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| DOI： |
| Received:October 28, 2005Revised:September 29, 2006 |
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| Max-plus-linear model-based predictive control for constrained hybrid systems: linear programming solution |
| Yuanyuan ZOU, Shaoyuan LI |
| (nstitute of Automation, Shanghai Jiao Tong University,
Shanghai 200240, China) |
| Abstract: |
| In this paper, a linear programming method is proposed to solve
model predictive control for a class of hybrid systems. Firstly,
using the (max, +) algebra, a typical subclass of hybrid systems
called max-plus-linear (MPL) systems is obtained. And then, model
predictive control (MPC) framework is extended to MPL systems. In
general, the nonlinear optimization approach or extended linear
complementarity problem (ELCP) were applied to solve the MPL-MPC
optimization problem. A new optimization method based on canonical
forms for max-min-plus-scaling (MMPS) functions (using the
operations maximization, minimization, addition and scalar
multiplication) with linear constraints on the inputs is presented.
The proposed approach consists in solving several linear programming
problems and is more efficient than nonlinear optimization. The
validity of the algorithm is illustrated by an example. |
| Key words: Hybrid systems Max-plus-linear systems Model predictive control Canonical form Max-min-plus-scaling function Linear programming |
