Intervalwise receding horizon H/sub ∞/ tracking control for discrete linear periodic systems

Kim, Ki Baek; Lee, Jae Won; Кwon, Wook Hyun

doi:10.1109/9.847115

Cited by 17 publications

(1 citation statement)

References 12 publications

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“…An LMI-based MPC mechanism based on insights obtained in[11] and[12] is proposed in this paper to formulate the problem as a convex optimization problem with linear inequalities. Unlike conventional MPC schemes[13][14][15], such proposed MPC scheme can incorporate input and output constraints.By performing algebraic transformation to (5), we can get the discrete state space model of SLOCS as follows:…”

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confidence: 99%

Intelligent Modeling and Predictive Control of Pre-Grinding System

Liu

Yang

Yan

2012

AMR

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Stably controlling the pre-grinding process is paramount important for improving the operational efficiency and significantly reducing production costs in cement plants. Recognizing the complexity in both structure and operation of the pre-grinding process, this paper proposed a fuzzy and model predictive control system to stabilize and optimize the pre-grinding process. Based on the available techniques and system analysis, it is divided into two different sub-systems. One is handled by Fuzzy Logic Control (FLC), and the other is implemented by Linear Matrix Inequality (LMI)-based Model Predictive Control (MPC) based on the model achieved by Least Square Support Vector Machine (LS-SVM) regression. With this approach, the control parameters can be obtained online by the use of aforementioned algorithms and applied to the pre-grinding system by using OLE for Process Control (OPC) and relevant software. The trail system has been deployed in the field and its operation clearly demonstrates the effectiveness and feasibility of this control system in practice.

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confidence: 99%

Intelligent Modeling and Predictive Control of Pre-Grinding System

Liu

Yang

Yan

2012

AMR

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LMI-Based Model Predictive Control for Linear Discrete-Time Periodic Systems

Böhm

Raff

Reble

et al. 2009

Nonlinear Model Predictive Control

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MPC of constrained discrete-time linear periodic systems — A framework for asynchronous control: Strong feasibility, stability and optimality via periodic invariance

Linares

Jones

2011

Automatica

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a b s t r a c tState-feedback model predictive control (MPC) of discrete-time linear periodic systems with timedependent state and input dimensions is considered. The states and inputs are subject to periodically time-dependent, hard, convex, polyhedral constraints. First, periodic controlled and positively invariant sets are characterized, and a method to determine the maximum periodic controlled and positively invariant sets is derived. The proposed periodic controlled invariant sets are then employed in the design of least-restrictive strongly feasible reference-tracking MPC problems. The proposed periodic positively invariant sets are employed in combination with well-known results on optimal unconstrained periodic linear-quadratic regulation (LQR) to yield constrained periodic LQR control laws that are stabilizing and optimal. One motivation for systems with time-dependent dimensions is efficient control law synthesis for discrete-time systems with asynchronous inputs, for which a novel modeling framework resulting in low dimensional models is proposed. The presented methods are applied to a multirate nano-positioning system.

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Intervalwise receding horizon H/sub ∞/ tracking control for discrete linear periodic systems

Cited by 17 publications

References 12 publications

Intelligent Modeling and Predictive Control of Pre-Grinding System

Intelligent Modeling and Predictive Control of Pre-Grinding System

LMI-Based Model Predictive Control for Linear Discrete-Time Periodic Systems

MPC of constrained discrete-time linear periodic systems — A framework for asynchronous control: Strong feasibility, stability and optimality via periodic invariance

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