Connected and autonomous vehicles are set to revolutionise our transportation and re-shape our cities. They will prevent accidents, reduce parking space requirements, lower congestion and pollution. But in order to achieve this, they need several sensors and wireless interfaces which connect them with other vehicles, consumer devices, infrastructure and the Internet. This connectivity adds great functionality but it also introduces a myriad of security and privacy threats. Safety critical functionality in the vehicle is controlled by a multitude of Electronic Control Units (ECUs) which are fully programmable. As vehicles become more programmable, complex and interconnected, they also become more vulnerable to cyber attacks. The main goal of this fellowship is to secure connected and autonomous vehicles, making them resilient to this type of attacks. We will achieve this goal by developing techniques to secure each component of the vehicle's electronic architecture: ensuring that each ECU only executes code that is suitably authenticated; using model learning techniques to develop a framework for automated security testing of ECUs in a way that it scales; securing the vehicle's sensors such as radar, lidar and optical cameras against signal spoofing, tampering and denial of service attacks which would cause them to output inaccurate readings; and improving the communication protocols between vehicles and between the vehicles and the infrastructure in order to provide authenticity, non-repudiation and privacy while complying with stringent real-time constraints.

With the increasing move to more electric systems in aircraft, ships and automobiles, there is a need to ensure that electromechanical actuators are designed to satisfy the conflicting specifications of low cost, low volume/weight, high performance and requiring little maintenance. The conclusion of the more for less design philosophy is that power electronic motor drives will be work harder, in harsher environments, for longer periods of time. Scheduled maintenance periods will be longer, and therefore it is imperative that drives, especially those used for safety critical applications will employ prognosis and diagnosis algorithms as part of their basic control structure, to predict and prevent in-service failure. The work proposed here will investigate the production of new signatures for indicating the condition of a motor drive and its load, and also determine how these signatures can be used to determine the type and severity of a fault. The aim is to embed the condition monitoring into the normal operation of an electromechanical actuator, in order to detect and distinguish between faults in the electrical machine, the power converter and the mechanical system.

A sensorless electric motor drive is the popular term for drives which do not use shaft mounted speed or position sensors. Sensorless operation is highly desirable for reasons of cost, simplicity and system integrity. However, it is well known that there are serious problems with sensorless motor drive control at zero and low speeds and this has been one of the main research topics in this field for many years. The conventional method for sensorless control, used in commercial products, is to estimate the machine flux and speed using a mathematical model of the motor. Below 1 to 2% base speed however, position and speed estimation using such a model deteriorates and speed and torque control is lost. There has been a recent impetus for zero speed sensorless drives for more-electric aircraft and vehicular applications. For the former, there is a requirement for direct electromechanical (EM) actuation of critical actuators in which locking of the mechanical transmission is not permissible. In the vehicular field direct EM drives will be required for the main drive train, and for power steering, active suspension and braking actuation. One approach to the solution of the zero speed problem, which does not require a machine model, has been to exploit the natural asymmetries or saliencies in AC machines. These saliencies are cause by magnetic flux saturation and the geometry of the construction of the motor itself. Flux or rotor position can then be tracked by processing the current response to a test voltage signal injection overlaid on the supplied motor voltage. These signal injection methods are now quite well understood, but do contribute to increased accoustic noise, reduced efficiency, the requirement for additional sensors, and an increase in bearing wear and electrical stress within the machine windings.The current proposal aims to overcome the above disadvantages by developing methodologies by which:1) No signal injection is required, the method being integrated with the fundamental voltage applied to the drive via the power converter. This eliminates the problems of extra noise, losses, bearing wear and electrical stresses.2) The requirements for sensors is substantially reduced (depending on the application). For bespoke applications (e.g. aerospace, automotive), the aim will be for one current sensor and one low cost di/dt sensor. For industrial standard drives the target aim is to use only the existing line current sensors. These aims are quite challenging. Mathematical feasibility of a non-signal injection method has been shown at Nottingham and the technique is currently subject to patent at the University. Practical investigation is now possible owing to advances in high-accuracy timing and sampling available in low-cost digital control systems.

Abstracts are not currently available in GtR for all funded research. This is normally because the abstract was not required at the time of proposal submission, but may be because it included sensitive information such as personal details.

Autonomous robots, capable of independent and intelligent navigation through unknown environments, have the potential to significantly increase human safety and security. They could replace people in potentially hazardous tasks, for instance search and rescue operations in disaster zones, or surveys of nuclear/chemical installations. Vision is one of the primary senses that can enable this capability, however, visual information processing is notoriously difficult, especially at speeds required for fast moving robots, and in particular where low weight, power dissipation and cost of the system are of concern. Conventional hardware and algorithms are not up to the task. The proposal here is to tightly integrate novel sensing and processing hardware, together with vision, navigation and control algorithms, to enable the next generation of autonomous robots. At the heart of the system will be a device known as a 'vision chip'. This bespoke integrated circuit differs from a conventional image sensor, including a processor with each pixel. This will offer unprecedented performance. The massively parallel processor array will be programmed to pre-process images, passing higher-level feature information upstream to vision tracking algorithms and the control system. Feature extraction at pixel level results in an extremely efficient and high speed throughput of information. Another feature of the new vision chip will be the measurement of 'time of flight' data in each pixel. This will allow the distance to a feature to be extracted and combined with the image plane data for vision tracking, simplifying and speeding up the real-time state estimation and mapping capabilities. Vision algorithms will be developed to make the most optimal use of this novel hardware technology. This project will not only develop a unique vision processing system, but will also tightly integrate the control system design. Vision and control systems have been traditionally developed independently, with the downstream flow of information from sensor through to motor control. In our system, information flow will be bidirectional. Control system parameters will be passed to the image sensor itself, guiding computational effort and reducing processing overheads. For example a rotational demand passed into the control system, will not only result in control actuation for vehicle movement, but will also result in optic tracking along the same path. A key component of the project will therefore be the management and control of information across all three layers: sensing, visual perception and control. Information share will occur at multiple rates and may either be scheduled or requested. Shared information and distributed computation will provide a breakthrough in control capabilities for highly agile robotic systems. Whilst applicable to a very wide range of disciplines, our system will be tested in the demanding field of autonomous aerial robotics. We will integrate the new vision sensors onboard an unmanned air vehicle (UAV), developing a control system that will fully exploit the new tracking capabilities. This will serve as a demonstration platform for the complete vision system, incorporating nonlinear algorithms to control the vehicle through agile manoeuvres and rapidly changing trajectories. Although specific vision tracking and control algorithms will be used for the project, the hardware itself and system architecture will be applicable to a very wide range of tasks. Any application that is currently limited by tracking capabilities, in particular when combined with a rapid, demanding control challenge would benefit from this work. We will demonstrate a step change in agile, vision-based control of UAVs for exploration, and in doing so develop an architecture which will have benefits in fields as diverse as medical robotics and industrial production.