**9. Conclusions**

Recently, there has been much interest in model predictive control which allows research‐ ers to address problems like feasibility, stability and performance in a rigorous manner. We first give a review of discrete-time model predictive control of constrained dynamic systems, both linear and nonlinear. The min-max approach for handling uncertainties are illustrated, then the LMIs methods are showed, and the advantages and disadvantages of methods are mentioned. The basic idea of each method and some method applications are stated. Despite the extensive literature that exists on predictive control and robustness to uncertainty, very little attention has been paid to the case of stochastic uncertainty. SMPC is emerging to adopt a stochastic uncertainty description (instead of a set-based descrip‐ tion). Some of the recent advances in this area are reviewed. We show that many impor‐ tant practical and theoretical problems can be formulated in the MPC framework, such as DMPC. Some considerable attention has been directed to NCSs. Although the network makes it convenient to control large distributed systems, there also exist many control issues, such as network delay and data dropout, which cannot be addressed using conven‐ tional control theory, sampling and transmission methods. Results from our recent re‐ search are summarized in Section 7. We have proposed a new networked control scheme, which can overcome the effects caused by the network delay. In the last section we re‐ view a number of distributed control architectures based on model predictive control. For the considered architectures, the underlying rationale, the fields of application, the mer‐ its and limitations are discussed.
