Research on Information Flow Topology for Connected Autonomous Vehicles

Information flow topology plays a crucial role in connected autonomous vehicles (CAVs). It describes how CAVs communicate and exchange information with each other. It predominantly affects the platoon\u27s performance, including the convergence time, robustness, stability, and scalability. It also dramatically affects the controller design of CAVs. Therefore, studying information flow topology is necessary to ensure the platoon\u27s stability and improve its performance. Advanced sliding mode controllers and optimisation strategies for information flow topology are investigated in this project. Firstly, the impact of information flow topology on the platoon is studied regarding tracking ability, fuel economy and driving comfort. A Pareto optimal information flow topology offline searching approach is proposed using a non-dominated sorting genetic algorithm (NSGA-II) to improve the platoon\u27s overall performance while ensuring stability. Secondly, the concept of asymmetric control is introduced in the topological matrix. For a linear CAVs model with time delay, a sliding mode controller is designed to target the platoon\u27s tracking performance. Moreover, the Lyapunov analysis is used via Riccati inequality to guarantee the platoon\u27s internal stability and input-to-output string stability. Then NSGA-II is used to find the homogeneous Pareto optimal asymmetric degree to improve the platoon\u27s performance. A similar approach is designed for a nonlinear CAVs model to find the Pareto heterogeneous asymmetric degree and improve the platoon\u27s performance. Thirdly, switching topology is studied to better deal with the platoon\u27s communication problems. A two-step switching topology framework is introduced. In the first step, an offline Pareto optimal topology search with imperfect communication scenarios is applied. The platoon\u27s performance is optimised using a multi-objective evolutionary algorithm based on decomposition (MOEA/D). In the second step, the optimal topology is switched and selected from among the previously obtained Pareto optimal topology candidates in real-time to minimise the control cost. For a continuous nonlinear heterogeneous platoon with actuator faults, a sliding mode controller with an adaptive mechanism is developed. Then, the Lyapunov approach is applied to the platoon\u27s tracking error dynamics, ensuring the systems uniformly ultimately bounded stability and string stability. For a discrete nonlinear heterogeneous platoon with packet loss, a discrete sliding mode controller with a double power reaching law is designed, and a modified MOEA/D with two opposing adaptive mechanisms is applied in the two-step framework. Simulations verify all the proposed controllers and frameworks, and experiments also test some. The results show the proposed strategy\u27s effectiveness and superiority in optimising the platoon\u27s performance with multiple objectives

Distributed Model Reference Adaptive Control for Vehicle Platoons with Uncertain Dynamics

This paper proposes a distributed model reference adaptive controller (DMRAC) for vehicle platoons with constant spacing policy, subjected to uncertainty in control effectiveness and inertial time lag. It formulates the uncertain vehicle dynamics as a matched uncertainty, and is applicable for both directed and undirected topologies. The directed topology must contain at least one spanning tree with the leader as a root node, while the undirected topology must be static and connected with at least one follower receiving information from the leader. The proposed control structure consists of a reference model and a main control system. The reference model is a closed-loop system constructed from the nominal model of each follower vehicle and a reference control signal. The main control system consists of a nominal control signal based on cooperative state feedback and an adaptive term. The nominal control signal allows the followers cooperatively track the leader, while the adaptive term suppresses the effects of uncertainties. Stability analysis shows that global tracking errors with respect to the reference model and with respect to the leader are asymptotically stable. The states of all followers synchronize to both the reference and leader states. Moreover, with the existence of unknown external disturbances, the global tracking errors remain uniformly ultimately bounded. The performance of the controlled system is verified through the simulations and validates the efficacy of the proposed controller

A minimal sensing and communication control strategy for adaptive platooning

Several cooperative driving strategies proposed in literature, sometimes known as cooperative adaptive cruise control strategies, assume that both relative spacing and relative velocity with preceding vehicle are available from on-board sensors (laser or radar). Alternatively, these strategies assume communication of both velocity states and acceleration inputs from preceding vehicle. However, in practice, on-board sensors can only measure relative spacing with preceding vehicle (since getting relative velocity requires additional filtering algorithms); also, reducing the number of variables communicated from preceding vehicle is crucial to save bandwidth. In this work we show that, after framing the cooperative driving task as a distributed model reference adaptive control problem, the platooning task can be achieved in a minimal sensing and communication scenario, that is, by removing relative velocity measurements with preceding vehicle and by removing communication from preceding vehicle of velocity states. In the framework we propose, vehicle parametric uncertainty is taken into account by appropriately designed adaptive laws. The proposed framework is illustrated and shown to be flexible to several standard architectures used in cooperative driving (one-vehicle look-ahead topology, leader-to-all topology, multivehicle look-ahead topology)

Process Simulation of Technical Precipitation Processes - The Influence of Mixing

This work develops and shows up methods to tackle multi-scale challenges in particle formation during precipitation crystallization. Firstly, molecular, micro- and meso-scale interactions in confined impinging jet mixers are investigated and simulatively predicted. Secondly, to build up on developed methods, macroscale as present for instance in stirred tank reactors is added to the considerations

Model Order Reduction

An increasing complexity of models used to predict real-world systems leads to the need for algorithms to replace complex models with far simpler ones, while preserving the accuracy of the predictions. This three-volume handbook covers methods as well as applications. This third volume focuses on applications in engineering, biomedical engineering, computational physics and computer science

Volume 1 – Symposium

We are pleased to present the conference proceedings for the 12th edition of the International Fluid Power Conference (IFK). The IFK is one of the world’s most significant scientific conferences on fluid power control technology and systems. It offers a common platform for the presentation and discussion of trends and innovations to manufacturers, users and scientists. The Chair of Fluid-Mechatronic Systems at the TU Dresden is organizing and hosting the IFK for the sixth time. Supporting hosts are the Fluid Power Association of the German Engineering Federation (VDMA), Dresdner Verein zur Förderung der Fluidtechnik e. V. (DVF) and GWT-TUD GmbH. The organization and the conference location alternates every two years between the Chair of Fluid-Mechatronic Systems in Dresden and the Institute for Fluid Power Drives and Systems in Aachen. The symposium on the first day is dedicated to presentations focused on methodology and fundamental research. The two following conference days offer a wide variety of application and technology orientated papers about the latest state of the art in fluid power. It is this combination that makes the IFK a unique and excellent forum for the exchange of academic research and industrial application experience. A simultaneously ongoing exhibition offers the possibility to get product information and to have individual talks with manufacturers. The theme of the 12th IFK is “Fluid Power – Future Technology”, covering topics that enable the development of 5G-ready, cost-efficient and demand-driven structures, as well as individual decentralized drives. Another topic is the real-time data exchange that allows the application of numerous predictive maintenance strategies, which will significantly increase the availability of fluid power systems and their elements and ensure their improved lifetime performance. We create an atmosphere for casual exchange by offering a vast frame and cultural program. This includes a get-together, a conference banquet, laboratory festivities and some physical activities such as jogging in Dresden’s old town.:Group A: Materials Group B: System design & integration Group C: Novel system solutions Group D: Additive manufacturing Group E: Components Group F: Intelligent control Group G: Fluids Group H | K: Pumps Group I | L: Mobile applications Group J: Fundamental