University roles and responsibilities
- Lab Instructor ENG 2093
- Office Hours ENG 1061
Vehicle Dynamics, Control Engineering
Lab Instructor, Department Mechanical Engineering – Dynamics & Control Instructor for practical lab work for an second year undergraduate course based on control (ENG 2093) engineering. Responsible for marking and assessment of student lab reports.
Office Hours, Department Mechanical Engineering Conduct office hours for undergraduate course on Mathematics (ENG 1061)
of issues such as traffic congestion, road safety, inefficient fuel
consumption and pollutant emissions that current road transportation
system suffers from. Connected autonomous vehicles
utilise communication systems to enhance the performance of
autonomous vehicles and consequently improve transportation by
enabling cooperative functionalities, namely, cooperative sensing
and cooperative manoeuvring. The former refers to the ability to
share and fuse information gathered from vehicle sensors and road
infrastructures to create a better understanding of the surrounding
environment while the latter enables groups of vehicles to drive in
a co-ordinated way which ultimately results in a safer and more efficient
driving environment. However, there is a gap in understanding
howand to what extent connectivity can contribute to improving the
efficiency, safety and performance of autonomous vehicles. Therefore,
the aim of this paper is to investigate the potential benefits
that can be achieved from connected autonomous vehicles through
analysing five use-cases: (i) vehicle platooning, (ii) lane changing,
(iii) intersection management, (iv) energy management and (v) road
friction estimation. The current paper highlights that although connectivity
can enhance the performance of autonomous vehicles and
contribute to the improvement of current transportation system performance,
the level of achievable benefits depends on factors such
as the penetration rate of connected vehicles, traffic scenarios and
the way of augmenting off-board information into vehicle control
the main-stream, there is an increasing motivation towards
development of systems that autonomously perform manoeuvres
involving combined lateral-longitudinal motion (e.g., lanechange,
merge, overtake, etc.). This paper presents a situational
awareness and trajectory planning framework for performing
autonomous overtaking manoeuvres. A combination of a potential
field-like function and reachability sets of a vehicle are used
to identify safe zones on a road that the vehicle can navigate
towards. These safe zones are provided to a model predictive
controller as reference to generate feasible trajectories for a
vehicle. The strengths of the proposed framework are: (i) it
is free from non-convex collision avoidance constraints, (ii)
it ensures feasibility of trajectory, and (iii) it is real-time
implementable. A proof of concept simulation is shown to
demonstrate the ability to plan trajectories for high-speed
These safe zones are provided to a tube-based robust model predictive controller as reference to generate feasible trajectories for combined lateral and longitudinal motion of a vehicle. The strengths of the proposed framework are: (i) it is free from nonconvex collision avoidance constraints, (ii) it ensures feasibility of trajectory even if decelerating or accelerating while performing lateral motion, and (iii) it is real-time implementable. The ability of the proposed framework to plan feasible trajectories for highspeed overtaking is validated in a high-fidelity IPG CarMaker and Simulink co-simulation environment.
controlled vehicle remains within its limits of stability. Furthermore, the robustness of the proposed control framework to variations in road friction changes is demonstrated by simulating an evasive manoeuvre at high-speeds for wide range of road friction conditions. Comparing the performance of the proposed control framework to a vehicle without the corrective actions available via torque vectoring highlight the additional benefits provided by the proposed closed-loop scheme at ensuring lateral-yaw stability under emergency scenarios.
where a group of consecutive connected autonomous vehicles (CAVs)
travel at the same speed with the aim of improving fuel efficiency, road
safety, and road usage. To achieve the benefits promised through platoon-
ing, platoon control algorithms must coordinate the dynamics of CAVs
such that the closed-loop system is stable, errors between consecutive
vehicles do not amplify along the string, and the time for re-establish
the platoon formation to changes in the operating conditions does not
diverge when the number of CAVs increases. Linear longitudinal vehicle
dynamics are often assumed in the literature to guarantee such stringent
platoon control requirements and they can be attained by equipping
vehicles in the fleet with mid-level control systems. However, model uncertainties and disturbances can jeopardise the tracking of the reference
linear behaviour. Hence, this paper presents for the first time, at the
best of the authors' knowledge, the design and the performance of an
adaptive control strategy and a robust model predictive control method
as possible solutions for the mid-level control problem. Numerical results confirm that both control techniques are effective at imposing the
dynamics of a linear time-invariant system to the longitudinal vehicle
motion and they outperform model-based feedback linearisation methods when the parameters of the nonlinear longitudinal vehicle model are
affected by uncertainties.
Autonomous high-speed driving is a safety-critical task and it is imperative that the planned trajectory of the vehicle can ensure safety (collision-avoidance) while computing smooth and feasible trajectories. We propose a trajectory planning framework that utilises information of the traffic vehicles to identify safe driving zones on the road using potential field functions and a robust model predictive controller for generating feasible trajectories that ensure the vehicle remains within the safe zones while performing the overtaking manoeuvre. The closed-loop performance of this controller is validated in a high-fidelity co-simulation environment.
The trajectory tracking controller is designed to ensure that the vehicle tracks the trajectory as closely as possible and preserves the lateral-yaw stability at all times. In this thesis, an Enhanced Model Reference Adaptive Control algorithm is used to design a generic lateral tracking controller for an autonomous vehicle. The control algorithm is applied to a vehicle path tracking problem and its tracking performance is investigated when subjected to external disturbances such as crosswind, road surface changes, modelling errors, and parameter miss-matches in a high-fidelity co-simulation environment.
Combined Planning & Control
Finally, the design of a combined motion planning & control scheme is carried out. The lateral tracking controller is augmented to include the dynamics of the steering actuator system and the updated tracking controller is combined with the RMPC based sophisticated path-planning framework to present a hierarchical closed-loop control architecture for autonomous overtaking. This architecture is implemented on the IPG CarMaker/Simulink environment and validated with different overtaking manoeuvring scenarios.
- Dixit, S., Fallah, S., Montanaro, U., Dianati, M., Stevens, A., Mccullough, F., & Mouzakitis, A. (2018). Trajectory planning and tracking for autonomous overtaking: State-of-the-art and future prospects. Annual Reviews in Control.
- van Aalst, S., Boulkroune, B., Dixit, S., Grubmüller, S., De Smet, J., Sannen, K. and De Nijs, W., 2017. Semi-autonomous Driving Based on Optimized Speed Profile. In Comprehensive Energy Management–Eco Routing & Velocity Profiles (pp. 19-37). Springer, Cham.