Model Predictive Tracking Control Using State-Dependent Gain-Scheduled Feedback

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概要

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• In this paper, we propose a design method of a dual-mode model predictive control law for linear dynamical systems with input constraints. The proposed control law is composed of a finite horizon open-loop optimal control law and a state-dependent gain-scheduled feedback control law. By using the proposed control law, both high control performance and large region of attraction ca be achieved. We show that, by using the control law, the closed-loop stability can be guaranteed and the tracking error converges to zero in the case where a reference signal to be tracked is generated by a certain linear dynamics. The control algorithm is reduced to a convex optimization problem under linear matrix inequlitity constraints.

著者

• WADA Nobutaka

  Tottori University, Department of Mechanical and Aerospace Engineering

• TOMOSUGI Hiroyuki

  Hiroshima University, Department of Mechanical System Engineering

• SAEKI Masami

  Hiroshima University, Department of Mechanical System Engineering

• NISHIMURA Masaharu

  Tottori University, Department of Mechanical and Aerospace Engineering

関連論文

• Model Predictive Tracking Control Using State-Dependent Gain-Scheduled Feedback

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