Enhancing automotive stability control with artificial neural networks

Many studies of automotive crash statistics have shown that driver error is a major cause of accident and injury on the roads worldwide. This has lead to the development of many active control systems to aid the driver during panic maneuvers, such as antilock braking systems. Nonetheless, there has been a slow growth in the control methodology of these systems, with wheel speed regulation based on the information derived from a small number of sensors the norm across all past and present systems. To achieve greater performance gains, it is important to control more vehicle parameters and obtain vehicle state information from larger sensor arrays. Problems arise using traditional control methodology, as additional variables create exponential increases in control algorithm complexity, and in computational requirements. Artificial neural networks (ANN) are presented in literature as an artificial intelligence solution to approaching problems. Significant benefits include, the ability to model highly non-linear and complex systems, capacity to incorporate many model inputs and outputs, low computational requirements and capability for self-learning from observed data. However, previous work has largely been limited to simulation or very narrow practical testing, from which it is difficult to draw useful conclusions. This thesis addresses these problems by developing two new ANN systems, implemented in broad practical tests. The first uses suspension and wheel speed vibration to intelligently predict road surface conditions, which is a major performance limitation in all current systems. The second models complex vehicle dynamics through a large sensor array and ANN process optimisation to implement intelligent traction control. This method determines the optimal driven wheel speed for maximum acceleration in the driver’s desired direction, in a process that is generic and adaptable to current and future active control systems. All results are derived from a real test vehicle, which was adapted for this investigation. This included the installation of chassis and engine sensors, data acquisition and control systems, engine management hardware and user interfaces, as well as constructing ANN models and controllers in the NI LabVIEW language. The positive outcomes of this work are a step towards establishing new methods of active vehicle control on a statistical and quantitative basis