Self-Learning Longitudinal Control for On-Road Vehicles

Reinforcement Learning is a promising tool to automate controller tuning. However, significant extensions are required for real-world applications to enable fast and robust learning. This work proposes several additions to the state of the art and proves their capability in a series of real world experiments

Self-Learning Longitudinal Control for On-Road Vehicles

Fahrerassistenzsysteme (Advanced Driver Assistance Systems) sind ein wichtiges Verkaufsargument für PKWs, fordern jedoch hohe Entwicklungskosten. Insbesondere die Parametrierung für Längsregelung, die einen wichtigen Baustein für Fahrerassistenzsysteme darstellt, benötigt viel Zeit und Geld, um die richtige Balance zwischen Insassenkomfort und Regelgüte zu treffen. Reinforcement Learning scheint ein vielversprechender Ansatz zu sein, um dies zu automatisieren. Diese Klasse von Algorithmen wurde bislang allerdings vorwiegend auf simulierte Aufgaben angewendet, die unter idealen Bedingungen stattfinden und nahezu unbegrenzte Trainingszeit ermöglichen. Unter den größten Herausforderungen für die Anwendung von Reinforcement Learning in einem realen Fahrzeug sind Trajektorienfolgeregelung und unvollständige Zustandsinformationen aufgrund von nur teilweise beobachteter Dynamik. Darüber hinaus muss ein Algorithmus, der in realen Systemen angewandt wird, innerhalb von Minuten zu einem Ergebnis kommen. Außerdem kann das Regelziel sich während der Laufzeit beliebig ändern, was eine zusätzliche Schwierigkeit für Reinforcement Learning Methoden darstellt. Diese Arbeit stellt zwei Algorithmen vor, die wenig Rechenleistung benötigen und diese Hürden überwinden. Einerseits wird ein modellfreier Reinforcement Learning Ansatz vorgeschlagen, der auf der Actor-Critic-Architektur basiert und eine spezielle Struktur in der Zustandsaktionswertfunktion verwendet, um mit teilweise beobachteten Systemen eingesetzt werden zu können. Um eine Vorsteuerung zu lernen, wird ein Regler vorgeschlagen, der sich auf eine Projektion und Trainingsdatenmanipulation stützt. Andererseits wird ein modellbasierter Algorithmus vorgeschlagen, der auf Policy Search basiert. Diesem wird eine automatisierte Entwurfsmethode für eine inversionsbasierte Vorsteuerung zur Seite gestellt. Die vorgeschlagenen Algorithmen werden in einer Reihe von Szenarien verglichen, in denen sie online, d.h. während der Fahrt und bei geschlossenem Regelkreis, in einem realen Fahrzeug lernen. Obwohl die Algorithmen etwas unterschiedlich auf verschiedene Randbedingungen reagieren, lernen beide robust und zügig und sind in der Lage, sich an verschiedene Betriebspunkte, wie zum Beispiel Geschwindigkeiten und Gänge, anzupassen, auch wenn Störungen während des Trainings einwirken. Nach bestem Wissen des Autors ist dies die erste erfolgreiche Anwendung eines Reinforcement Learning Algorithmus, der online in einem realen Fahrzeug lernt

Deep Learning -Powered Computational Intelligence for Cyber-Attacks Detection and Mitigation in 5G-Enabled Electric Vehicle Charging Station

An electric vehicle charging station (EVCS) infrastructure is the backbone of transportation electrification. However, the EVCS has various cyber-attack vulnerabilities in software, hardware, supply chain, and incumbent legacy technologies such as network, communication, and control. Therefore, proactively monitoring, detecting, and defending against these attacks is very important. The state-of-the-art approaches are not agile and intelligent enough to detect, mitigate, and defend against various cyber-physical attacks in the EVCS system. To overcome these limitations, this dissertation primarily designs, develops, implements, and tests the data-driven deep learning-powered computational intelligence to detect and mitigate cyber-physical attacks at the network and physical layers of 5G-enabled EVCS infrastructure. Also, the 5G slicing application to ensure the security and service level agreement (SLA) in the EVCS ecosystem has been studied. Various cyber-attacks such as distributed denial of services (DDoS), False data injection (FDI), advanced persistent threats (APT), and ransomware attacks on the network in a standalone 5G-enabled EVCS environment have been considered. Mathematical models for the mentioned cyber-attacks have been developed. The impact of cyber-attacks on the EVCS operation has been analyzed. Various deep learning-powered intrusion detection systems have been proposed to detect attacks using local electrical and network fingerprints. Furthermore, a novel detection framework has been designed and developed to deal with ransomware threats in high-speed, high-dimensional, multimodal data and assets from eccentric stakeholders of the connected automated vehicle (CAV) ecosystem. To mitigate the adverse effects of cyber-attacks on EVCS controllers, novel data-driven digital clones based on Twin Delayed Deep Deterministic Policy Gradient (TD3) Deep Reinforcement Learning (DRL) has been developed. Also, various Bruteforce, Controller clones-based methods have been devised and tested to aid the defense and mitigation of the impact of the attacks of the EVCS operation. The performance of the proposed mitigation method has been compared with that of a benchmark Deep Deterministic Policy Gradient (DDPG)-based digital clones approach. Simulation results obtained from the Python, Matlab/Simulink, and NetSim software demonstrate that the cyber-attacks are disruptive and detrimental to the operation of EVCS. The proposed detection and mitigation methods are effective and perform better than the conventional and benchmark techniques for the 5G-enabled EVCS

Neuro_Dynamic Programming and Reinforcement Learning for Optimal Energy Management of a Series Hydraulic Hybrid Vehicle Considering Engine Transient Emissions.

Sequential decision problems under uncertainty are encountered in various fields such as optimal control and operations research. In this dissertation, Neuro-Dynamic Programming (NDP) and Reinforcement Learning (RL) are applied to address policy optimization problems with multiple objectives and large design state space. Dynamic Programming (DP) is well suited for determining an optimal solution for constrained nonlinear model based systems. However, DP suffers from curse of dimensionality i.e. computational effort grows exponentially with state space. The new algorithms address this problem and enable practical application of DP to a much broader range of problems. The other contribution is to design fast and computationally efficient transient emission models. The power management problem for a hybrid vehicle can be formulated as an infinite time horizon stochastic sequential decision-making problem. In the past, policy optimization has been applied successfully to design optimal supervisory controller for best fuel economy. Static emissions have been considered too but engine research has shown that transient operation can have significant impact on real-world emissions. Modeling transient emissions results in addition of more states. Therefore, the problem with multiple objectives i.e. minimize fuel consumption and transient particulate and NOX emissions, becomes computationally intractable by DP. This research captures the insight with models and brings it into the supervisory controller design. A self-learning supervisory controller is designed based on the principles of NDP and RL. The controller starts “naïve” i.e. with no knowledge to control the onboard power but learns to do so in an optimal manner after interacting with the system. The controller tries to minimize multiple objectives and continues to evolve until a global solution is achieved. Virtual sensors for predicting real-time transient particulate and NOX emissions are developed using neuro-fuzzy modeling technique, which utilizes a divide-and-conquer strategy. The highly nonlinear engine operating space is partitioned into smaller subspaces and a separate local model is trained to for each subspace. Finally, the supervisory controller along with virtual emission sensors is implemented and evaluated using the Engine-In-the-Loop (EIL) setup. EIL is a unique facility to systematically evaluate control methodologies through concurrent running of real engine and a virtual hybrid powertrain.Ph.D.Mechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/89829/1/rajit_1.pd

Multi-Robot Systems: Challenges, Trends and Applications

This book is a printed edition of the Special Issue entitled “Multi-Robot Systems: Challenges, Trends, and Applications” that was published in Applied Sciences. This Special Issue collected seventeen high-quality papers that discuss the main challenges of multi-robot systems, present the trends to address these issues, and report various relevant applications. Some of the topics addressed by these papers are robot swarms, mission planning, robot teaming, machine learning, immersive technologies, search and rescue, and social robotics

Systems Structure and Control

The title of the book System, Structure and Control encompasses broad field of theory and applications of many different control approaches applied on different classes of dynamic systems. Output and state feedback control include among others robust control, optimal control or intelligent control methods such as fuzzy or neural network approach, dynamic systems are e.g. linear or nonlinear with or without time delay, fixed or uncertain, onedimensional or multidimensional. The applications cover all branches of human activities including any kind of industry, economics, biology, social sciences etc