Speed Control of DC Motor Using Fuzzy PID Controller

Abstract: In this project, we designed a DC motor whose speed can be controlled by a PID controller. The proportional, integral and derivative gains (KP, KI, KD) of the PID controller are adjusted according to Fuzzy logic. First of all, the fuzzy logic controller is designed according to rules so that the systems is basically robust. There are 25 rules for the auto-tuning of each parameter of the PID controller. The FLC (fuzzy logic controller) has two inputs. The first is the motor speed error between the reference (setpoint) and the actual speed. The second is the variation of the speed error (derivative of the speed error). Secondly the output of the FLC is the parameters of the PID controller which are used to control the speed of the DC motor. The study shows that both the precise characters of PID controllers and the flexible characters of fuzzy controllers are present in the fuzzy self-tuning PID controller. The fuzzy auto-tuning approach implemented on a conventional PID structure was able to control the speed of the DC motor. It also improved the dynamic and static response of the system. The comparison between the conventional response and the fuzzy self-tuning response was performed based on the simulation result obtained by MATLAB/SIMULINK. The simulation results show that the designed self-adaptive PID controller achieves good dynamic behavior of the DC motor, perfect speed tracking with short rise and settling times, zero overshoot and steady state error and thus gives better performance compared to the conventional PID controller. We then model the fuzzy PID using simple code on Arduino IDE and perform a practical experiment, to confirm our theorical results.

• The paper presents a novel fuzzy PID controller that dynamically adjusts parameters for improved DC motor speed control.
• Simulation and practical experiments demonstrate enhanced setpoint tracking, reduced overshoot, and faster response times.
• The integration of fuzzy logic with traditional PID techniques offers a robust method for real-time control optimization.

Speed Control of DC Motor Using Fuzzy PID Controller

This paper presents a methodology for enhancing DC motor speed control through the integration of a Fuzzy PID Controller. The implementation combines traditional PID controllers with fuzzy logic systems to enhance system performance by improving setpoint tracking, reducing overshoot, and optimizing rise and settling times.

Introduction

PID controllers are ubiquitous in industrial applications due to their simplicity and effectiveness for a wide range of control systems. However, tuning these controllers for optimal performance, particularly in systems with underdamped responses, presents challenges. The paper proposes a fuzzy logic-based method for dynamically adjusting PID parameters (KP, KI, KD) to improve the control quality for DC motors. This approach leverages operator experience translated into fuzzy logic rules, allowing for intuitive parameter tuning that can adjust in real-time based on system behavior.

Modeling and Simulation

DC Motor Modeling

The DC motor is modeled using an electrical equivalent that consists of a voltage generator in series with an inductance and a resistor. The induced voltage, known as the electromotive force (EMF), opposes the applied voltage. The torque generated by the motor is directly proportional to the armature current, described by:

$T = K_t \times i$

where $K_t$ is the torque constant. Similarly, the EMF is proportional to the shaft's angular velocity:

$e = K_b \times \theta$

Combining Newton's and Kirchhoff's laws, the system transfer function relates rotational speed output to armature voltage input, providing a basis for the control design.

Figure 1: Representation of a DC motor.

PID Controller

Traditional PID tuning through manual methods is described, aiming to achieve oscillation-free control with optimal rise and settling times. The resulting gains, KP, KI, and KD, tune the controller for a balance between response speed and stability without overshooting.

Fuzzy PID Controller

The fuzzy logic controller (FLC) provides a rule-based framework for PID parameter adjustment. The FLC inputs include the speed error and its derivative, outputting adjusted gains for the conventional PID controller. Membership functions, rules, and consequent actions govern the control strategy, leveraging linguistic variables based on expert knowledge.

Figure 2: Membership function of the gain Kp.

Figure 3: Membership function of the gain Kd.

Figure 4: Simulink diagram of the DC motor in series with a Fuzzy PID Controller.

Figure 5: The Fuzzy PID rule viewer.

Simulation Results

Simulation results demonstrate the superior performance of the Fuzzy PID controller over traditional PID methods. The fuzzy system achieves precise speed tracking with minimal rise times, settling times, and zero overshoot. When compared to a system without a controller or with a conventional PID, the Fuzzy PID system offers a robust improvement.

Practical Experiment

The theoretical framework was validated through practical experimentation using an Arduino-based setup. While theoretical predictions were generally confirmed, transient overshoot occurred due to initial current surges, indicating areas for further refinement in practical applications.

Conclusion

The study effectively combines PID precision with fuzzy logic flexibility, enhancing the dynamic response and reducing steady-state errors in DC motor control. Future prospects include refining the approach for more sophisticated systems, potentially incorporating adaptive algorithms or hybrid control methodologies.

Overall, this research exemplifies the practical application of fuzzy logic in control systems, offering a significant enhancement over traditional PID methods. As technology advances, such integrated approaches could become standard in complex industrial applications requiring robust, adaptable control systems.