Adaptive model predictive control for a class of constrained linear systems based on the comparison model. (English) Zbl 1111.93036

Fukushima, Hiroaki; Kim, Tae-Hyoung; Sugie, Toshiharu

Automatica 43, No. 2, 301-308 (2007).

Summary: This paper proposes an adaptive model predictive control (MPC) algorithm for a class of constrained linear systems, which estimates system parameters on-line and produces the control input satisfying input/state constraints for possible parameter estimation errors. The key idea is to combine the robust MPC method based on the comparison model with an adaptive parameter estimation method suitable for MPC. To this end, first, a new parameter update method based on the moving horizon estimation is proposed, which allows to predict an estimation error bound over the prediction horizon. Second, an adaptive MPC algorithm is developed by combining the on-line parameter estimation with an MPC method based on the comparison model, suitably modified to cope with the time-varying case. This method guarantees feasibility and stability of the closed-loop system in the presence of state/input constraints. A numerical example is given to demonstrate its effectiveness.

MSC:

93C40	Adaptive control/observation systems
93B51	Design techniques (robust design, computer-aided design, etc.)
93D25	Input-output approaches in control theory
93C05	Linear systems in control theory
93A30	Mathematical modelling of systems (MSC2010)
93C15	Control/observation systems governed by ordinary differential equations

Keywords:

model predictive control; adaptive estimation; constrained systems; robust stability; comparison principle

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