Switching adaptive output feedback model predictive control for a class of input-constrained linear plants
Junhyung Kim, Tae Woong Yoon, Hyungbo Shim, Jin Heon Seo
Abstract
Junhyung Kim, Tae Woong Yoon, Hyungbo Shim, Jin Heon Seo
Abstract
An adaptive output feedback model predictive control (MPC) for an uncertain input-constrained neutrally stable linear plant using control-relevant switching is presented. By employing an input-to-state stabilising MPC as the multi-controller, switching adaptive output feedback MPC is proposed for the system. Unlike previous methods for handling uncertainties on the basis of minmax MPC laws or techniques for linear parameter varying systems, the proposed MPC scheme employs model switching to deal with modelling uncertainties through adaptation; a best model is selected for the MPC law from time to time. The proposed scheme using finite prediction horizon guarantees global stability. Simulation results are given to show the effectiveness of the scheme.
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An adaptive output feedback model predictive control (MPC) for an uncertain input-constrained neutrally stable linear plant using control-relevant switching is presented. By employing an input-to-state stabilising MPC as the multi-controller, switching adaptive output feedback MPC is proposed for the system. Unlike previous methods for handling uncertainties on the basis of minmax MPC laws or techniques for linear parameter varying systems, the proposed MPC scheme employs model switching to deal with modelling uncertainties through adaptation; a best model is selected for the MPC law from time to time. The proposed scheme using finite prediction horizon guarantees global stability. Simulation results are given to show the effectiveness of the scheme.
Key concepts: Control theory (sociology), Model predictive control, Adaptive control, Minimax, Scheme (mathematics), Stability (learning theory), Controller (irrigation), Computer science