The cluster will develop basic theory and methodology for distributed optimization, learning and decision-making in large scale dynamic systems.
Vision for the Cluster
Background: Our modern society is critically dependent on large-scale networks for services such as energy supply, transportation and communications. The design and operation of such networks is becoming increasingly complex, due to their growing size, autonomy and heterogeneity. To address these difficulties, a systematic theory and methodology for large-scale optimization and control is needed. The cluster is addressing this challenge, building on leading academic expertise in related areas, in combination with strategic industrial and international partnerships.
Objective: New theory and methodology will be developed in the form of algorithms and analysis methods for dynamic operation of large scale systems. Optimization methods will be designed to operate in real time interaction with the physical environment. For scalable solutions, this means that data needs to processed in a distributed fashion, where local units for sensing, computation and actuation are coordinated via physical infrastructure as well as communication networks and broadcast technology. A relevant scenario is transmission and distribution of electrical power, where high penetration of wind and solar power needs to be balanced by flexible demand response triggered by economic incentives, with coupling to district heating networks and gas networks.
Another scenario is traffic control, where efficient methodology is needed to coordinate routing and speed control for individual cars with conflict resolution at intersections and coordination of traffic flow at a higher level.
Connection to other WASP Clusters: Many other WASP clusters are devoted to networks of autonomous systems, such as vehicles and robots, designed to interact with other agents in a complex environment. Here optimization is an important tool and scalability is a central issue. In particular, the cluster on Perception and Learning relies heavily on scalable methods for optimization. Another example is the cloud Autonomous Cloud and Networks which gives an implementation for methods developed in this cluster, while the Automated Transport Systems is focusing on an important application, where methods for large scale optimization and control play an important role.
Future Demonstrations: This cluster is closely related to the goals stated for the WASP WARA-CAT research arena. However, the truly large-scale aspects of the cluster will be hard to test in an artificial experimental environment. As a consequence, opportunities for full scale demonstrations will rely on collaborations with partners outside WASP, such as networks operators for traffic, power or district heating.
Scalable optimization in a dynamic environment
Optimization is already a widely used technology for dynamic control. The premier example is Model Predictive Control based on centralized information, which is widely used, but far from scalable. On the other hand, there is also a rapid growth of algorithms designed for scalability, but not in the context of dynamical systems. The challenge is to combine the two, such that stability and dynamic performance can be guaranteed, even when the algorithmic dynamics of scalable optimization are strongly coupled with dynamics of physics, humans and communications.
Global versus local information
The theory for control and decision-making is relatively straightforward when full information about the system is available. Another widely studied case is where information is limited, but localized and fixed. This case is much more complicated to analyze, but good practical solutions often exist. Our challenge is to develop scalable methods for control based on propagation of partial information, with provable bounds on dynamic performance. In this setting, global broadcast information could be combined with local information kept for internal use.
Adaptation and Learning
The rapid growth of machine learning algorithms during the past decades has drastically changed the way we approach modeling and decision-making in autonomous systems. Most likely, this development has only just started. However, most of the learning theory has been developed
without explicit regard for dynamics. For efficient integration of the new learning algorithms with control of dynamical systems we will build on established theory for adaptive control and systems identification, where stability and dynamic performance is explicitly taken into account.
Control through economic incentives
Many applications have a strong presence of independent decision-makers, responding to price variations and individual preferences. The integration and coordination of such decision-makers will be studied for a resource sharing network, where time-varying local demands are to be balanced by time-varying supplies elsewhere in the network. The actions of each node are governed by a desire to optimize a given utility function. In addition to local production and consumption, the exchange between nodes is limited by capacity constraints of the network links. Our objective is to understand and exploit the interaction between node demands and network dynamics. Privacy, security and fairness will be essential factors in evaluation of achievable performance.
There are numerous industrial challenges relating to this cluster. ABB is selling hardware and software for automation of industrial processes, where optimization plays a key role. Industrial trends are increasingly facilitating integration and data exchange between low level control loops
and high level process management and business decisions. This is increases the need for scalable optimization and control. Cloud technology (for example provided by Ericsson) stimulates this development further. Moreover, the “sharing economy” is creating new business models, where information exchange and price mechanisms enable more efficient solutions to many societal needs.
For industry, this means new opportunities. At the same time, also the role of government is becoming increasingly complex, creating a demand for analytical tools as provided in this cluster.
Mikael Johansson, KTH, email@example.com
Anders Rantzer , Lund University
Anders Hansson, Linköping University
Daniel Axehill, Linköping University
Bengt Lennartsson, Chalmers Samuel
Bo Bernhardsson, Lund University
Large-Scale Optimization for Distributed Control
Shervin Parvini Ahmadi, LiU
This PhD project will investigate network topology such as hierarchical network structure, e.g. chordal graphs, for scalable computations with applications to control in a wide sense, e.g. system identification, machine learning, control design, and estimation. We will also study how privacy affects the achievable performance and what amount of communication that is needed between different computational agents in order to achieve the most efficient overall computations. Different type of computational platforms will be investigated. We will also develop efficient optimization code for generic problem formulations. Relevant applications are within infrastructure networks for traffic, water, gas, electricity, and building control. Industrial contacts have been established with ABB Corporate Research in Switzerland.
Advanced real-time planning and decision making for autonomous systems
Kristoffer Bergman (industrial student), SAAB and LiU
This project aims at developing state-of-the-art real-time planning and decision making algorithms that we believe will play a key-role of the “intelligence” of future autonomous systems. We would like to, as far as possible, to work model based and minimize situation and platform specialization, as well as operator involvement. The algorithms should themselves find solutions, i.e., a sequence of decisions in time, to the given problems formulated in the form of a user-defined mission objective, constraints on behaviors and actions, and a model of relevant parts of the world (updated in realtime from observations and communication) where the platform acts.
Control using Distributed Information
Hamed Sadeghi, Lund University
Motivated by applications in infrastructure networks (mainly traffic and transportation) we are studying how network flows can be optimized using distributed controllers. Existing results for linear systems with an H-infinity objective will be generalized to accommodate non-linear flow constraints and other convex objectives. Tradeoffs between congestion reduction and shortest path solutions will be studied. During the initial phase, we will study how desirable static performance objectives can be met using distributed feedback. Objectives and constraints appearing in traffic and transportation networks will be emphasized. The next step is to design the dynamic properties of the distributed controllers, while keeping the static properties intact.
Integrated verification, optimization and learning
Fredrik Hagebring, academic PhD, Chalmers
This project focuses on combining formal verification and optimization in correct-by design control synthesis. The synthesis is based on specifications of the desired system behavior that may include performance, safety and liveness properties. Optimality in terms of cost, performance and energy is also crucial. To get a holistic view, correctness and optimality need to be further related. The goal is to obtain a unified framework that guarantees optimal and correct behavior of autonomous systems, including both controllable and uncontrollable/spontaneous dynamic behavior. Robustness concerning uncertainties and adaptation/learning based on changes in decisions and system behavior will be considered.
Towards the autonomous mine
Max Åstrand, industrial PhD, ABB and KTH
Underground mining is a cost-intensive process where the costs increase as production goes deeper. To make production more efficient the mining industry turns to automation and autonomous mining machinery. However, there are still many open problems that need to be addressed to realize the vision of a fully autonomous mine. This project will contribute towards this vision starting by studying automatic scheduling of an underground production fleet. Scheduling can be seen as the glue that unites long-term goals with their autonomous realization, and is thus a crucial part of a fully autonomous mine.
Embedded optimization for real-time machine learning
Sarit Khirirat, academic PhD, KTH
An increasing number of our daily decisions are guided by computers that sense, infer and act on the data that they observe. Many of these autonomous decision-making tasks are naturally posed as optimization problems. Examples include finding the best parameters (“training”) of a deep neural network, deriving an optimal decision rule for trading of volatile assets, or determining the best action of an autonomous vehicle under dynamic constraints and uncertain observations. Real-time optimization is becoming a critical technology for making better, faster and more informed autonomous decisions. In this project, we will develop advanced optimization algorithms that are able to run in real-time on embedded hardware. We aim at novel classes of optimization algorithms that can deliver optimal or near-optimal solutions with limited memory,
processing and energy resources. Particular attention will be given to algorithms that can exploit emerging multi-core/multi-GPU embedded platforms. The project will blend applied mathematics for algorithm development,
implementation of these algorithms on emerging hardware architectures, and application of real-time optimization to autonomous decision-making.