Dynamics and Control
The dynamics and control research area carries out research across a wide range of dynamic systems from turbine blades through to aircraft landing gear.
Research aims
The dynamics and control research focuses on modeling and computing often non-linear systems to enhance their performance and support control system development. Many of our projects aim to use this knowledge to improve efficiency and work towards achieving Net Zero.
Research areas
Find out more about our research areas:
- Dynamic analysis of structures with contact interfaces and mistuning effects
- Aircraft tyre wear and emissions
- Uncertain structural dynamics applied to extreme vehicle motions
- Optimal control of autonomous vehicles
Dynamic analysis of structures with contact interfaces and mistuning effects
Most practical structures have contact interfaces and joints, leading to complex interactions in flexible rotor systems, especially with bearings or rotor-stator contact. These interactions can significantly alter dynamic responses, potentially causing multiple solutions, bifurcations, instability, or chaos.
In this research, models are developed to predict the dynamics of strongly nonlinear structures with friction, gaps, and contact interfaces.
Faculty
Aircraft tyre wear and emissions
Landing conditions are the most challenging for the undercarriage of an aircraft. Under these conditions aircraft tyres absorb considerable energy resulting in a rapid rise in tyre temperatures which accelerates wear and produces harmful emissions.
Research is underway to model the dynamic behaviour of aircraft tyres at touchdown, to predict the transient temperature profile, and to explore ways of reducing wear and harmful emissions.
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Uncertain structural dynamics applied to extreme vehicle motions
Predicting the dynamic response of structures with uncertain parameters is crucial for safety and design reliability, especially in vehicle dynamics. Catastrophic events like vehicle lift-off and flip-over, caused by unfavorable suspension parameters, are rare but critical to predict. Vehicle dynamic models are essential for manufacturers since testing every scenario isn't feasible. However, predicting these rare events typically requires lengthy Monte Carlo simulations, which are prone to statistical noise.
This research is developing a precise and efficient method to predict rare vehicle flip-over events without statistical noise, avoiding the heavy computational demands of Monte Carlo simulations, even with many uncertain suspension parameters.
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Optimal control of autonomous vehicles
Autonomous vehicles (AVs) are being developed to reduce traffic accidents and expand mobility options, but they are highly energy-intensive due to the many on-board sensors. Efforts are being made to reduce this energy consumption without sacrificing safety and comfort. One approach is optimizing steering control, though the nonlinear nature of steering models complicates this.
Ongoing research aims to develop robust optimal control methods to effectively reduce energy use in AVs.
The perception module plays a crucial role in the autonomous driving system, which is a very complicated system. In contrast, current perception research in the area of autonomous driving primarily focuses on the recognition of vehicles, lanes, and traffic signs, ignoring other factors that may cause traffic accidents, and fails to consider the fact that many traffic accidents on highways are caused by wild or wandering animals. The following studies were conducted to close this gap in knowledge:
- a dataset of 1050 photos for large animals that could appear on highways
- a more efficient Yolo model by improving its backbone, replacing the C3 module with C3Ghost. The number of parameters is decreased to fewer than 3.7 million, just 52.7% of Yolov5s but the average accuracy for each type of animals (mAP% 0.5) has reached over 95%
- our GhostSort-YoloNet (GS-YoloNet) also incorporates the Deep Sort algorithm to achieve real-time ranging and speed assessment of numerous targets, which is promising for practical application.
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