Model Predivtive Control Model Predictive Control (MPC), which was developed in late 1970’s, is one of the most important computer algorithms which has received much attention as a powerful tool for the control of industrial process systems. The reason for this success can be attributed to the fact that model predictive control is the most general way of posing the process control problem in the time domain. The basic concept of model predictive control is to calculate a sequence of future control signals in such a way that it minimizes a multistage cost function defined over a prediction horizon. The performance index to be optimized is the expectation of quadratic function measuring the distance between the predicted system output and some reference sequence over the horizon plus a quadratic function measuring the control effort. In fact, model predictive control uses a model to predict the future outputs of the system, in a definite prediction horizon, in discrete time domain. Then, a sequence of control inputs is obtained using this model and minimizing a defined objective function. The objective function related to predictive control method, for obtaining an optimum control input, is expressed in the form of second order function. In this thesis, two heuristic optimization algorithms including Shuffled Frog Leaping (SFL) Algorithm and Imperialist Competitive Algorithm (ICA) have been used in order to minimize the objective function and bettering optimization in Generalized Predictive Control (GPC) method. For this purpose, first the SFL and ICA algorithm are introduced. Then after applying several modifications on these algorithms, their weaknesses are covered and three new algorithms are proposed: a New Hybrid Shuffled Frog Leaping (NHSFL) algorithm, a Modified Shuffled Frog Leaping (MSFL) algorithm and a Modified Imperialist Competitive Algorithm (MICA). Following, to show the effectiveness of the proposed modified algorithms, these algorithms are tested on two different complicated engineering problems known as Economic dispatch (ED) and Generation Expansion Planning (GEP) problem which are two of the most important problems to be solved in the operation and planning of a power system. Finally, the proposed algorithms introduced in this dissertation, are used for optimization in generalized predictive control algorithm and two predictive control algorithms are introduced: Model predictive Control Based on New Hybrid Shuffled Frog Leaping (MPC-NHS) and Model predictive Control Based on Modified Imperialist Competitive Algorithm (MPC-MI). Also, in order to verify the effectiveness and ability of the proposed extended MPC algorithms, the MPC-NHS and MPC-MI are Keywords: Shuffled frog leaping algorithm, Imperialist competitive algorithm, Model predictive control, Economic Dispatch, Generation expansion planning,, SVC.
