With the introduction of autonomous vehicles (AVs) onto roads globally in the future, questions arise regarding the potential to use such vehicles and to adjust infrastructure to accommodate them more efficiently. This paper presents a novel approach to enhance highway capacity by reducing lane width. The paper explores the safety implications of narrowing highway lanes through simulation modelling, focusing on a two-lane highway driving scenario. Two highway scenarios are introduced with a situation whereby one AV is overtaking another AV, i.e., on a highway with a straight road section and on a highway with a curved road section. The approach presented uses deontological ethical principles embedded into AV navigation algorithms, ensuring safe interactions between vehicles. Virtual boundaries (VBs) are used to define safe operating distances and to guide AV behaviour. A model predictive control (MPC) algorithm coupled with velocity control is used to optimise trajectory planning. This is then used investigate the safety of the straight and curved highway sections to determine the effectiveness of implementing such approaches.

The operation of a tethered drone with an Aurrigo Auto-Pod (AAP), or an autonomous robot (AR), represents a novel integration of technologies that demands a meticulous consideration of system requirements. Currently, there is a paucity of literature addressing the use of autonomous vehicles/robots (such as AAP/AR) with tethered drone systems. Through collaborative meetings between academics and industry partner Aurrigo, system requirements which address four critical areas were identified. These are for the requirements of system specific applications, including airports and large events such as concerts and sports games. This paper presents a comprehensive first step and a literature review based on these identified system requirements.

Automotive manufacturers are increasingly looking to hybrid vehicle designs to meet customer demands for more efficient vehicles. The increased efficiency of the hybrid architectures comes at the expense of increased system complexity. The field of automotive diagnostics for conventional vehicles is now well developed but diagnosing complex problems of system interactions is a new challenge for hybrid vehicle designers. The objective of this paper is to demonstrate a motion control detection algorithm and explore the appropriate vehicle responses to the detected condition.

The paper presents various control techniques for optimization and energy efficiency for domestic heating systems. A description of the heating system model, its behaviour and underpinning processes are presented. The model of the heating system, which is inherently nonlinear, is formulated either as a linear time varying (LTV) system or a bilinear time varying (BTV) system or bilinear time invariant (BTI) system. For each model representation various formulations of generalised predictive control (GPC) are investigated. The original GPC, in its non-incremental and modified forms are implemented and tested for these systems. Depending on the model structure, i.e whether the model is LTV, BTV or BTI, the GPC is formulated as a linear self tuning control (LSTC) GPC, bilinear self tuning control (BSTC) GPC and fixed bilinear (FB) GPC, respectively. It is shown that depending on the adopted model representation various degrees of improvement are possible when using different formulations for GPC.

The behavioral framework for modelling and control is a framework that does not distinguish \textit{a priori} between input and output of a system. Control in the behavioral framework does not make use of the intelligent control scheme consisting of observation of system output and feedback. Instead it relies on the interconnection of the to-be-controlled system to an appropriate controller, yielding the desired overall behaviour. In this paper, such a scheme is presented and compared to a feedback control scheme based on a numerical study. A focussed introduction into the behavioral framework with the concepts important for this paper is presented.

System uncertainties, such as modelling errors, external disturbances etc. can impede the fault detection process. Therefore a need arises for robust fault diagnosis, where complete disturbance decoupling can be achieved. The main contribution of this paper is the design of a parity relation for robust fault detection using right eigenstructure assignment. Up to date the left eigenstructure assignment technique has been used for the design of first order disturbance decoupled parity relations. In many cases complete disturbance decoupling is not possible when using left eigenstructure assignment. Therefore, the proposed method utilises the right eigenstructure assignment technique for the purpose of robust fault detection. The novelty of the developed scheme is to replace the traditional asymptotically convergent state observer by a state observer, which converges in a predefined time. The proposed method is demonstrated to be equivalent to a parity relation of a user predefined order.

The paper focuses on modelling and system identification of an air handling unit (AHU), which is part of larger heating ventilation and air conditioning (HVAC) system dedicated for clean room manufacturing. The aim is to use the model for subsequent control optimisation and further for control system design. The underlying physical relations of an AHU are investigated through a white-box model. Based on the white-box model considerations a discrete-time bilinear black-box model is proposed. The AHU cooling/heating capacity is altered by means of water valves. The valve introduces a Hammerstein nonlinearity on the system input, which needs to be additionally identified together with the dynamic bilinear model representing the heating/cooling coil of the AHU. The discrete-time model coefficients are then estimated based on real measurements.