Adaptive receding horizon control for constrained MIMO systems |
| |
| Affiliation: | 1. Automatic Control Laboratory, Swiss Federal Institute of Technology, Zurich, Switzerland;2. ABB Switzerland Ltd., Corporate Research, Baden-Daettwil, Switzerland;1. Institute of Technological Development for the Chemical Industry (INTEC), CONICET-Universidad Nacional del Litoral (UNL), Güemes 3450, (3000) Santa Fe, Argentina;2. GIPSA-lab, Grenoble Campus, 11 rue des Mathématiques, BP 46, 38402 Saint Martin d’Héres Cedex, France;3. Department of Chemical Engineering, University of São Paulo, Av. Prof. Luciano Gualberto, trv 3 380, 61548 São Paulo, Brazil;1. Department of Control Science and Engineering, Jilin University, PR China;2. Institute for Systems Theory and Automatic Control, University of Stuttgart, Germany;1. Control and Power Group, Electrical and Electronic Engineering, Imperial College London, United Kingdom;2. Mathematical Institute, University of Bayreuth, 95440 Bayreuth, Germany;3. Institut für Mathematik, Technische Universität Ilmenau, 98693 Ilmenau, Germany;1. Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Milan, Italy;2. Dipartimento di Ingegneria Civile e Architettura, Università di Pavia, Pavia, Italy;1. Department of Chemical and Biomolecular Engineering, University of California, Berkeley, CA 94720, USA;2. Department of Electric Engineering, Universidade Federal da Bahia, Salvador-BA 40210-630, Brazil |
| |
| Abstract: | An adaptive control algorithm for open-loop stable, constrained, linear, multiple input multiple output systems is presented. The proposed approach can deal with both input and output constraints, as well as measurement noise and output disturbances. The adaptive controller consists of an iterative set membership identification algorithm, that provides a set of candidate plant models at each time step, and a model predictive controller, that enforces input and output constraints for all the plants inside the model set. The algorithm relies only on the solution of standard convex optimization problems that are guaranteed to be recursively feasible. The experimental results obtained by applying the proposed controller to a quad-tank testbed are presented. |
| |
| Keywords: | Adaptive control Self tuning control Learning control Set membership identification Model predictive control Control of constrained systems Impulse response |
| 本文献已被 ScienceDirect 等数据库收录！ |
|
