Model-free data driven control for trajectory tracking of an amplified piezoelectric actuator

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

Micro/nanopositioning systems commonly use piezoelectric actuators due to their high stiffness, fast response and ultra-high precision. However, three main factors affect their tracking performance, namely hysteresis, creep, and structural vibrations. To overcome these limitations, this paper proposes a new combined feedback and feedforward control strategy. Unlike most existing control algorithms for micro/nanopositioning systems, the new controller is a model-free learning-based capable of smoothly tracking continuous reference signals. It is further endowed with an ability to prevent fallacious learning associated with sensor noise and reference signal discontinuities. The paper also provides complete proofs for the convergence of the tracking error and boundedness of the control signals. Experimental trajectory tracking results obtained using the proposed controller applied on a commercially available amplified piezoelectric actuator verify the theoretical findings.

Original languageEnglish
Pages (from-to)27-35
Number of pages9
JournalSensors and Actuators, A: Physical
Volume279
DOIs
Publication statusPublished - Aug 15 2018

Fingerprint

piezoelectric actuators
Piezoelectric actuators
Trajectories
trajectories
Controllers
Feedforward control
learning
Feedback control
Hysteresis
controllers
Creep
Stiffness
feedforward control
structural vibration
vibration
Sensors
feedback control
stiffness
discontinuity
hysteresis

Keywords

  • Data-driven control
  • Micro/nanopositioning actuator
  • Model-free control
  • Piezoelectric actuator
  • Trajectory tracking

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Instrumentation
  • Condensed Matter Physics
  • Surfaces, Coatings and Films
  • Metals and Alloys
  • Electrical and Electronic Engineering

Cite this

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abstract = "Micro/nanopositioning systems commonly use piezoelectric actuators due to their high stiffness, fast response and ultra-high precision. However, three main factors affect their tracking performance, namely hysteresis, creep, and structural vibrations. To overcome these limitations, this paper proposes a new combined feedback and feedforward control strategy. Unlike most existing control algorithms for micro/nanopositioning systems, the new controller is a model-free learning-based capable of smoothly tracking continuous reference signals. It is further endowed with an ability to prevent fallacious learning associated with sensor noise and reference signal discontinuities. The paper also provides complete proofs for the convergence of the tracking error and boundedness of the control signals. Experimental trajectory tracking results obtained using the proposed controller applied on a commercially available amplified piezoelectric actuator verify the theoretical findings.",
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AU - Saleem, Ashraf

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N2 - Micro/nanopositioning systems commonly use piezoelectric actuators due to their high stiffness, fast response and ultra-high precision. However, three main factors affect their tracking performance, namely hysteresis, creep, and structural vibrations. To overcome these limitations, this paper proposes a new combined feedback and feedforward control strategy. Unlike most existing control algorithms for micro/nanopositioning systems, the new controller is a model-free learning-based capable of smoothly tracking continuous reference signals. It is further endowed with an ability to prevent fallacious learning associated with sensor noise and reference signal discontinuities. The paper also provides complete proofs for the convergence of the tracking error and boundedness of the control signals. Experimental trajectory tracking results obtained using the proposed controller applied on a commercially available amplified piezoelectric actuator verify the theoretical findings.

AB - Micro/nanopositioning systems commonly use piezoelectric actuators due to their high stiffness, fast response and ultra-high precision. However, three main factors affect their tracking performance, namely hysteresis, creep, and structural vibrations. To overcome these limitations, this paper proposes a new combined feedback and feedforward control strategy. Unlike most existing control algorithms for micro/nanopositioning systems, the new controller is a model-free learning-based capable of smoothly tracking continuous reference signals. It is further endowed with an ability to prevent fallacious learning associated with sensor noise and reference signal discontinuities. The paper also provides complete proofs for the convergence of the tracking error and boundedness of the control signals. Experimental trajectory tracking results obtained using the proposed controller applied on a commercially available amplified piezoelectric actuator verify the theoretical findings.

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