Public transport trajectory planning with probabilistic guarantees

The paper proposes an eco-cruise control strategy for urban public transportbuses. The aim of the velocity control is ensuring timetable adherence, whileconsidering upstream queue lengths at traffic lights in a probabilistic way. Thecontribution of the paper is twofold. First, the shockwave profile model (SPM)is extended to capture the stochastic nature of traffic queue lengths. The modelis adequate to describe frequent traffic state interruptions at signalized intersections.Based on the distribution function of stochastic traffic volume demand,the randomness in queue length, wave fronts, and vehicle numbers are derived.Then, an outlook is provided on its applicability as a full-scale urban traffic networkmodel. Second, a shrinking horizon model predictive controller (MPC) isproposed for ensuring timetable reliability. The intention is to calculate optimalvelocity commands based on the current position and desired arrival time of thebus while considering upcoming delays due to red signals and eventual queues.The above proposed stochastic traffic model is incorporated in a rolling horizonoptimization via chance-constraining. In the optimization, probabilistic guaranteesare formulated to minimize delay due to standstill in queues at signalized intersections. Optimization results are analyzed from two particular aspects, (i)feasibility and (ii) closed-loop performance point of views. The novel stochasticprofile model is tested in a high fidelity traffic simulator context. Comparativesimulation results show the viability and importance of stochastic bounds in urbantrajectory design. The proposed algorithm yields smoother bus trajectoriesat an urban corridor, suggesting energy savings compared to benchmark controlstrategies

Multi-advisor deep reinforcement learning for smart home energy control

Effective automated smart home control is essential for smart-grid enabled approaches to demand response, named in the literature as automated demand response. At it’s heart, this is a multi-objective adaptive control problem because it requires balancing an appliance’s primary objective with demandresponse motivated objectives. This control problem is difficult due to the scale and heterogeneity of appliances as well as the time-varying nature of both dynamics and consumer preferences. Computational considerations further limit the types of acceptable algorithms to apply to the problem. We propose approaching the problem under the multi-objective reinforcement learning framework. We suggest a multi-agent multi-advisor reinforcement learning system to handle the consumer’s time-varying preferences across objectives. We design some simulations to produce preliminary results on the nature of user preferences and the feasibility of multi-advisor reinforcement learning. Further smarthome simulations are designed to demonstrate the linear scalability of the algorithm with respect to both number of agents and number of objectives. We demonstrate the algorithms performance in simulation against a comparable centrallized and decentrallized controller. Finally, we identify the need for stronger performance measures for a system of this type by considering the effect on agents of newly selected preferences

Seventh International Workshop on Simulation, 21-25 May, 2013, Department of Statistical Sciences, Unit of Rimini, University of Bologna, Italy. Book of Abstracts

Seventh International Workshop on Simulation, 21-25 May, 2013, Department of Statistical Sciences, Unit of Rimini, University of Bologna, Italy. Book of Abstract

Advanced Control and Estimation Concepts, and New Hardware Topologies for Future Mobility

According to the National Research Council, the use of embedded systems throughout society could well overtake previous milestones in the information revolution. Mechatronics is the synergistic combination of electronic, mechanical engineering, controls, software and systems engineering in the design of processes and products. Mechatronic systems put “intelligence” into physical systems. Embedded sensors/actuators/processors are integral parts of mechatronic systems. The implementation of mechatronic systems is consistently on the rise. However, manufacturers are working hard to reduce the implementation cost of these systems while trying avoid compromising product quality. One way of addressing these conflicting objectives is through new automatic control methods, virtual sensing/estimation, and new innovative hardware topologies

Seventh International Workshop on Simulation, 21-25 May, 2013, Department of Statistical Sciences, Unit of Rimini, University of Bologna, Italy. Book of Abstracts

Seventh International Workshop on Simulation, 21-25 May, 2013, Department of Statistical Sciences, Unit of Rimini, University of Bologna, Italy. Book of Abstract

Proceedings of the 2009 Joint Workshop of Fraunhofer IOSB and Institute for Anthropomatics, Vision and Fusion Laboratory

The joint workshop of the Fraunhofer Institute of Optronics, System Technologies and Image Exploitation IOSB, Karlsruhe, and the Vision and Fusion Laboratory (Institute for Anthropomatics, Karlsruhe Institute of Technology (KIT)), is organized annually since 2005 with the aim to report on the latest research and development findings of the doctoral students of both institutions. This book provides a collection of 16 technical reports on the research results presented on the 2009 workshop