Dr Karim Ahmadi Dastgerdi is a Lecturer in Aerospace Engineering at the School of Architecture, Technology and Engineering (ATE), University of Brighton, United Kingdom. He is an aerospace engineer with a strong background in flight dynamics, control systems, and unmanned aerial vehicles (UAVs). He has worked as a postdoctoral researcher at Queen’s University Belfast and Adana Alparslan Science and Technology University, and he has experience in both academic research and practical engineering.

Dr Ahmadi Dastgerdi’s research focuses on flight dynamics, adaptive and fault-tolerant control, artificial intelligence, and Digital Twin technology. His main interest is in how complex dynamic systems, such as drones and autonomous vehicles, can operate safely and reliably when there are uncertainties, disturbances, or faults.

A key part of his research is the design of adaptive and intelligent control systems. This includes fault detection, fault-tolerant control, and learning-based control methods for UAVs and other dynamic systems. He is especially interested in combining traditional model-based control with AI and data-driven methods to improve system performance, safety, and robustness.

More recently, his research has expanded into Digital Twin technology. In this work, he combines physical models, real-time data, and artificial intelligence to create virtual representations of real systems. These digital twins can be used for monitoring, prediction, optimisation, and control in aerospace, robotics, and industrial applications.

Dr. Ahmadi Dastgerdi has published research in areas such as adaptive control, path planning, fault diagnosis, and autonomous UAV systems. He also works closely with academic and industrial partners on experimental drone platforms. His research aims to connect theory with real-world applications and support the development of safe and intelligent autonomous systems.

Supervisory Interests

Dr. Ahmadi Dastgerdi welcomes supervision of undergraduate, MSc, and PhD students working in areas related to flight dynamics of fixed-wing and rotary-wing aircraft, adaptive, robust, and fault-tolerant control systems, and the use of artificial intelligence and machine learning for control and autonomous decision-making. His supervisory interests also include Digital Twin technology for dynamic and cyber-physical systems, unmanned aerial vehicles (UAVs), path planning, guidance and navigation, as well as robotics and intelligent engineering systems. He is particularly interested in research projects that combine theoretical development with simulation studies, experimental work, or real-world implementation.

Approach to Teaching

Teaching Philosophy

My teaching philosophy is based on clear explanation, structured learning, and practical application. As an aerospace engineer working in flight dynamics and control systems, I believe students learn best when they understand both the mathematics and the physical meaning behind engineering models. I teach subjects related to aerospace engineering, flight dynamics, and control systems. My aim is to help students develop strong analytical skills, system-level thinking, and confidence in solving engineering problems. I also encourage students to think about real-world limitations, safety, and design trade-offs.

Approach to Teaching and Learning

In my teaching, I focus on conceptual understanding, problem-solving, and engineering judgement. I explain how real engineering systems often behave differently from ideal models and why robustness and validation are important, especially in aerospace and autonomous systems.

I use tools such as MATLAB and Python, along with simulations, diagrams, and case studies, to help students connect theory with practice.

Active Learning and Student Engagement

I believe students learn more effectively when they are actively involved in the learning process. In my classes, I use problem-based learning, guided examples, group discussion, and practical exercises. Students are encouraged to ask questions, explain their thinking, and learn from mistakes. This approach is especially helpful in subjects that involve mathematics and system modelling.

Assessment of Student Learning

Assessment is designed to support learning and improvement. I use a mix of formative assessment, such as in-class exercises and quizzes, and summative assessment, such as coursework and exams. I always try to show how the course content relates to real engineering problems in aerospace, robotics, and intelligent systems.

Classroom Environment and Inclusivity

I aim to create a supportive and inclusive classroom environment where all students feel comfortable participating. I encourage teamwork, open discussion, and mutual respect. I am approachable and available to students outside class time, and I adapt my teaching based on student feedback and learning needs.

Educational Vision

My goal is to educate engineers who are technically skilled, thoughtful, and responsible. By linking teaching with research and real-world examples, I aim to prepare students for careers in aerospace engineering, robotics, and autonomous systems.