The aim of this course is to build upon foundational control systems knowledge, deepening students' understanding of higher-order system dynamics, advanced control techniques, and system stability analysis. In addition to mastering classical methods such as PID controllers, root locus, and frequency response techniques, students will explore the integration of artificial intelligence into control systems for enhanced performance. The course emphasizes the practical application of theory through real-world industrial challenges, collaborative problem-solving, and interdisciplinary projects that incorporate emerging AI-driven solutions in modern automation and control environments. By engaging with industry-relevant problems, students will develop both the technical and interpersonal skills necessary to succeed in professional engineering settings.
A Understand and present characteristics of higher order systems B Analyze stability of systems C Analyze systems and build controllers using Root Locus method D Analyze systems and build controllers using frequency response techniques E Integrate AI to control systems F Demonstrate creativity in controller design and be able to evaluate results
The teaching philosophy of the module follows very much the philosophy of Syntegrative Education. This means that the teaching delivery pattern, which follows more intensive block teaching, allows more meaningful contributions from industry partners. This philosophy is carried through also in terms of assessment, with a reduction in the use of exams and an increase in coursework, especially problem-based assessments that are project-focused. This module will be delivered through a combination of lectures, group discussions, case studies, hands-on practical exercises, etc. Lectures and group discussions focus on student-centered learning, encouraging students to address open-ended, real-world problems. These sessions guide students through complex control systems and AI concepts while fostering independent research. Students are expected to identify areas of learning relevant to their projects, conducting self-guided research to deepen their understanding and tackle practical challenges. The course encourages teamwork, with students collaborating on interdisciplinary projects under the guidance of the module leader and, where relevant, industrial mentors. These projects allow students to apply theoretical concepts to industry-focused problems, helping them develop both technical and soft skills such as communication, teamwork, and project management. Assessment is primarily project-based, giving students the opportunity to gain hands-on experience and apply their knowledge to solve practical control systems challenges. Independent learning and research play a significant role, as students progress towards achieving the learning outcomes through continual engagement with real-world, industry-driven problems. This teaching and learning method ensures that students develop both technical expertise and the practical skills necessary for professional success in control systems and AI applications.