Abstract
This paper develops a stochastic hybrid model-based control
system that can determine online the optimal control actions, detect faults
quickly in the control process, and reconfigure the controller accordingly using
interacting multiple-model (IMM) estimator and generalized predictive control
(GPC) algorithm. A fault detection and control system consists of two main
parts: the first is the fault detector and the second is the controller
reconfiguration. This work deals with three main challenging issues: design of
fault model set, estimation of stochastic hybrid multiple models, and stochastic
model predictive control of hybrid multiple models. For the first issue, we propose
a simple scheme for designing faults for discrete and continuous random
variables. For the second issue, we consider and select a fast and reliable fault
detection system applied to the stochastic hybrid system. Finally, we develop a
stochastic GPC algorithm for hybrid multiple-models controller reconfiguration
with soft switching signals based on weighted probabilities. Simulations for the
proposed system are illustrated and analyzed.