Hybrid state machine model for fast model predictive control: application to path tracking

Abstract

Cyber-Physical Systems (CPS) are composed of computing devices interacting with physical systems. Model-based design is a powerful methodology in CPS design in the implementation of control systems. For instance, Model Predictive Control (MPC) is typically implemented in CPS applications, e.g., in path tracking of autonomous vehicles. MPC deploys a model to estimate the behavior of the physical system at future time instants for a specific time horizon. Ordinary Differential Equations (ODE) are the most commonly used models to emulate the behavior of continuous-time (non-)linear dynamical systems. A complex physical model may comprise thousands of ODEs which pose scalability, performance and power consumption challenges. One approach to address these model complexity challenges are frameworks that automate the development of model-to-model transformation. In this paper, we introduce a model generation framework to transform ODE models of a physical system to Hybrid Harmonic Equivalent State (HES) Machine model equivalents. Moreover, tuning parameters are introduced to reconfigure the model and adjust its accuracy from coarse-grained time critical situations to fine-grained scenarios in which safety is paramount. Machine learning techniques are applied to adopt the model to run-time applications. We conduct experiments on a closed-loop MPC for path tracking using the vehicle dynamics model. We analyze the performance of the MPC when applying our Hybrid HES Machine model. The performance of our proposed model is compared with state-of-the-art ODE-based models, in terms of execution time and model accuracy. Our experimental results show a 32% reduction in MPC return time for 0.8% loss in model accuracy.