A comparison of tracking step inputs with a piezo stage using model predictive control and saturated linear quadratic Gaussian control. (January 2022)
- Record Type:
- Journal Article
- Title:
- A comparison of tracking step inputs with a piezo stage using model predictive control and saturated linear quadratic Gaussian control. (January 2022)
- Main Title:
- A comparison of tracking step inputs with a piezo stage using model predictive control and saturated linear quadratic Gaussian control
- Authors:
- Pao, Lucy Y.
- Abstract:
- Abstract: Compressed Sensing for Atomic Force Microscopy is a newer imaging mode that requires the piezo stage be driven rapidly between measurement locations. In contrast to raster scanning applications, this translates to a setpoint tracking problem. This paper considers the setpoint tracking performance of a piezo nano-positioning stage subject to rate-of-change limitations on the control signal, which is derived from the current limit of the power amplifier. To compensate the vibrational dynamics of the stage, a model predictive control scheme (MPC) and a linear quadratic Gaussian (LQG) controller which saturates the control increment are considered. In both cases, hysteresis and drift are compensated via dynamic inversion. To design the weighting matrices required by the MPC and linear feedback designs, an extension to classic reciprocal root locus ideas is proposed. The robustness of both schemes using classical methods like gain margin, phase margin, and gain of the sensitivity function at low frequencies is analyzed. The overall settle times achieved by both controllers (in both simulation and experiment) across a range of control weights where the reference input is a sequence of step inputs of varying amplitudes are compared. The results show that the best simulation settle time is achieved by MPC using the smallest control weight. However under experimental conditions, the best settle time is achieved by a much larger control weight and the performance of MPCAbstract: Compressed Sensing for Atomic Force Microscopy is a newer imaging mode that requires the piezo stage be driven rapidly between measurement locations. In contrast to raster scanning applications, this translates to a setpoint tracking problem. This paper considers the setpoint tracking performance of a piezo nano-positioning stage subject to rate-of-change limitations on the control signal, which is derived from the current limit of the power amplifier. To compensate the vibrational dynamics of the stage, a model predictive control scheme (MPC) and a linear quadratic Gaussian (LQG) controller which saturates the control increment are considered. In both cases, hysteresis and drift are compensated via dynamic inversion. To design the weighting matrices required by the MPC and linear feedback designs, an extension to classic reciprocal root locus ideas is proposed. The robustness of both schemes using classical methods like gain margin, phase margin, and gain of the sensitivity function at low frequencies is analyzed. The overall settle times achieved by both controllers (in both simulation and experiment) across a range of control weights where the reference input is a sequence of step inputs of varying amplitudes are compared. The results show that the best simulation settle time is achieved by MPC using the smallest control weight. However under experimental conditions, the best settle time is achieved by a much larger control weight and the performance of MPC becomes comparable with that of saturated linear feedback. This result is explained by showing that robustness increases with larger control weights. Highlights: Setpoint tracking controllers for a piezo stage with current limitation are compared. Model predictive and saturated linear feedback controllers perform comparably. Classical notions of robustness are useful in analyzing both controllers. … (more)
- Is Part Of:
- Control engineering practice. Volume 118(2022)
- Journal:
- Control engineering practice
- Issue:
- Volume 118(2022)
- Issue Display:
- Volume 118, Issue 2022 (2022)
- Year:
- 2022
- Volume:
- 118
- Issue:
- 2022
- Issue Sort Value:
- 2022-0118-2022-0000
- Page Start:
- Page End:
- Publication Date:
- 2022-01
- Subjects:
- Tracking step inputs -- Piezo stage -- Model predictive control -- Linear quadratic Gaussian control -- Hysteresis
Automatic control -- Periodicals
629.89 - Journal URLs:
- http://www.sciencedirect.com/science/journal/09670661 ↗
http://www.elsevier.com/journals ↗ - DOI:
- 10.1016/j.conengprac.2021.104972 ↗
- Languages:
- English
- ISSNs:
- 0967-0661
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 3462.020000
British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 20079.xml