This paper proposes an MPC - based (model predictive control) scheme to control active and reactive powers of DERs (distributed energy resources) in a grid - connected mode (either through a bus with its associated loads as a PCC (point of common coupling) or an MG (micro - grid)). DER may be a DG (distributed generation) or an ESS (energy storage system). In the proposed scheme, the set - poin...

Model predictive controller is widely used in industrial plants. Uncertainty is one of the critical issues in real systems. In this paper, the direct adaptive Simplified Model Predictive Control (SMPC) is proposed for unknown or time varying plants with uncertainties. By estimating the plant step response in each sample, the controller is designed and the controller coefficients are directly ca...

To improve the performance of a robust control, in presence of internal or external disturbance and uncertainties, to make a smooth tracking and elimination of high frequency disturbances especially in accurate systems with minimum power consumption an integration of robust optimal controller considered. Here, derivation and implementation of the proposed controller based on the combination of ...

Nowadays, extensive research has been conducted for gas turbine engines control due to growing importance of gas turbine engines for different industries and the need to design a suitable control system for a gas turbine as the heart of the industry. In order to design gas turbine control system, various control variables can be used, but in the meantime, fuel flow inserting into combustion cha...

Model-based predictive control (MPC) is one of the most efficient techniques that is widely used in industrial applications. In such controllers, increasing the prediction horizon results in better selection of the optimal control signal sequence. On the other hand, increasing the prediction horizon increase the computational time of the optimization process which make it impossible to be imple...

Typical production objectives in distillation process require the delivery of products whose compositions meet certain specifications. The distillation control system, therefore, must hold product compositions as near the set points as possible in faces of upset. In this project, inferential model predictive control, that utilizes an artificial neural network estimator and model predictive cont...