Distributed model predictive control of linear systems with unmeasurable states and uncertain parameters

Distributed model predictive control (DMPC) is widely used in complex industrial process control. The theoretical researches of DMPC have got more and more attention because of its good performances, such as the ability of dealing with all kinds of constraints effectively, high flexibility and fault tolerance. In this paper, the linear systems with uncertain parameters and unmeasurable states are confirmed by generalized polynomial chaos expansion method. Then the DMPC algorithm is realized by using the state observers to estimate states.