相关论文: Eco-Coasting Strategies Using Road Grade Preview: …

State-of-the-art model-based Reinforcement Learning (RL) approaches either use gradient-free, population-based methods for planning, learned policy networks, or a combination of policy networks and planning. Hybrid approaches that combine…

This paper introduces a centralized approach for fuel-efficient urban platooning by leveraging real-time Vehicle- to-Everything (V2X) communication and Signal Phase and Timing (SPaT) data. A nonlinear Model Predictive Control (MPC)…

This paper presents a novel Nonlinear Model Predictive Control (NMPC) scheme for underwater robotic vehicles operating in a constrained workspace including static obstacles. The purpose of the controller is to guide the vehicle towards…

This work introduces an efficient power management approach for shipboard microgrids that integrates diesel generators, a fuel cell, and battery energy storage system. This strategy addresses both unit commitment and power dispatch,…

In this project, we attempt to optimize a landing trajectory of a rocket. The goal is to minimize the total fuel consumption during the landing process using different techniques. Once the optimal and feasible trajectory is generated using…

In this paper we present a model predictive control (MPC) approach to optimize vehicle scheduling and routing in an autonomous mobility-on-demand (AMoD) system. In AMoD systems, robotic, self-driving vehicles transport customers within an…

A nonparametric fuel consumption model is developed and used for eco-routing algorithm development in this paper. Six months of driving information from the city of Ann Arbor is collected from 2,000 vehicles. The road grade information from…

The energy consumption of an exascale High-Performance Computing (HPC) supercomputer rivals that of tens of thousands of people in terms of electricity demand. Given the substantial energy footprint of exascale HPC systems and the…

Sequential and terminal constraint feasibility of the model predictive control (MPC) play important roles in ensuring MPC control continuity. This study thus investigates these two properties theoretically using an MPC model for vehicle…

In this paper, we consider the problem of periodic optimal control of nonlinear systems subject to online changing and periodically time-varying economic performance measures using model predictive control (MPC). The proposed economic MPC…

Autonomous Underwater Vehicles (AUVs) are a highly promising technology for ocean exploration and diverse offshore operations, yet their practical deployment is constrained by energy efficiency and endurance. To address this, we propose…

In this paper, we propose a combined energy and comfort optimization (CECO) strategy for the air conditioning (A/C) system of the connected and automated vehicles (CAVs). By leveraging the weather and traffic predictions enabled by the…

This paper presents a safe learning-based eco-driving framework tailored for mixed traffic flows, which aims to optimize energy efficiency while guaranteeing safety during real-system operations. Even though reinforcement learning (RL) is…

Predictive energy management of Connected and Automated Vehicles (CAVs), in particular those with multiple power sources, has the potential to significantly improve energy savings in real-world driving conditions. In particular, the…

Connected and Automated Vehicles (CAVs) have real-time information from the surrounding environment by using local on-board sensors, V2X (Vehicle-to-Everything) communications, pre-loaded vehicle-specific lookup tables, and map database.…

Model predictive control (MPC) has emerged as an effective strategy for water distribution systems (WDSs) management. However, it is hampered by the computational burden for large-scale WDSs due to the combinatorial growth of possible…

A finite horizon optimal tracking problem is considered for linear dynamical systems subject to parametric uncertainties in the state-space matrices and exogenous disturbances. A suboptimal solution is proposed using a model predictive…

Control schemes for autonomous systems are often designed in a way that anticipates the worst case in any situation. At runtime, however, there could exist opportunities to leverage the characteristics of specific environment and operation…

Optimization-based approaches such as Model Predictive Control (MPC) are promising approaches in proactive control for safety-critical applications with changing environments such as automated driving systems. However, the computational…

We address eco-driving at roundabouts in mixed traffic to enhance traffic flow and traffic efficiency in urban areas. The aim is to proactively optimize speed of automated or non-automated connected vehicles (CVs), ensuring both an…