Optimum PID Controller with Fuzzy Self-Tuning for DC Servo Motor

Abstract

DC motors are simple and controllable, making them a popular choice for various applications. However, the speed and load characteristics of DC motors can change, making it difficult to control them effectively. This paper proposes an optimum PID controller with fuzzy self-tuning for DC servo motors. The controller uses two steps to adjust the PID gains: The ACS algorithm is employed to identify the optimal PID gains in the first step. A fuzzy logic (FLC) controller is employed in the second stage to further fine-tune the gains. The FLC considers two cost functions: the first function is the sum of the squares of the error between the controlled output and reference input. The second function is a mathematical expression that specifies the required characteristics of the system response. The fuzzy self-tune then uses a set of rules to adjust the PID gains in response to changes in the system. The rules are based on the two cost functions designed to maintain the optimum PID gains for various operating settings. The outcomes of the two functions are: Kp = 5.2381, Ki = 7.0427, and Kd = 0.49468, with rising time = 0.2503, overshoot = 2.5079, and settling time = 10.4824 in the first cost function. The second cost function outcomes are Kp = 8.1381; Ki = 8.6427; and Kd = 0.49468. The FST-PID controller's performance is evaluated using Matlab-Simulink. The proposed controller was tested on a DC servo motor, and the results showed good performance in both steady-state and transient responses. The controller also maintained the optimum PID gains in the event of changes or disturbances. So, the motor's speed can effectively control under a variety of conditions.

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“…However, recent attention has shifted towards algorithmic control of electric motor rotation, with PID control emerging as a fundamental approach [10], [11].…”