detection; Estimation and Fault Detection for Vehicle Applications; Fluid Power Systems; Human Assistive Systems and Wearable Robots; Human-in-the-Loop Systems; Intelligent Transportation Systems; Learning Control. Fort Lauderdale, Florida, USA. October 17–19, 2012. pp. 733-741. ASME. https://doi.org/10.1115/DSCC2012-MOVIC2012-8845
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