Abstract
The aim of this paper is to evaluate the usability of the self-organizing migrating algorithm (SOMA) in a nonlinear system predictive control area. The model predictive control is based on an objective function minimization. Two approaches to model predictive control applied on a nonlinear system are studied here. Firstly, the SOMA was used to minimize the objective function, secondly, the fmicon function included in the MATLAB optimization toolbox was used for the same. The nonlinear system simulated here is an exothermic semi-batch reactor mathematical model based on a real chemical exothermic process. Also the input data used here to simulate the process were obtained from the same real process. Results obtained by the simulation means were than evaluated using suitable criterion which was defined for that purpose and discussed.
Highlights
Control of nonlinear systems brings challenges in the controller design
Suitable algorithm minimizes an objective function which is based on the responses from a real system model and the real system itself
Results of the best self-organizing migrating algorithm (SOMA) and MATLAB simulations were selected for the comparison
Summary
The current availability of powerful computing technologies enables using of complex computational methods. One of such complex method is the self-organizing migrating algorithm (SOMA). This algorithm can be used for solving of various optimization problems. Such problem definitely is the model predictive control (MPC). Suitable algorithm minimizes an objective function which is based on the responses from a real system model and the real system itself. Minimizing the objective function using SOMA is studied here and the comparing with MATLAB fmincon function is done to evaluate the SOMA control ability
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