Stabilization of a pan-tilt system using a polytopic quasi-LPV model and LQR control

Evren, Sanem; Ünel, Mustafa

doi:10.1109/iecon.2016.7793477

Cited by 1 publication

(6 citation statements)

References 16 publications

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mentioning

confidence: 82%

“…The readers are referred to [1] for the details about the obtained quasi-LPV model. In this work, the state vector is augmented to include the integral of the position errors as follows:…”

Section: A the Extended Polytopic Quasi-lpv Modelmentioning

confidence: 99%

“…The polytopic quasi-LPV model of the pan-tilt system is derived in [1]. The state vector of this LPV model, x ∈ R 4 , is designed as:…”

Section: A the Extended Polytopic Quasi-lpv Modelmentioning

confidence: 99%

“…∈ R 2n denotes the observer state vector, y 1 = q m ∈ R n is the encoder measurement given in (1), and the observer gains should be designed as:…”

Section: B a Cascaded High Gain Observermentioning

confidence: 99%

“…In this paper, the polytopic quasi-LPV model derived in [1] is extended by including the integral of the position errors in the new state vector. An LMI based optimal LQR controller that utilizes acceleration feedback (AFB) is synthesized based on this LPV model.…”

Section: Introductionmentioning

confidence: 99%

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Stabilization of pan-tilt systems using acceleration based LMI-LQR controller

Han

Ünel

2017

2017 11th Asian Control Conference (ASCC)

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Abstract-This paper extends the previous work on LPV modeling of a pan-tilt system [1] and tackles the robust stabilization problem by employing angular acceleration feedback in an LMI based optimal LQR controller. The state vector of the LPV model is augmented to include the integral of the position errors in addition to joint angles and velocities. Therefore, an extended polytopic quasi-LPV model of the pantilt system is derived. The LMI based optimal LQR controller that utilizes acceleration feedback is synthesized based on the extended LPV model. Since the time varying parameter vector is 4 dimensional, the proposed controller is synthesized by interpolating LMIs at 16 vertices of the polytope. A cascaded nonlinear high gain observer is also designed to estimate reliable positions, velocities and accelerations from noisy encoder measurements. Simulation results show that the proposed LMI based optimal LQR controller outperforms the classical LMI based LQR controller.

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“…

…”

mentioning

confidence: 82%

“…The readers are referred to [1] for the details about the obtained quasi-LPV model. In this work, the state vector is augmented to include the integral of the position errors as follows:…”

Section: A the Extended Polytopic Quasi-lpv Modelmentioning

confidence: 99%

“…The polytopic quasi-LPV model of the pan-tilt system is derived in [1]. The state vector of this LPV model, x ∈ R 4 , is designed as:…”

Section: A the Extended Polytopic Quasi-lpv Modelmentioning

confidence: 99%

“…∈ R 2n denotes the observer state vector, y 1 = q m ∈ R n is the encoder measurement given in (1), and the observer gains should be designed as:…”

Section: B a Cascaded High Gain Observermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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