This study briefly discusses the primary AI’s roles in enhancing control engineering education (CEE), which has the potential to revolutionise the teaching-learning framework by making complex concepts and methodologies more intuitive, interactive, and application-driven. While understanding the potential benefits of these AI tools, such as assisting with problem-solving in education, some of the concerns about their use are summarised. An example is discussed how AI enhances CEE in MATLAB \& Simulink. The centre point in the brief paper is that AI should be a tool to enhance teaching-learning, rather than a shortcut to avoid it. More >

This paper focuses on the implementation of a control framework for a fixed wing aircraft system and the simulation demonstrations. The aim is to develop several Proportional Integral Derivative (PID) controllers to stabilise the altitude and attitude in a 2D environment by regulating the engine power, the pitch angle, and height in flight operation. In technique, a dynamic mathematical model is established by considering the degrees of freedom and the dynamics of motion of a fixed wing aircraft, which provide a foundation for design and simulation. A simplified aircraft dynamic model is tailored for testing the formed control systems, which can be flexibly modified with different aircraft c... More >

This study explains the main idea and structure of a What-How intelligent control (WH-I-control) system and a universal control (U-control) system. The system has two control layers. The bottom layer uses the U-control framework to manage 'How' to control within a universal framework. The top layer uses intelligent control (I-control) to coordinate and guide 'What' to achieve both global and local control goals. This study also reviews the configurations, functions, and integration of these two control layers in analysis, design, and applications. More >

The Tuned Liquid Damper (TLD) method offers a practical and cost-effective solution for seismic design. Accurate modeling of the TLD system’s dynamic behavior is crucial for optimizing its performance. In this study, the nonlinear dynamics of the TLD system are characterized using the Housner model, with parameters estimated via a nonlinear state estimation approach. To address challenges associated with model discretization and unknown noise processes, we introduce a Robust Extended Kalman Filter (REKF) that incrementally incorporates uncertainties to more accurately capture system dynamics. The proposed method is evaluated through real-time hybrid simulation, employing seismic input sign... More >

Indoor scene recognition poses considerable hurdles, especially in cluttered and visually analogous settings. Although several current recognition systems perform well in outside settings, there is a distinct necessity for enhanced precision in inside scene detection, particularly for robotics and automation applications. This research presents a revolutionary deep Convolutional Neural Network (CNN) model tailored with bespoke parameters to improve indoor picture comprehension. Our proprietary dataset consists of seven unique interior scene types, and our deep CNN model is trained to attain excellent accuracy in classification tasks. The model exhibited exceptional performance, achieving a t... More >