New robust model predictive control for uncertain systems with input constraints using relaxation matrices
- New robust model predictive control for uncertain systems with input constraints using relaxation matrices
- Lee, SM; Won, SC; Park, JH
- Date Issued
- SPRINGER/PLENUM PUBLISHERS
- In this paper, we propose a new robust model predictive control (MPC) method for time-varying uncertain systems with input constraints. We formulate the problem as a minimization of the worst-case finite-horizon cost function subject to a new sufficient condition for cost monotonicity. The proposed MPC technique uses relaxation matrices to derive a less conservative terminal inequality condition. The relaxation matrices improve feasibility and system performance. The optimization problem is solved by semidefinite programming involving linear matrix inequalities (LMIs). A numerical example shows the effectiveness of the proposed method.
- model predictive control; time-varying uncertain systems; input constraints; LMIs; RECEDING HORIZON CONTROL; STABILITY
- Article Type
- JOURNAL OF OPTIMIZATION THEORY AND APPLICATIONS, vol. 138, no. 2, page. 221 - 234, 2008-08
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