Investigation and Tesing of Predictive Modelling for Automation Safety

This study compares traditional control methods and Predictive Modelling control for vehicle speed control, highlighting their strengths and limitations in dynamic environments. While traditional linear/non-linear controllers have been widely used due to their simplicity and effectiveness in linear systems, they perform well only in stable conditions and struggle with non-linearities, requiring frequent re-tuning. In contrast, Model Predictive Control (MPC) offers improved accuracy, faster settling times, and scope for accounting for the vibrational behaviour of different composite materials, making it more suitable for complex vehicle systems. Predictive Modelling provides a more adaptive and precise solution, especially beneficial in dynamic environments where robustness to disturbances like road gradients is crucial. This makes it a promising approach for modern vehicles, where handling diverse driving conditions effectively is essential for safety and efficiency. By leveraging model predictive control (MPC), control inputs are optimized based on future system predictions, enhancing overall vehicle performance in a variety of scenarios.