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)
With self-driving vehicles being pushed towards 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 overtaking manoeuvres.
Vehicle platooning is a promising cooperative driving vision 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.
—This paper presents the design of a robust distributed state-feedback controller in the discrete-time domain for homogeneous vehicle platoons with undirected topologies, whose dynamics are subjected to external disturbances and under random single packet drop scenario. A linear matrix inequality (LMI) approach is used for devising the control gains such that a bounded H∞ norm is guaranteed. Furthermore, a lower bound of the robustness measure, denoted as γ gain, is derived analytically for two platoon communication topologies, i.e., the bidirectional predecessor following (BPF) and the bidirectional predecessor leader following (BPLF). It is shown that the γ gain is highly affected by the communication topology and drastically reduces when the information of the leader is sent to all followers. Finally, numerical results demonstrate the ability of the proposed methodology to impose the platoon control objective for the BPF and BPLF topology under random single packet drop. Index Terms—vehicle platoon, LMI, distributed H∞ control with packet drops, robustness of closed-loop system.
Trajectory planning and trajectory tracking constitute two important functions of an autonomous overtaking system and a variety of strategies have been proposed in the literature for both functionalities. However, uncertainties in environment perception using the current generation of sensors has resulted in most proposed methods being applicable only during low-speed overtaking. In this paper, trajectory planning and trajectory tracking approaches for autonomous overtaking systems are reviewed. The trajectory planning techniques are compared based on aspects such as real-time implementation, computational requirements, and feasibility in real-world scenarios. This review shows that two important aspects of trajectory planning for high-speed overtaking are: (i) inclusion of vehicle dynamics and environmental constraints and (ii) accurate knowledge of the environment and surrounding obstacles. The review of trajectory tracking controllers for high-speed driving is based on different categories of control algorithms where their respective advantages and disadvantages are analysed. This study shows that while advanced control methods improve tracking performance, in most cases the results are valid only within well-regulated conditions. Therefore, existing autonomous overtaking solutions assume precise knowledge of surrounding environment which is not representative of real-world driving. The paper also discusses how in a connected driving environment, vehicles can access additional information that can expand their perception. Hence, the potential of cooperative information sharing for aiding autonomous high-speed overtaking manoeuvre is identified as a possible solution.
Automated vehicles are increasingly getting mainstreamed and this has pushed development of systems for autonomous manoeuvring (e.g., lane-change, merge, overtake, etc.) to the forefront. A novel framework for situational awareness and trajectory planning to perform autonomous overtaking in high-speed structured environments (e.g., highway, motorway) is presented in this paper. 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 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.
Connected autonomous vehicles are considered as mitigators 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 systems.
Quantifying and encoding occupants’ preferences as an objective function for the tactical decision making of autonomous vehicles is a challenging task. This paper presents a low-complexity approach for lane-change initiation and planning to facilitate highly automated driving on freeways. Conditions under which human drivers find different manoeuvres desirable are learned from naturalistic driving data, eliminating the need for an engineered objective function and incorporation of expert knowledge in form of rules. Motion planning is formulated as a finite-horizon optimisation problem with safety constraints. It is shown that the decision model can replicate human drivers’ discretionary lane-change decisions with up to 92% accuracy. Further proof of concept simulation of an overtaking manoeuvre is shown, whereby the actions of the simulated vehicle are logged while the dynamic environment evolves as per ground truth data recordings.
Vehicle platooning provides avenues to improve road transportation byreducingenergyconsumptionandpollutantemissionswhileincreasingsafety androadusage.However,tosafelyoperateasequenceofconnectedautonomous vehicles(CAVs)as a platoon, stringent conditions, such as string stability, on the closed-loop platoon dynamics must be guaranteed. To meet such platoon control speciﬁcations, linear longitudinal vehicle dynamics are usually assumed for the design of platoon control algorithms, and are imposed to each vehicle in the platoon by using mid-level control systems. However, disturbances and model mismatches can limit the compensation of the nonlinear vehicle dynamics, and thus jeopardise the tracking of the reference linear behaviour. To systematically impose the linear behaviour to the vehicles in a platoon despite model uncertainties and disturbances, in this paper two adaptive solutions are proposed and compared: (i) an adaptive solution for systems in Brunovsky form; and (ii) the Enhanced Model Reference Adaptive Control. Numerical results conﬁrm that both adaptive techniques are effective in imposing the linear dynamics to the longitudinal vehicle motion, also when integrated within a platoon control architecture. The closed-loop platoon performance is assessed via a set of performance indicators for different platoon lengths.
In this article, an enhanced model reference adaptive control (EMRAC) algorithm is used to design a generic lateral-tracking controller for a vehicle. This EMRAC is different from the EMRAC in the literature as it adopts a σ-modification approach to bind the adaptive gain of the switching action. Moreover, an extended Lyapunov theory for discontinuous systems is used to analytically prove the ultimate boundedness of the closed-loop control system when the adaptive gain of the switching action is bounded with a σ-modification strategy. The control algorithm is applied to a vehicle path-tracking problem and its tracking performance is investigated under conditions of: 1) external disturbances such as crosswind; 2) road surface changes; 3) modeling errors; and 4) parameter missmatches in a co-simulation environment based on IPG Carmaker/MATLAB. The simulation studies show that the controller is effective at tracking a given reference path for performing different autonomous highway driving maneuvers while ensuring the boundedness of all closed-loop signals even when the system is subjected to the conditions mentioned above.
This paper proposes an integrated trajectory planning based on Model Predictive Control (MPC) for designing collision-free evasive trajectory and a torque vectoring controller based on optimal control to ensure lateral-yaw stabilization in autonomous emergency collision avoidance under low friction and crosswinds on highways. The trajectory for performing the evasive manoeuvre is designed to minimise the deviation of the vehicle from the lane center while ensuring the vehicle remains within the road boundaries. The steering input computed from the MPC is used to steer the vehicle along the reference trajectory while the torque vectoring controller provides additional lateral-yaw stability. The integrated control framework was implemented on IPG Carmaker-MATLAB co-simulation platform and its efficacy was evaluated under different scenarios. Simulations performed for emergency collision avoidance at high speeds with low road friction and heavy crosswinds confirm the ability of the proposed closed-loop framework at successfully avoiding collisions with moving obstacles while ensuring that the 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.
- 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.