Linear-Quadratic Regulator (LQR) Controller Optimisation for Disturbance Compensation Milling Table

Tan, Kar Hoe (2024) Linear-Quadratic Regulator (LQR) Controller Optimisation for Disturbance Compensation Milling Table. Final Year Project (Bachelor), Tunku Abdul Rahman University of Management and Technology.

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Abstract

From its inception to their incorporation with cutting-edge computational systems, milling machines have undergone a fast evolution, creating opportunities for increased efficiency and precision. The evolution of this technology, from the crude mechanics of the 19th century to the complex computer-integrated systems of today, is thoroughly explored in this paper. Controllers in milling machines are given a lot of attention, emphasising the change from Relay Logic Controllers to the advent of microprocessors and open architecture controllers. The study focuses on the Linear Quadratic Regulator (LQR) and examines its mathematical foundations, historical setting, and significant effects on several sectors. A thorough analysis of the literature compares the capabilities of LQR with those of conventional milling machine controllers like Proportional- Integral-Derivative (PID) and Nonlinear PID. The paper goes into further detail on optimisation algorithms, examining Particle Swarm Optimisation (PSO), Bacteria Foraging Algorithm (BFA), and Ant Colony Optimisation (ACO) critically for their potential to improve LQR’s effectiveness in milling machine applications. The research framework creates a cogent structure by fusing scientific procedures with theoretical underpinnings, with the goal of addressing the main research question and goals. In terms of methodology, MATLAB serves as the main instrument for optimisation procedures because to its extensive libraries, ensuring a thorough and organised approach. This proposal provides a roadmap for examining the potential of LQR, augmented by cutting-edge optimisation algorithms, in revolutionizing milling machine operations, thus promising advancements in precision, efficiency, and productivity.