PID parameters tuning based on Dandelion optimization algorithm for dc motor

In various applications demanding precise control over position and speed, such as robotics and CNC machines, the utilization of direct current (DC) motors has been widely acknowledged. In this context, an exhaustive investigation into a permanent magnet DC motor (PMDC) has been undertaken in this thesis. A cascaded P-PI controller has been formulated and executed to manage the rotational speed and angular position of the motor. Comprised of three individual controllers, the cascaded configuration has been engineered such that the first controller is committed to current regulation, the second to speed adjustment, and the third to position governance. To optimize the control methodology for the DC motor, various algorithms have been explored. The Classic Method (CM), genetic algorithm (GA), butterfly optimization algorithm (BOA), and dandelion optimization algorithm (DOA) have been applied to this purpose. Performance criteria have been employed to compare these four algorithms. It was found that DOA exhibited the shortest rise time of 0.076 seconds and the least overshoot of 3.13%, thus being identified as the superior option for motor control. Following closely, BOA demonstrated a rise time of 0.08 seconds and a somewhat elevated overshoot of 4%. On the other hand, GA was observed to be less efficacious with a protracted rise time of 0.35 seconds, settling time of 0.5 seconds, and the highest overshoot at 8.1128%. Optimization of the PID controller parameters was subsequently carried out using DOA, in light of its exceptional response to time-domain features and its attainment of desired performance metrics. The cascaded P-PI controller, when optimized with DOA, has been shown to facilitate superior control over the PMDC motor's speed and position. By virtue of this optimization, an enhanced level of accuracy and stability has been achieved, making the system more reliable and efficient in practical applications that necessitate rigorous control over motor variables. Through systematic evaluation, it has been revealed that the cascaded P-PI controller's architecture enables a more refined and effective control strategy. Additionally, the benefit of employing optimization algorithms for fine-tuning the controller has been distinctly verified. Among the algorithms scrutinized, DOA emerged as the most effective in optimizing the PID controller parameters, thereby substantiating its applicability and efficacy in real-world scenarios. The findings of this thesis have significant implications for industries and applications where precise motor control is paramount. Through the implementation of the cascaded P-PI controller optimized by DOA, an advancement in the state of the art in motor control has been realized. The research has successfully addressed the inherent challenges related to speed and position control in PMDC motors and has laid down a robust foundation for future work in this domain. Thus, a comprehensive understanding of the comparative advantages and limitations of various optimization algorithms in the context of PMDC motor control has been achieved, culminating in the identification of DOA as the preeminent choice for optimization.