Hassan, Fazilah and Zolotas, Argyrios (2017) Impact of fractional order methods on optimized tilt control for rail vehicles. Fractional Calculus & Applied Analysis, 20 (3). pp. 765-789. ISSN 1311-0454
Full content URL: https://doi.org/10.1515/fca-2017-0039
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Item Type: | Article |
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Item Status: | Live Archive |
Abstract
Advances in the use of fractional order calculus in control theory in- creasingly make their way into control applications such as in the process industry, electrical machines, mechatronics/robotics, albeit at a slower rate into control applications in automotive and railway systems. We present work on advances in high-speed rail vehicle tilt control design enabled by use of fractional order methods. Analytical problems in rail tilt control still exist especially on simplified tilt using non-precedent sensor information (rather than use of the more complex precedence (or preview) schemes). Challenges arise due to suspension dynamic interactions (due to strong coupling between roll and lateral dynamic modes) and the sensor measurement. We explore optimized PID-based non-precedent tilt control via both direct fractional-order PID design and via fractional-order based loop shaping that reduces effect of lags in the design model. The impact of fractional order design methods on tilt performance (track curve following vs ride quality) trade off is particularly emphasized. Simulation results illustrate superior benefit by utilizing fractional order-based tilt control design.
Keywords: | Fractional Calculus, Fractional Order Control, Optimization, Tilt Control, railway vehicles, Active suspensions, Non-minimum phase zeros |
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Subjects: | H Engineering > H660 Control Systems H Engineering > H100 General Engineering H Engineering > H332 Rail Vehicle Engineering G Mathematical and Computer Sciences > G530 Systems Analysis and Design J Technologies > J990 Technologies not elsewhere classified G Mathematical and Computer Sciences > G120 Applied Mathematics |
Divisions: | College of Science > School of Engineering |
Related URLs: | |
ID Code: | 27565 |
Deposited On: | 24 May 2017 12:14 |
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