Nonlinear Model Predictive Control of Ironless Linear Motors

Nguyen, Tuan Thanh; Lazăr, Mircea; Butler, Hans

doi:10.1109/ccta.2018.8511445

Cited by 4 publications

(4 citation statements)

References 17 publications

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“…In this section we provide an illustrative example of the proposed methodology for the position control of a linear motor, see, e.g., [25]. The simplified motion dynamics of the mechanical part of the actuator can be represented by the continuous-time model…”

Section: A Position Control For a Linear Motormentioning

confidence: 99%

Data-driven predictive control with estimated prediction matrices and integral action

Verheijen¹,

Silva²,

Lazăr³

2021

Preprint

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This paper presents a data-driven approach to the design of predictive controllers. The prediction matrices utilized in standard model predictive control (MPC) algorithms are typically constructed using knowledge of a system model such as, state-space or input-output models. Instead, we directly estimate the prediction matrices relating future outputs with current and future inputs from measured data, off-line. Online, the developed data-driven predictive controller reduces to solving a quadratic program with a similar structure and complexity as linear MPC. Additionally, we develop a new procedure for estimating prediction matrices from data for predictive controllers with integral action, corresponding to the rate-based formulation of linear MPC. The effectiveness of the developed data-driven predictive controller is illustrated on position control of a linear motor model.

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Section: A Position Control For a Linear Motormentioning

confidence: 99%

Data-driven predictive control with estimated prediction matrices and integral action

Verheijen¹,

Silva²,

Lazăr³

2021

Preprint

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show abstract

“…However, the paper [1] focuses on a linear model of the motor which is not desirable for the three-phase iron core linear motor [5], [21]. The authors in [2] proposed a nonlinear MPC (NMPC) for improving the positioning accuracy of an ironless linear motor and the result showed significant improvement over the PID-and linear MPC-based schemes. Motivated by the findings of [2], this study proposes the use of NMPC in reducing positioning error of current-driven iron core linear motors whose model is inherently nonlinear.…”

Section: Introductionmentioning

confidence: 99%

“…The authors in [2] proposed a nonlinear MPC (NMPC) for improving the positioning accuracy of an ironless linear motor and the result showed significant improvement over the PID-and linear MPC-based schemes. Motivated by the findings of [2], this study proposes the use of NMPC in reducing positioning error of current-driven iron core linear motors whose model is inherently nonlinear. Moreover, the studies by [1], [2] focused on positioning of systems in the meter scales as opposed to the micro-meter scale considered in this paper.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Nonlinear Model Predictive Motion Control of Linear Motor Drive for Micro/Nano-Positioning Applications

Paul

Küçükdemiral

Bevan

2022

2022 4th International Conference on Electrical, Control and Instrumentation Engineering (ICECIE)

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This paper presents an offset-free nonlinear model predictive controller (NMPC) for linear motor drive system which have been proposed to address the challenges of their rotary counterparts in precision linear motion control. The main challenges associated with the linear motors is that they are more sensitive to disturbances and parameter variations. The frictional force which affects the drive system is nonlinear and discontinuous. Since it is not possible to generate a discontinuous motor force to achieve desired tracking control, a continuous friction model is used to approximate the actual discontinuous friction model. Extant studies [1], [2] proposed predictive control of linear motors but focused on positioning in the meter range. Here, we propose an offset-free NMPC for micro-positioning applications in which the unmodelled nonlinear functions and the external disturbances are modelled as the integrating disturbance state that is estimated using an extended Kalman filter while the inherent robustness of NMPC is relied on to handle the parametric uncertainties. Numerical simulations are used to show the effectiveness of NMPC in controlling the linear drive system for precise motion tracking in the micrometer range.

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Recursive data–driven predictive control with persistence of excitation conditions

Verheijen

Silva

Lazăr

2022

2022 IEEE 61st Conference on Decision and Control (CDC)

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Nonlinear Model Predictive Control of Ironless Linear Motors

Cited by 4 publications

References 17 publications

Data-driven predictive control with estimated prediction matrices and integral action

Data-driven predictive control with estimated prediction matrices and integral action

Nonlinear Model Predictive Motion Control of Linear Motor Drive for Micro/Nano-Positioning Applications

Recursive data–driven predictive control with persistence of excitation conditions

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