Probabilistic reasoning for automotive systems
Creators: name: Comissiong, Rhys
Publisher: name: Loughborough University
Year: 2021
Subjects: subject: 99999 Engineering not elsewhere classified
subject: FOS: Other engineering and technologies

description: Modern control strategies for internal combustion engines use increasingly complex networks of sensors and actuators to measure different physical parameters. Often indirect measurements and estimation of variables, based off sensor data, are used in the closed loop control of the engine and its subsystems. Thus, sensor fusion techniques and virtual instrumentation have become more significant to the control strategy. With the large volumes of data produced by the increasing number of sensors, the analysis of sensor networks has become more important. Understanding the value of the information they contain and how well it is extracted through uncertainty quantification will also become essential to the development of control architecture. This can potentially lead to the reduction of physical sensors and actuators used on internal combustion engines and other applications.With the increase in computational efficiency, Artificial Intelligence and Machine Learning techniques have gained popularity. However, these methods typically use black box models, which can be difficult for Automotive Engineering applications. For this thesis, the automotive system is framed as a model-based knowledge engineering problem. This framework uses Probabilistic Reasoning to understand what can be known about the system using available sensor information.To control an automotive system, a control engineer must consider the different components of the closed-loop control architecture: the controller, the plant model, the actuators interacting with the plant, the sensors providing feedback on the plant and the observer collecting information from the sensors. Traditionally there has been much development in the design of controllers and the modelling of plants. The implementation of closed loop control is dependent on the appropriate choice of actuators and sensors as these determine the effectiveness of the controller’s ability to interact with the plant. Currently there is no formal methodology to identify the optimal sensor and actuator configuration for an automotive system. Therefore, the engineering problem being considered is given a sensor configuration for a control architecture is it possible to match this against the sensing requirements without considering a full closed loop controller?The challenge for identifying sensor selection is how do you compare sensors if they measure different quantities? This presented an opportunity to use information provided by sensors to make quantitative comparisons. Current engineering processes are obsessed with metrics and finding improvements. The research aim of this thesis is to determine the most suitable sensor configuration for a given system and control objective. Sensing requirements of the control architecture could then be formed on this knowledge, quantifying the sensor selection process.The scope of the research presented is focussed on the evaluating the impact of sources of uncertainty within a control architecture. Variation between systems and sensor noise cause lead to uncertainty in the control parameters and controller objectives. This leads to a reduction in the overall performance and delivery of attributes of the system. Therefore, to identify the suitable set of sensors, an estimate on the confidence of a measurement and how uncertainty propagates through the system is required.The main contributions of the thesis can be defined as a methodology to quantify how much information a sensor contributes to a control architecture, without considering closed loop effects. The control architecture is represented as a nonlinear dynamic system to ensure realistic representation of automotive systems, which are typically heteroscedastic and multi-modal in nature. Information is defined as the confidence in the control variable being measured by representing parameters as distributions. This captures the uncertainty in the measurement and how it propagates through to variables of interest within the control architecture. The methodology uses Bayesian observers to model the control architecture and update the knowledge of the system using sensor measurements. By evaluating sensor information, sensing requirements for the optimal control architecture can be defined using quantitative information-based metrics. Control architectures that use different sensors to measure different variables can also be quantitatively compared. Consequently, the methodology can be used to support offline design for sensor selection of control architecture, contributing towards the development of an optimal control architecture. Further work has suggested the methods could be developed for online applications for embedded computational intelligence of automotive hardware.The novelty in this approach is that the existing literature suggested many approaches for controller design but no clear consensus on control architecture design. Many different approaches were identified in the literature for sensor selection but were applied to applications which were limited and so not practically applicable. The literature search did not identify probabilistic graphical modelling as a suitable approach to sensor selection, but its ability to model uncertainty propagation and recent developments in computational statistics made it an ideal solution.