Combinatorial optimization has been used in different research areas. It has been employed successfully in software testing fields to construct minimum set of combinations (i.e., in terms of size) which in turn represents the minimum number of test cases. It was also found to be a successful approach that can be applied to solve other similar problems in different fields of research. In line with this approach, this paper presents a new application of the combinational optimization in the design of PID controller for DC servomotor. The design of PID controller involves the determination of three parameters. To find optimal initial PID parameters, different tuning methods have been proposed and designed in the literature. The combinatorial design is concerned with the arrangement of finite set of elements into combinatorial set that satisfies some given constraints. Consequently, the proposed method takes the interaction of the input parameters as a constraint for constructing this combinatorial set. The generated sets are then used in the proposed tuning method. The method proved its effectiveness within a set of experiments in a simulated environment. 1. Introduction Direct current (DC) motors have been used intensively in various industrial applications. The significance of DC motors is related to their efficient characteristics such as preciseness, fast adaptation, smooth operation, and high torque capabilities. From the control point of view, DC motors exhibit linear speed-torque characteristics; therefore, proficient control aspects can be achieved [1]. During the recent decades, various controller structures have been proposed for controlling the DC motor. The most common controller used is the proportional plus integral plus derivative (PID) due to its simple structure and robust performance [2]. The design of the PID controller involves the determination of three parameters which are the proportional, integral, and derivative gains. Over the years, various tuning methods have been proposed to determine the PID gains. The first classical tuning rule method was proposed by Ziegler and Nichols [3] and also it is proposed by Cohen and Coon [4]. The advantage of these classical tuning methods is that the model of the system is not required to be known. However, the controller designed with these experimental methods can have an acceptable but not optimum system response. Therefore, recently, many artificial intelligence (AI) techniques have been employed to determine the optimal parameters and hence improve the controller performances. Such AI
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