Design of PID controller with integration of artificial intelligence for machine tools application

Misbahul Munir, Nurul Qamariyah (2018) Design of PID controller with integration of artificial intelligence for machine tools application. Project Report. Universiti Teknikal Malaysia Melaka, Melaka, Malaysia. (Submitted)

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Abstract

Nowadays, the industry of machine tools requires high precision performance with affordable price in order to compete well in the market. In this project, proportional integral derivative (PID) controller is being used as the basic controller before designing the other controllers with the integration of artificial intelligence (AI). In fact, the design of the controllers is directly related to the issue of precise positioning of the machine tool performance which will affect the tracking error of the tool when moving from the previous point to the targeted point. Hence, this project purposed a solution to design PID controller with integration of AI, namely Particle Swarm Optimisation (PSO) and Firefly Algorithm (FFA) for optimisation in precise positioning for machine tools application through the analysis on the tracking error, root mean square error (RMSE), and the computational time. This research is also to validate and compare the proposed controllers through simulation and experimental analysis. To reach the aims of this research, there are several steps to be done, which are; the literature review; system identification and cutting force identification; controller design and numerical analysis; simulation and experimental analysis; comparison and discussion on results; and lastly, the report writing. The experimental analysis is done using XY table to represent the machine tool. The findings of this project have achieved the aims by obtaining the optimal solution for precise positioning of the machine tools through the integration of PSO and FFA into PID controller by reducing the errors; which are the tracking error and RMSE compared to the basic PID controller. Through the analysis of RMSE, it is found that the integration of AI into PID controller improved the performance by approximately 26% to 37%. It is highlighted that the FFA had shown better precision performance generally by having lower tracking error and RMSE values and required shorter computational time compared to PSO.

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