Abstract In this paper we present a framework for robust explicit/multi-parametric model predictive control (mpc). Based on four key steps, the proposed framework offers a systematic method for the off–line design, v...
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Abstract In this paper we present a framework for robust explicit/multi-parametric model predictive control (mpc). Based on four key steps, the proposed framework offers a systematic method for the off–line design, validation/testing and implementation of robust explicitmpc controllers for embedded systems. An important feature of the framework, is the use of a robust explicit/multi–parametricmpc technique that can guarantee that the system constraints are not violated due to the system model uncertainties. The framework is illustrated for the design of a robust explicit/multi–parametricmpc controller for the hydrogen desorption in metal–hydride bed storages.
An overview of multi-parametric programming and control is presented with emphasis on historical milestones, novel developments in the theory of multi-parametric programming and explicitmpc as well as their applicati...
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An overview of multi-parametric programming and control is presented with emphasis on historical milestones, novel developments in the theory of multi-parametric programming and explicitmpc as well as their application to the design of advanced controller for complex multi-scale systems. (C) 2012 Elsevier Ltd. All rights reserved.
A new algorithm for robust explicit/multi-parametric Model Predictive Control (mpc) for uncertain, linear discrete-time systems is proposed. Based on previous work on Dynamic Programming (DP), multi-parametric Program...
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A new algorithm for robust explicit/multi-parametric Model Predictive Control (mpc) for uncertain, linear discrete-time systems is proposed. Based on previous work on Dynamic Programming (DP), multi-parametric Programming and Robust Optimization, the proposed algorithm features, (i) a DP reformulations of the mpc optimization problem, (ii) a robust reformulation of the constraints, and (iii) a multi-parametric programming step, where the control variables are obtained as explicit functions of the state variable, such that the state and input constraints are satisfied for all admissible values of the uncertainty. A key feature of the proposed procedure is that, as opposed to previous methods, it only solves a convex multi-parametric programming problem for each stage of the DP procedure. (C) 2012 Elsevier Ltd. All rights reserved.
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