13 research outputs found
Co-Design of Autonomous Systems: From Hardware Selection to Control Synthesis
Designing cyber-physical systems is a complex task which requires insights at
multiple abstraction levels. The choices of single components are deeply
interconnected and need to be jointly studied. In this work, we consider the
problem of co-designing the control algorithm as well as the platform around
it. In particular, we leverage a monotone theory of co-design to formalize
variations of the LQG control problem as monotone feasibility relations. We
then show how this enables the embedding of control co-design problems in the
higher level co-design problem of a robotic platform. We illustrate the
properties of our formalization by analyzing the co-design of an autonomous
drone performing search-and-rescue tasks and show how, given a set of desired
robot behaviors, we can compute Pareto efficient design solutions.Comment: 8 pages, 6 figures, to appear in the proceedings of the 20th European
Control Conference (ECC21
Task-driven Modular Co-design of Vehicle Control Systems
When designing autonomous systems, we need to consider multiple trade-offs at
various abstraction levels, and the choices of single (hardware and software)
components need to be studied jointly. In this work we consider the problem of
designing the control algorithm as well as the platform on which it is
executed. In particular, we focus on vehicle control systems, and formalize
state-of-the-art control schemes as monotone feasibility relations. We then
show how, leveraging a monotone theory of co-design, we can study the embedding
of control synthesis problems into the task-driven co-design problem of a
robotic platform. The properties of the proposed approach are illustrated by
considering urban driving scenarios. We show how, given a particular task, we
can efficiently compute Pareto optimal design solutions.Comment: 8 pages, 7 figures. Proceedings of the 2022 IEEE 61th Conference on
Decision and Contro
Reliability-aware Control of Power Converters in Mobility Applications
This paper introduces an automatic control method designed to enhance the
operation of electric vehicles, besides the speed tracking objectives, by
including reliability and lifetime requirements. The research considers an
automotive power converter which supplies electric power to a permanent magnet
synchronous motor (PMSM). The primary control objective is to mitigate the
thermal stress on the power electronic Insulate Gate Bipolar Transistors
(IGBTs), while simultaneously ensuring effective speed tracking performance. To
achieve these goals, we propose an extended H-inf design framework, which
includes reliability models. The method is tested in two distinct scenarios:
reliability-aware, and reliability-free cases. Furthermore, the paper conducts
a lifetime analysis of the IGBTs, leveraging the Rainflow algorithm and
temperature data.Comment: Submitted to ECC 20204 conferenc
Categorification of Negative Information using Enrichment
In many applications of category theory it is useful to reason about “negative information”. For example, in planning problems, providing an optimal solution is the same as giving a feasible solution (the “positive” information) together with a proof of the fact that there cannot be feasible solutions better than the one given (the “negative” information). We model negative information by introducing the concept of “norphisms”, as opposed to the positive information of morphisms. A “nategory” is a category that has “Nom”-sets in addition to hom-sets, and specifies the compatibility rules between norphisms and morphisms. With this setup we can choose to work in “coherent” “subnategories”: subcategories that describe a potential instantiation of the world in which all morphisms and norphisms are compatible. We derive the composition rules for norphisms in a coherent subnategory; we show that norphisms do not compose by themselves, but rather they need to use morphisms as catalysts. We have two distinct rules of the type morphism+norphism→norphism. We then show that those complex rules for norphism inference are actually as natural as the ones for morphisms, from the perspective of enriched category theory. Every small category is enriched over P = ⟨Set, ×, 1⟩. We show that we can derive the machinery of norphisms by considering an enrichment over a certain monoidal category called PN (for “positive”/“negative”). In summary, we show that an alternative to considering negative information using logic on top of the categorical formalization is to “categorify” the negative information, obtaining negative arrows that live at the same level as the positive arrows, and suggest that the new inference rules are born of the same substance from the perspective of enriched category theory.ISSN:2075-218
Analysis and Control of Autonomous Mobility-on-Demand Systems
Challenged by urbanization and increasing travel needs, existing trans- portation systems call for new mobility paradigms. In this article, we present the emerging concept of Autonomous Mobility-on-Demand, whereby centrally orchestrated fleets of autonomous vehicles provide mobility service to customers. We provide a comprehensive review of methods and tools to model and solve problems related to Autonomous Mobility-on-Demand systems. Specifically, we first identify problem settings for their analysis and control, both from the operational and the planning perspective. We then review modeling aspects, including transportation networks, transportation demand, congestion, opera- tional constraints, and interactions with existing infrastructure. There- after, we provide a systematic analysis of existing solution methods and performance metrics, highlighting trends and trade-offs. Finally, we present various directions for further research.ISSN:2573-514
Co-Design of Embodied Intelligence: A Structured Approach
We consider the problem of co-designing em- bodied intelligence as a whole in a structured way, from hardware components such as propulsion systems and sensors to software modules such as control and perception pipelines. We propose a principled approach to formulate and solve complex embodied intelligence co-design problems, leveraging a monotone co-design theory. The methods we propose are intuitive and integrate heterogeneous engineering disciplines, allowing analytical and simulation-based modeling techniques and enabling interdisciplinarity. We illustrate through a case study how, given a set of desired behaviors, our framework is able to compute Pareto efficient solutions for the entire hardware and software stack of a self-driving vehicle