In this paper, an optimal tuned saturated PI type controller with anti-windup structure is used for process control. In first step, a [5]. Seshagiri and Khalil developed a new "conditional integrator" approach to the design of robust output regulation for multi-input multioutput (MIMO) minimum phase nonlinear systems transformable into the normal form, uniformly in a set of constant disturbances and uncertain parameters [6] and later they showed that this method tuned saturated PI/PID type controller with an anti-windup structure in some cases [7]. A genetic algorithm (GA) is an optimization technique that looks for the solution of the optimization problem, imitating species evolutionary mechanism [8], [9], [10], [11], [12]. In this type of algorithms, a set of individuals (so-called population) changes generation by generation (evolution) adapting better to the environment. Many multi-objective optimization algorithms using evolutionary concepts have been suggested since the pioneering work by Schaffer [13]. In order to obtain the best results, the search process needs to be guided toward the Paretooptimal front, maintaining diversity to prevent premature convergence and to achieve a well distributed population.In this paper we optimize the multivariable sliding mode PI control parameters for process control given in [7]. We use single objective genetic algorithm to optimize the error mean square used as objective function. The results show that optimizing control parameters results in high control input. To decrease the control input, a multi objective genetic algorithm is used when the maximum control input is defined as objective function. The results showed that optimizing the control parameters improve the control efficiency and reduces the error settling time in both cases. This paper is organized as follow: in section 2 the mathematical modeling of multi component isothermal liquid-phase kinetic sequence carried out in a continuous stirredtank reactor (CSTR) is presented. In section 3, the control strategy for multi variable system is presented. Finally, section 4 shows the performances of the proposed optimal control obtained through simulation.
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