Advanced control methodologies have helped the development of modern vehicles that are capable of path planning and path following. For instance, Model Predictive Control (MPC) employs a predictive model to predict the behavior of the physical system for a specific time horizon in the future. An optimization problem is solved to compute optimal control actions while handling model uncertainties and nonlinearities. However, these prediction routines are computationally intensive and the computational overhead grows with the complexity of the model. Switching MPC addresses this issue by combining multiple predictive models, each with a different precision granularity. In this artcle, we proposed a novel switching predictive control method based on a model reduction scheme to achieve various model granularities for path following in autonomous vehicles. A state-based model with tunable parameters is proposed to operate as a reconfigurable predictive model of the vehicle. A runtime switching algorithm is presented that selects the best model using machine learning. We employed a metric that formulates the tradeoff between the error and computational savings due to model reduction. Our simulation results show that the use of the predictive model in the switching scheme as opposed to single granularity scheme, yields a 45% decrease in execution time in tradeoff for a small 12% loss in accuracy in prediction of future outputs and no loss of accuracy in tracking the reference trajectory.

Advances in sensing, computation, storage and actuation technologies have entered cyber-physical systems (CPSs) into the smart era where complex control applications requiring high performance are supported. Neural networks (NNs) models are proposed as a predictive model to be used in model predictive control (MPC) applications. However, the ability to efficiently exploit resource hungry NNs in embedded resource-bound settings is a major challenge. In this paper, we propose priority neuron network (PNN), a resource-aware NNs model that can be reconfigured into smaller subnetworks at runtime. This approach enables a tradeoff between the model's computation time and accuracy based on available resources. The PNN model is memory efficient since it stores only one set of parameters to account for various subnetwork sizes. We propose a training algorithm that applies regularization techniques to constrain the activation value of neurons and assigns a priority to each one. We consider the neuron's ordinal number as our priority criteria in that the priority of the neuron is inversely proportional to its ordinal number in the layer. This imposes a relatively sorted order on the activation values. We conduct experiments to employ our PNN as the predictive model of a vehicle in MPC for path tracking. To corroborate the effectiveness of our proposed methodology, we compare it with two state-of-the-art methods for resource-aware NN design. Compared to state-of-the-art work, our approach can cut down the training time by 87% and reduce the memory storage by 75% while achieving similar accuracy. Moreover, we decrease the computation overhead for the model reduction process that searches for n neurons below a threshold, from O(n) to O(logn).

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.