In Networked Control Systems (NCSs), all o r some parts of control systems such as sensors, controllers, and actuators communicate together via digital networks. Due to non-ideal communication environment, data packet losses and delays are unavoidable. Thus, any controller design algorithm should consider these flaws. Model Predictive Control (MPC) is one of the most popular control methods in industrial process especially in chemical, oil, and petrochemical industries. Since MPCs usually are employed in slow rate processes, NCS can provide effective and reasonable platform to implement control systems. In this thesis, a new approach is proposed to design MPC controllers in networked control systems. First, a model is introduced for the control system such that data packet losses and delays in network are included in the design procedure. To increase the accuracy of the model, data exchange protocol is considered based on IEEE 802.15.4 which is a network protocol for industrial automation applications. The proposed design method is derived from infinite horizon MPCs. At each sampling time, the controller solves an optimization problem with some constraints expressed as LMIs. Simulation results indicate that the total elapsed time to compute future control commands in each sampling time is sufficiently small to be applicable in real-time implementations of MPCs. To conclude feasibility, it is also proved that any LMI created in the controller design has a real feasible solution. Furthermore, input and output constraints are considered in the design process. Finally, the proposed design procedure is extended to achieve a robust model predictive controller design method in NCS. In this case, it is assumed that the plant uncertainty can be expressed as polytopic. Then, it is proved that the controller design procedure guarantees asymptotical stability conditions for the equilibrium point of the system. A simulation for the proposed robust controller shows that in presence of model uncertainties and time delays in the network data exchange, the system remains stable. Keywords: Model Predictive Control (MPC), Networked Control System (NCS), Linear Matrix Inequality, Polytopic Uncertainty, Compensate Delay and Packet Loss, Lyapunov Function.
