In this review paper, the integration of Machine Learning (ML) and Model Predictive Control
(MPC) in Automotive Control System (ACS) applications are discussed. ACS can be divided …

This paper proposes a cooperative strategy of connected and automated vehicles (CAVs)
longitudinal control for a mixed connected and automated traffic environment based on deep …

Reinforcement learning is concerned with a generic concept of an agent acting in an
environment. From the control theory standpoint, reinforcement learning may be considered …

This paper develops an integrated safety-enhanced reinforcement learning (RL) and model
predictive control (MPC) framework for autonomous vehicles (AVs) to navigate unsignalized …

The control of continuous-time environments while actively deciding when to take costly
observations in time is a crucial yet unexplored problem, particularly relevant to real-world …

We present a Newton-type method that converges fast from any initialization and for arbitrary
convex objectives with Lipschitz Hessians. We achieve this by merging the ideas of cubic …

This paper presents a review of the design and application of model predictive control
strategies for Micro Aerial Vehicles and specifically multirotor configurations such as …

Deep Reinforcement Learning (DRL) has recently been applied to eco-driving to intelligently
reduce fuel consumption and travel time. While previous studies synthesize simulators and …

Event-triggered model predictive control (eMPC) is a popular optimal control method with an
aim to alleviate the computation and/or communication burden of MPC. However, it …

This article proposes a novel reinforcement learning-based model predictive control
(RLMPC) scheme for discrete-time systems. The scheme integrates model predictive control …