IEEE.org | IEEE Xplore Digital Library | IEEE Standards | IEEE Spectrum | More Sites
Call for Award Nominations
More Info
There are many success stories and major achievements in control, something remarkable for a field as young as 50 years. In this talk we will sample these accomplishments and speculate about the future prospects of control. While there are examples of feedback from ancient times, extensive use of feedback paralleled industrialization: steam, electricity, communication, transportation etc. Control was established as a field in the period 1940-1960, when the similarities of control in widely different fields were recognized. Control constituted a paradigm shift in engineering that cut across the traditional engineering disciplines: mechanical, electrical, chemical, aerospace. A holistic view of control systems with a unified theory emerged in the 1950s, triggered by military efforts during the Second World War. The International Federation of Automatic Control (IFAC) was formed in 1956. Education in control spread rapidly to practically all engineering disciplines. Conferences and journals also appeared. A second phase, driven by the space race and the emergence of computers, started around 1960. Theory developed dramatically as did industrial applications. A large number of sub-specialties appeared and perhaps due to this, the holistic view of the field was lost. In my opinion we are now entering a third phase driven by the ubiquitous use of control and a strong interest in feedback and control among fellow scientists in physics and biology. There are also new areas driven by networked systems, autonomy and safe design of large complex systems. What will happen next depends largely on how we respond to the new challenges and on how we manage to recapture the holistic view of systems. Key issues will be education and interaction with other disciplines.
This talk will focus on progress towards a more “unified” theory for complex networks involving several elements: hard limits on achievable robust performance ( “laws”), the organizing principles that succeed or fail in achieving them (architectures and protocols), the resulting high variability data and “robust yet fragile” behavior observed in real systems and case studies (behavior, data), and the processes by which systems evolve (variation, selection, design). Insights into what the potential universal laws, architecture, and organizational principles are can be drawn from three converging research themes. First, detailed description of components and a growing attention to systems in biology and neuroscience, the organizational principles of organisms and evolution are becoming increasingly. Biologists are articulating richly detailed explanations of biological complexity, robustness, and evolvability that point to universal principles and architectures. Second, while the components differ and the system processes are far less integrated, advanced technology’s complexity is now approaching biology’s and there are striking convergences at the level of organization and architecture, and the role of layering, protocols, and feedback control in structuring complex multiscale modularity. Third, new mathematical frameworks for the study of complex networks suggests that this apparent network-level evolutionary convergence within/between biology/technology is not accidental, but follows necessarily from their universal system requirements to be fast, efficient, adaptive, evolvable, and most importantly, robust to perturbations in their environment and component part. We have the beginnings of the underlying mathematical framework and also a series of case studies in classical problems in complexity from statistical mechanics, turbulence, cell biology, human physiology and medicine, neuroscience, wildfire, earthquakes, economics, the Internet, and smartgrid. A workshop preceding CDC will explore this in more detail (with Pablo Parrilo). Selected references: [1] M Chiang, SH Low, AR Calderbank, JC. Doyle (2006) Layering As Optimization Decomposition, PROCEEDINGS OF THE IEEE, Volume: 95 Issue: 1 Jan 2007 [2]Alderson DL, Doyle JC (2010) Contrasting views of complexity and their implications for network-centric infrastructures. IEEE Trans Systems Man Cybernetics—Part A: Syst Humans 40:839-852. [3]H. Sandberg, J. C. Delvenne, J. C. Doyle. On Lossless Approximations, the Fluctuation-Dissipation Theorem, and Limitations of Measurements, IEEE Trans Auto Control, Feb 2011 [4]Chandra F, Buzi G, Doyle JC (2011) Glycolytic oscillations and limits on robust efficiency. Science, Vol 333, pp 187-192. [5]JC Doyle, ME Csete (2011) Architecture, Constraints, and Behavior, P Natl Acad Sci USA, in press, available online [6]Gayme DF, McKeon BJ, Bamieh B, Papachristodoulou P, Doyle JC (2011) Amplification and Nonlinear Mechanisms in Plane Couette Flow, Physics of Fluids, in press (published online 17 June 2011)
Cyber-physical systems combine a cyber side (computing and networking) with a physical side (mechanical, electrical, and chemical processes). Such systems present the biggest challenges as well as the biggest opportunities in several large industries, including electronics, energy, automotive, defense and aerospace, telecommunications, instrumentation, industrial automation. Engineers today do successfully design cyber-physical systems in a variety of industries. Unfortunately, the development of systems is costly, and development schedules are difficult to stick to. The complexity of cyber-physical systems, and particularly the increased performance that is offered from interconnecting what in the past have been separate systems, increases the design and verification challenges. As the complexity of these systems increases, our inability to rigorously model the interactions between the physical and the cyber sides creates serious vulnerabilities. Systems become unsafe, with disastrous inexplicable failures that could not have been predicted. Distributed control of multi-scale complex systems is largely an unsolved problem. A common view that is emerging in research programs in Europe and the US is "enabling contract-based design (CBD)," which formulates a broad and aggressive scope to address urgent needs in the systems industry. We present a design methodology and a few examples in controller design whereby contract-based design can be merged with platform-based design to formulate the design process as a meet-in-the-middle approach, where design requirements are implemented in a subsequent refinement process using as much as possible elements from a library of available components. Contracts are formalizations of the conditions for correctness of element integration (horizontal contracts), for lower level of abstraction to be consistent with the higher ones, and for abstractions of available components to be faithful representations of the actual parts (vertical contracts).
More information provided here.
Fifty years ago, control and computing were part of a broader system science. After a long period of intra-disciplinary development which resulted in control and computing being distant from each other, embedded and hybrid systems have challenged us to unite the, now developed, theories of continuous control and discrete computing on a broader system theoretic basis. In this talk, we will present a notion of system approximation that applies to both discrete and continuous systems by developing notions of approximate language inclusion, approximate simulation, and approximate bisimulation relations. We define a hierarchy of approximation pseudo-metrics between two systems that quantify the quality of the approximation, and capture the established notions in computer science as zero sections. Algorithms are developed for computing the proposed pseudo-metrics, both exactly and approximately. The exact algorithms require the generalization of the fixed point algorithms for computing simulation and bisimulation relations, or dually, the solution of a static game whose cost is the so-called branching distance between the systems. Approximations for the pseudo-metrics can be obtained by considering Lyapunov-like functions called simulation and bisimulation functions. Our approximation framework will be illustrated in in problems such as safety verificaion problems for continuous systems, approximating nonlinear systems by discrete systems, and hierarchical control design.
A distributed systems consists of an interconnection of two or more subsystems. Control of such systems is structured by two or more controllers each receiving an observation stream from a local subsystem and providing an input to the local subsystem. The control objectives mostly refer to the interaction of the subsystems in the global system. Examples of distributed control systems include: The control of autonomous underwater vehicles with the problem of coordination of the activities of the vehicles. The control of road networks with a hierarchical-distributed system for coordination of different control measures. The control of automated guided vehicles on a container terminal for safety and for efficiency. Control of large complex machines with the problem of control of the parallel operations using several actuators and sensors. Control synthesis of distributed systems will be described for the following control architectures: Distributed control often leading to a game theoretic approach. Distributed control with communication between controllers in which the emphasis is on what, when, and to whom to communicate. Coordination control with attention for the coordination aspects between subsystems. Hierarchical control of a hierarchically structured system. A research program will be described for control of distributed systems and of hierarchical systems. The lecture is based on the project Control for Coordination of Distributed Systems (C4C; sponsored by the European Commission INFSO-ICT-223844).
We consider the NP-hard problem of finding a minimum norm vector in $n$-dimensional real or complex Euclidean space, subject to $m$ concave homogeneous quadratic constraints. We show that the semidefinite programming relaxation for this nonconvex quadratically constrained quadratic program (QP) provides an $O(m^2)$ approximation in the real case, and an $O(m)$ approximation in the complex case. Moreover, we show that these bounds are tight up to a constant factor. When the Hessian of each constraint function is of rank one (namely, outer products of some given so-called {\it steering} vectors) and the phase spread of the entries of these steering vectors are bounded away from $\pi/2$, we establish a certain "constant factor" approximation (depending on the phase spread but independent of $m$ and $n$) for both the SDP relaxation and a convex QP restriction of the original NP-hard problem. When the homogeneous quadratic constraints are separable and $m=n$, we show that the SDP relaxation is actually tight. All theoretical results will be illustrated through a transmit beamforming application in wireless communication.
Recent advances in experimental techniques have made it possible to generate an enormous amount of `raw' biological data, with cancer biology being no exception. The main challenge faced by cancer biologists now is the generation of plausible hypotheses that can be evaluated against available data and/or validated through further experimentation. For persons trained in control theory, there is now a significant opportunity to work with biologists to create a virtuous cycle of hypothesis generation and experimentation. In this talk, we discuss four specific problems in cancer biology that are amenable to study using probabilistic methods. These are: reverse engineering gene regulatory networks, constructing context-specific gene regulatory networks, analyzing the significance of expression levels for collections of genes, and discriminating between drivers (mutations that cause cancer) and passengers (mutations that are caused by cancer or have no impact). Some research problems that merit the attention of the controls community are also suggested.
Recent policies combined with potential for technological innovations and business opportunities, have attracted a high level of interest in smart grids. The potential for a highly distributed system with a high penetration of intermittent sources poses opportunities and challenges. Any complex dynamic infrastructure network typically has many layers, decision-making units and is vulnerable to various types of disturbances. Effective, intelligent, distributed control is required that would enable parts of the networks to remain operational and even automatically reconfigure in the event of local failures or threats of failure. A major challenge is posed by the lack of a unified mathematical framework with robust tools for modeling, simulation, control and optimization of time-critical operations in complex multicomponent and multiscaled networks. Mathematical models of such complex systems are typically vague (or may not even exist); moreover, existing and classical methods of solution are either not available, or are not sufficiently powerful. From a strategic R&D viewpoint, how to retrofit and engineer a stable, secure, resilient grid with large numbers of such unpredictable power sources? What roles will assets optimization, increased efficiency, energy storage, advanced power electronics, power quality, electrification of transportation, novel control algorithms, communications, cyber and infrastructure security play in the grid of the future? What are the emerging technologies to enable new products, services, and markets? In this presentation, we will present an overview of smart grids and recent advances in distributed sensing, modeling, and control, particularly at both the high-voltage power grid and at consumer level. Such advances may contribute toward the development of an effective, intelligent, distributed control of power system networks with a focus on addressing distributed sensing, computation, estimation, controls and dynamical systems challenges and opportunities ahead.
We live in a "distributed world" made by countless "nodes", being them cities, computers, people, etc., connected by a dense web of transportation, communication, or social ties. The term "network", describing such a collection of nodes and links, nowadays has become of common use thanks to our extensive reliance on "connections of interdependent systems" for our everyday life, for building complex technical systems, infrastructures and so on. In an increasingly "smarter" planet, it is expected that systems are safe, reliable, available 24/7, and possibly at low-cost maintenance. In this connection, monitoring and fault diagnosis are of customary importance to ensure high levels of safety, performance, reliability, dependability, and availability. In fact, faults and malfunctions can result, just referring to industrial plants, in off-specification production, increased operating costs, chance of line shutdown, danger for humans, detrimental environmental impact, and so on. Faults and malfunctions should be detected promptly and their source and severity should be diagnosed so that corrective actions can be possibly taken. This lecture deals with a on-line approximation-based distributed fault diagnosis approach for large-scale nonlinear systems, by exploiting a "divide et impera" approach in which the overall diagnosis problem is decomposed into smaller subproblems, simpler enough to be solved within the existing computation and communication architectures. The distributed detection, isolation and identification task is broken down and assigned to a network of "Local Diagnostic Units", each having a "different/local view" on the system: they are allowed to communicate with each other and also to cooperate on the diagnosis of system components that may be shared thus yielding a global diagnosis decision. In the lecture, issues and perspectives will be addressed as well in a paradigmatic industrial context of safety-critical process control systems.
Concept abstraction is an important component of intelligence. Scientists today still do not know how the brain accomplishes it. In this talk we compare some recent mathematical results about random walks on manifolds and graphs with the features of concept abstraction processes to seek understanding of the algorithms involved.
Switched systems with positivity constraints arise in various areas. They have been fruitfully employed to model consensus problems, biological systems dynamics, and recently, viral mutation dynamics under drug treatment. The theory of "positive switched systems" is rather challenging and offers quite a number of interesting open problems. In the talk, we will illustrate the main results available as far as stability, stabilizability, and controllability issues are concerned. Some open problems will be proposed, and some applications of this general theory in the area of biological systems will be illustrated.
Designs in systems and control are traditionally carried out through deterministic algorithms consisting of a sequence of steps set by deterministic rules. This approach, however, can be generalized by the introduction of randomization: a randomized algorithm is an algorithm where one or more steps are based on a random rule, that is – among many deterministic rules – one rule is selected according to a random scheme. Randomization has turned out to be a powerful tool for solving a number of problems deemed unsolvable with deterministic methods.
A crucial fact is that randomization permits one to introduce the notion of ``probabilistically successful algorithm''. In many cases, when deterministic successfulness cannot be achieved, probabilistic successfulness offers a valid alternative.
In the talk, the use of randomized algorithms will be discussed in relation to several problems:
Hybrid systems combine continuous-time dynamics with discrete modes of operation. The states of such system usually have two distinct components: one that evolves continuously, typically according to a differential equation; and another one that only changes through instantaneous jumps.
We present a model for Stochastic Hybrid Systems (SHSs) where transitions between discrete modes are triggered by stochastic events, much like transitions between states of a continuous-time Markov chains. However, in SHSs the rate at which transitions occur depends on both the continuous and the discrete states of the hybrid system. The combination of continuous dynamics, discrete events, and stochasticity results in a modeling framework with tremendous expressive power, making SHSs appropriate to describe the dynamics of a wide variety of systems. This observation has been the driving force behind the several recent research efforts aimed at developing tools to analyze these systems.
In this talk, we use several examples to illustrate the use of SHSs as a versatile modeling tool to describe dynamical systems that arise in distributed control and estimation, networked control systems, molecular biology, and ecology. In parallel, we will also discuss several mathematical tools that can be used to analyze such systems, including the use of the extended generator, Lyapunov-based arguments, infinite-dimensional moment dynamics, and finite-dimensional truncations.
Central banks and funds investment managers work with mathematical models. In recent years, a new class of model has come into prominence—generalized dynamic factor models. These are characterized by having a modest number of inputs, corresponding to key economic variables and industry-sector-wide variables for central banks and funds managers respectively, and a large number of outputs, economic time series data or individual stock price movements for example. It is common to postulate that the input variables are linked to the output variables by a finite-dimensional linear time-invariant discrete-time dynamic model, the outputs of which are corrupted by noise to yield the measured data. The key problems faced by central banks or funds managers are model fitting given the output data (but not the input data), and then using the model for prediction purposes. These are essentially tasks usually considered by those practicing identification and time series modelling. Nevertheless there is considerable underlying linear system theory. This flows from the fact that the underlying transfer function matrix is tall. This presentation will describe a number of consequences of this seemingly trivial fact, and then go on to indicate how to cope with time series with different periodicities, e.g. monthly and quarterly, where multirate signal processing and control concepts are of relevance.
Moore's Law describes an important trend in the history of computer hardware: that the number of transistors that can be inexpensively placed on an integrated circuit is increasing exponentially, doubling approximately every two years. The self-fulfilling prophesy of Moore's Law is under threat. The new bottleneck comes not from hardware and technology capabilities, but from control, computation, and algorithmic constraints in various steps in the design flow such as verification and optical proximity correction.
In the first part of the talk, we describe our efforts in developing a new class of wireless sensors for use in semiconductor manufacturing. These sensors are fully self-contained with on board power, communications, and signal processing electronics. The sensors offer unprecedented spatial and time resolution, making them suitable for equipment diagnostics and design, and for process optimization and control. We will illustrate the applications of these sensors in IC processing, and describe our efforts at commercializing this technology.
In the second part of the talk, will outline three very large scale computational problems that are critical to next generation ICs. These are: Inverse lithography, design verification, and design-for manufacturability. We formulate these mathematically using the common language of clip calculus and show possible solutions. We will conclude with arguing for the vital importance of computation and modeling in this economically important field.
In economic networks, behaviors that seem intelligently conceived (for the most part) emerge without a coordinating entity, but as a result of multiple agents pursuing self-interested strategies. Such behaviors have traditionally been interpreted using paradigms of non-cooperative game theory. However, standard game theory usually assumes that the players know the models of their own payoff functions and the actions of the other players. Relying on the method of extremum seeking, we design algorithms that do not need any modeling information and where, instead, the players employ only the measurements of their own payoff values. The extremum seeking algorithms are proved to converge to the Nash equilibria of the underlying non-cooperative games. In other words, extremum seeking allows the player to learn its Nash strategy. Extremum seeking algorithms are not restricted to games with a limited number of players but are, in fact, applicable to games with uncountably many players. While in finite games each player employing extremum seeking must employ a distinct probing frequency, a remarkable situation arises in games with uncountably many players - we show that, as long as any frequency is employed by countably many players, convergence to the Nash equilibrium is guaranteed. Such large (for all practical purposes uncountable) games arise in future energy trading markets involving households that own plug-in hybrid electric vehicles, whose battery capacity is used for the storage of energy during periods of excess production from wind and solar sources and for selling energy back to the grid, at a price, or in quantity, determined by a controller pursuing profit maximization for the household with the help of an extremum seeking algorithm.
Extremely rare events occur in several areas of science and engineering. Some examples are huge movements in the prices of assets and commodities; and weather phenomena such as high winds and hurricanes. Attempts to model these kinds of events using the law of large numbers often fail because "rare" events seem to occur *far more frequently* than a Gaussian distribution would suggest. In some areas, such as clinical trials of drug candidates, a "law of large numbers" will not apply because the available data is far too small to permit the drawing of confident conclusions. For instance, one may have to estimate a few dozen parameters on the basis of a few dozen samples (whereas prudence would suggest at least a few thousand samples -- but these are simply not available).
In this talk we give a *very elementary introduction* to some theoretical methods for modeling and coping with extremely rare or adverse events. To handle rare events, we suggest the use of "heavy tailed" random variables, which have finite first moment but infinite variance. The form of the "law of large numbers" for such variables, and how these affect the design process, will be discussed. To model adverse events with limited data, we suggest the use of "worst case probability distributions" which can be formulated as a linear programming problem, and often leads to surprisingly good insights.
There has been remarkable progress in sampled-data control theory in the last two decades. The main achievement here is that there exists a digital (discrete-time) control law that takes the intersample behavior into account and makes the overall analog (continuous-time) performance optimal, in the sense of H-infinity norm. This naturally suggests its application to digital signal processing where the same hybrid nature of analog and digital is always prevalent. A crucial observation here is that the perfect band-limiting hypothesis, widely accepted in signal processing, is often inadequate for many practical situations. In practice, the original analog signals (sounds, images, etc.) are neither fully band-limited nor even close to be band-limited in the current processing standards. The problem is to interpolate high-frequency components beyond the so-called Nyquist frequency, and this is nothing but the intersample signals discarded through sampling. Assuming a natural signal generator model, sampled-data control theory provides an optimal platform for such problems. This new method has been implemented to a custom LSI chips by SANYO corporation, and has made success of producing over 12 million chips. This talk provides a new problem formulation, design procedure, and various applications in sound processing/compression and image processing.
Decoherence, which is caused due to the interaction of a quantum system with its environment plagues all quantum systems and leads to the loss of quantum properties that are vital for quantum computation and quantum information processing. Superficially, this problem appears to be the disturbance decoupling problem in classical control theory. In this talk first we briefly review recent advances in Quantum Control. Then we propose a novel strategy using techniques from geometric systems theory to completely eliminate decoherence and also provide conditions under which it can be done so. A novel construction employing an auxiliary system, the bait, which is instrumental to decoupling the system from the environment, is presented. This literally corresponds to the Internal Model Principle for Quantum Mechanical Systems which is quite different from the classical theory due to the quantum nature of the system. Almost all the earlier work on decoherence control employ density matrix and stochastic master equations to analyze the problem. Our approach to decoherence control involves the bilinear input affine model of quantum control system which lends itself to various techniques from classical control theory, but with non-trivial modifications to the quantum regime. This approach yields interesting results on open loop decouplability and Decoherence Free Subspaces (DFS). The results are also shown to be superior to the ones obtained via master equations. Finally, a methodology to synthesize feedback parameters itself is given, that technology permitting, could be implemented for practical 2-qubit systems performing decoherence free Quantum Computing. Open problems and future directions in quantum control also will be discussed.
