Manfred Morari

Eawag: Das Wasserforschungs-Institut des ETH-Bereichs, Duebendorf, Zurich, Switzerland

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Publications (628)733.7 Total impact

  • IEEE Transactions on Power Systems 01/2015; DOI:10.1109/TPWRS.2015.2391233 · 3.53 Impact Factor
  • IEEE Transactions on Power Systems 01/2015; DOI:10.1109/TPWRS.2015.2394354 · 3.53 Impact Factor
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    ABSTRACT: Lagrangian duality in mixed integer optimization is a useful framework for problems decomposition and for producing tight lower bounds to the optimal objective, but in contrast to the convex counterpart, it is generally unable to produce optimal solutions directly. In fact, solutions recovered from the dual may be not only suboptimal, but even infeasible. In this paper we concentrate on large scale mixed--integer programs with a specific structure that is of practical interest, as it appears in a variety of application domains such as power systems or supply chain management. We propose a solution method for these structures, in which the primal problem is modified in a certain way, guaranteeing that the solutions produced by the corresponding dual are feasible for the original unmodified primal problem. The modification is simple to implement and the method is amenable to distributed computations. We also demonstrate that the quality of the solutions recovered using our procedure improves as the problem size increases, making it particularly useful for large scale instances for which commercial solvers are inadequate. We illustrate the efficacy of our method with extensive experimentations on a problem stemming from power systems.
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    ABSTRACT: This paper provides necessary and sufficient conditions for several forms of controlled system reliability. For comparison purposes, past results on the reliability analysis of controlled systems are reviewed and several of the past results are shown to be either conservative or have exponential complexity. For systems with real and complex uncertainties, conditions for robust reliable stability and performance are formulated in terms of the structured singular values of certain transfer functions. The conditions are necessary and sufficient for the controller to stabilize the closed-loop system while retaining a desirable level of the closed-loop performance in the presence of actuator/sensor faults or failures, as well as plant-model mismatches. The resulting conditions based on the structured singular value are applied to the decentralized control for a high-purity distillation column and singular value decomposition-based optimal control for a parallel reactor with combined precooling. Tight polynomial-time bounds for the conditions can be evaluated by using available off-the-shelf software.
    Computers & Chemical Engineering 11/2014; 70:67–77. DOI:10.1016/j.compchemeng.2014.07.023 · 2.45 Impact Factor
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    ABSTRACT: The problem of experiment design for constrained linear systems with multiple inputs is addressed. A parametric model of the system is considered. The presented theoretical results provide a guideline on how to design experiments that minimize the worst-case identification error, as measured by the radius of information of the set of feasible model parameters, calculated in any norm. In addition, it is shown that an alternative, simpler approach can be employed when input constraints are symmetric and the worst-case identification error is minimized in either - or -norm. For such cases, on the basis of the derived results, a computationally tractable algorithm for the experiment design is proposed. The presented results are valid for a general model representation, which admits the commonly used finite impulse response model as a special case. The features of the presented method are illustrated in a numerical example.
    Automatica 11/2014; DOI:10.1016/j.automatica.2014.10.058 · 3.13 Impact Factor
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    Georg Schildbach, Paul Goulart, Manfred Morari
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    ABSTRACT: This paper is concerned with the design of a linear control law for a linear system with stationary additive disturbances. The objective is to find a state feedback gain that minimizes a quadratic stage cost function, while observing chance constraints on the input and/or the state. Unlike most of the previous literature, the chance constraints (and the stage cost) are not considered on each input/state of the transient response. Instead, they refer to the input/state of the closed-loop system in its stationary mode of operation. Hence the control is optimized for the long-run, rather than for finite-horizon operation. The controller synthesis problem can be cast as a convex semi-definite program (SDP). The chance constraints appear as linear matrix inequalities. Both single chance constraints (SCCs) and joint chance constraints (JCCs) on the input and/or the state can be included. If the disturbance is Gaussian, this information can be used to improve the controller design. The presented approach can also be extended to the case of output feedback. The entire design procedure is flexible and easy to implement, as demonstrated on a short illustrative example.
    Automatica 11/2014; 51. DOI:10.1016/j.automatica.2014.10.096 · 3.13 Impact Factor
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    ABSTRACT: An adaptive control algorithm for open-loop stable, constrained, linear, multiple input multiple output systems is presented. The proposed approach can deal with both input and output constraints, as well as measurement noise and output disturbances. The adaptive controller consists of an iterative set membership identification algorithm, that provides a set of candidate plant models at each time step, and a model predictive controller, that enforces input and output constraints for all the plants inside the model set. The algorithm relies only on the solution of standard convex optimization problems that are guaranteed to be recursively feasible. The experimental results obtained by applying the proposed controller to a quad-tank testbed are presented.
    Automatica 11/2014; DOI:10.1016/j.automatica.2014.10.036 · 3.13 Impact Factor
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    ABSTRACT: Neuromodulation of spinal sensorimotor circuits improves motor control in animal models and humans with spinal cord injury. With common neuromodulation devices, electrical stimulation parameters are tuned manually and remain constant during movement. We developed a mechanistic framework to optimize neuromodulation in real time to achieve high-fidelity control of leg kinematics during locomotion in rats. We first uncovered relationships between neuromodulation parameters and recruitment of distinct sensorimotor circuits, resulting in predictive adjustments of leg kinematics. Second, we established a technological platform with embedded control policies that integrated robust movement feedback and feed-forward control loops in real time. These developments allowed us to conceive a neuroprosthetic system that controlled a broad range of foot trajectories during continuous locomotion in paralyzed rats. Animals with complete spinal cord injury performed more than 1000 successive steps without failure, and were able to climb staircases of various heights and lengths with precision and fluidity. Beyond therapeutic potential, these findings provide a conceptual and technical framework to personalize neuromodulation treatments for other neurological disorders.
    Science translational medicine 09/2014; 6(255):255ra133. DOI:10.1126/scitranslmed.3008325 · 14.41 Impact Factor
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    ABSTRACT: Airborne wind energy systems aim to harvest the power of winds blowing at altitudes higher than what conventional wind turbines reach. They employ a tethered flying structure, usually a wing, and exploit the aerodynamic lift to produce electrical power. In the case of ground-based systems, where the traction force on the tether is used to drive a generator on the ground, a two phase power cycle is carried out: one phase to produce power, where the tether is reeled out under high traction force, and a second phase where the tether is recoiled under minimal load. The problem of controlling a tethered wing in this second phase, the retraction phase, is addressed here, by proposing two possible control strategies. Theoretical analyses, numerical simulations, and experimental results are presented to show the performance of the two approaches. Finally, the experimental results of complete autonomous power generation cycles are reported and compared with first-principle models.
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    ABSTRACT: In this paper, a novel hierarchical multirate control scheme for nonlinear discrete-time systems is presented, consisting of a robust nonlinear model predictive controller (NMPC) and a multirate sliding mode disturbance compensator (MSMDC). The proposed MSMDC acts at a faster rate than the NMPC in order to keep the system as close as possible to the nominal trajectory predicted by NMPC despite model uncertainties and external disturbances. The a priori disturbance compensation turns out to be very useful in order to improve the robustness of the NMPC controller. A dynamic input allocation between MSMDC and NMPC allows to maximize the benefits of the proposed scheme that unites the advantages of sliding mode control (strong reduction of matched disturbances, low computational burden) to those of NMPC (optimality, constraints handling). Sufficient conditions required to guarantee input-to-state stability and constraints satisfaction by the overall scheme are also provided. Copyright © 2014 John Wiley & Sons, Ltd.
    International Journal of Robust and Nonlinear Control 09/2014; DOI:10.1002/rnc.3244 · 1.90 Impact Factor
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    ABSTRACT: This paper focuses on cooperative distributed model predictive control (MPC) of wind farms, where the farms respond to active power control commands issued by the transmission system operator. A distributed MPC scheme is proposed, which aims at satisfying the requirements imposed by the grid code while minimizing the farm-wide mechanical structure fatigue. The distributed MPC control law is defined by a global finite-horizon optimal control problem, which is solved at every time step by distributed optimization. The computational approach is completely distributed, that is, every turbine evaluates its own globally optimal input by considering local measurements and communicating to neighboring turbines only. Two MPC versions are compared, in the first of which the farm-wide power output constraint is implemented as a hard constraint, whereas in the second, it is implemented as a soft constraint. As for distributed optimization methods, the alternating direction method of multipliers as well as a dual decomposition scheme based on fast gradient updates are compared. The performance of the proposed distributed MPC controller, as well as the performance of the distributed optimization methods used for its operation, are compared in the simulation on four exemplary scenarios. The results of the simulations imply that the use of cooperative distributed MPC in wind farms is viable both from a performance and from a computational viewpoint. Copyright © 2014 John Wiley & Sons, Ltd.
    Optimal Control Applications and Methods 08/2014; DOI:10.1002/oca.2136 · 1.06 Impact Factor
  • M. Herceg, C. N. Jones, M. Morari
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    ABSTRACT: The paper presents a review of active set (AS) algorithms that have been deployed for implementation of fast model predictive control (MPC). The main purpose of the survey is to identify the dominant features of the algorithms that contribute to fast execution of online MPC and to study their influence on the speed. The simulation study is conducted on two benchmark examples where the algorithms are analyzed in the number of iterations and in the workload per iteration. The obtained results suggest directions for potential improvement in the speed of existing AS algorithms. Copyright © 2014 John Wiley & Sons, Ltd.
    Optimal Control Applications and Methods 08/2014; DOI:10.1002/oca.2140 · 1.06 Impact Factor
  • Damian Frick, Alexander Domahidi, Manfred Morari
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    ABSTRACT: Predictive control of hybrid systems is currently considered prohibitive using embedded computing platforms. To overcome this limitation for mixed logical dynamical systems of small to medium size, we propose to use 1) a standard branch-and-bound approach combined with a fast embedded interior point solver, 2) pre-processing heuristics, run once and offline, to significantly reduce the number of subproblems to be solved, and 3) relaxations of the original MPC problem that allow a trade off between computation time and closed-loop performance. A problem-specific ANSI C implementation of the proposed method can be automatically generated, and has a fixed memory footprint and a code size that is insignificantly larger than that of the subproblem solver. Two extensive numerical studies are presented, where problems with up to 60 binary variables are solved in less than 0.2 seconds with a performance deterioration of below 2 % when compared to an optimal MPC scheme.
    Computers & Chemical Engineering 07/2014; DOI:10.1016/j.compchemeng.2014.06.005 · 2.45 Impact Factor
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    ABSTRACT: This paper describes autonomous racing of RC race cars based on mathematical optimization. Using a dynamical model of the vehicle, control inputs are computed by receding horizon based controllers, where the objective is to maximize progress on the track subject to the requirement of staying on the track and avoiding opponents. Two different control formulations are presented. The first controller employs a two-level structure, consisting of a path planner and a nonlinear model predictive controller (NMPC) for tracking. The second controller combines both tasks in one nonlinear optimization problem (NLP) following the ideas of contouring control. Linear time varying models obtained by linearization are used to build local approximations of the control NLPs in the form of convex quadratic programs (QPs) at each sampling time. The resulting QPs have a typical MPC structure and can be solved in the range of milliseconds by recent structure exploiting solvers, which is key to the real-time feasibility of the overall control scheme. Obstacle avoidance is incorporated by means of a high-level corridor planner based on dynamic programming, which generates convex constraints for the controllers according to the current position of opponents and the track layout. The control performance is investigated experimentally using 1:43 scale RC race cars, driven at speeds of more than 3 m/s and in operating regions with saturated rear tire forces (drifting). The algorithms run at 50 Hz sampling rate on embedded computing platforms, demonstrating the real-time feasibility and high performance of optimization-based approaches for autonomous racing. Copyright © 2014 John Wiley & Sons, Ltd.
    Optimal Control Applications and Methods 07/2014; DOI:10.1002/oca.2123 · 1.06 Impact Factor
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    ABSTRACT: A recently proposed adaptive model predictive control algorithm is implemented in real time and its performance is verified experimentally on a quad-tank testbed. The algorithm relies on an iterative set membership identification procedure in order to provide a set of possible plant models at each time step. This set is then exploited by the model predictive controller in order to robustly enforce output constraints. The experimental results show that the algorithm provides good reference tracking performance while robustly satisfying output constraints for different operating conditions.
    2014 European Control Conference (ECC); 06/2014
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    ABSTRACT: While rule based control (RBC) is current practice in most building automation systems that issue discrete control signals, recent simulation studies suggest that advanced, optimization based control methods such as hybrid model predictive control (HMPC) can potentially outperform RBC in terms of energy efficiency and occupancy comfort. However, HMPC requires a more complex IT infrastructure and numerical optimization in the loop, which makes commissioning, operation of the building, and error handling significantly more involved than in the rule based setting. In this paper, we suggest an automated RBC synthesis procedure for binary decisions that extracts prevalent information from simulation data with HMPC controllers. The result is a set of simple decision rules that preserves much of the control performance of HMPC. The methods are based on standard machine learning algorithms, in particular support vector machines (SVMs) and adaptive boosting (AdaBoost). We consider also the ranking and selection of measurements which are used for a decision and show that this feature selection is useful in both complexity reduction and reduction of investment costs by pruning unnecessary sensors. The suggested methods are evaluated in simulation for six different case studies and shown to maintain the performance of HMPC despite a tremendous reduction in complexity.
    Journal of Process Control 06/2014; 24(6). DOI:10.1109/CDC.2012.6426767 · 2.18 Impact Factor
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    ABSTRACT: Model predictive control (MPC) is a promising alternative in building control with the potential to improve energy efficiency and comfort and to enable demand response capabilities. Creating an accurate building model that is simple enough to allow the resulting MPC problem to be tractable is a challenging but crucial task in the control development. In this paper we introduce the Building Resistance-Capacitance Modeling (BRCM) Matlab Toolbox that facilitates the physical modeling of buildings for MPC. The Toolbox provides a means for the fast generation of (bi-)linear resistance-capacitance type models from basic building geometry, construction and systems data. Moreover, it supports the generation of the corresponding potentially time-varying costs and constraints. The Toolbox is based on previously validated modeling principles. In a case study a BRCM model was automatically generated from an EnergyPlus input data file and its predictive capabilities were compared to the EnergyPlus model. The Toolbox itself, the details of the modeling and the documentation can be found at www.brcm.ethz.ch.
    2014 American Control Conference - ACC 2014; 06/2014
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    ABSTRACT: We describe a new approach to robust unit commitment for an electricity network, which couples the switching decisions to a set of time-coupled redispatch rules in order to minimize the expected cost of operation over a planning horizon. We assume bounded uncertainties arising from imperfect predictions of loads and intermittent renewable infeeds. We refer to the time-coupled redispatch rules as affine reserve policies, and they apply not only to generators but to other continuously-controllable devices such as energy storage units or demand response installations, which are modelled as generic mixed logical-dynamical systems. We use a lumped-parameter example to demonstrate that unit commitment decisions coupled with affine reserve policies can reduce the number of time periods in which expensive peaking plants need to be employed, and that the effect is present for a wide range of parameter values. An important benefit is that the approach allows the uncertainty of future energy storage levels to be managed more tightly than existing formulations would allow.
    American Control Conference (submitted); 06/2014
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    Claudio Ruch, Joseph Warrington, Manfred Morari
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    ABSTRACT: The recent increase in popularity of shared mobility systems, in which users take a bicycle or car from a geographically-dispersed public pool in order to complete part of a journey, is due in part to improved technologies for tracking and billing customer journeys. In many schemes, a customer can start and end a journey at different docking stations and is billed according to a set fee structure. However, a given system generally becomes imbalanced due to asymmetry of demand for such “one-way” services across the system and throughout the day, and the resulting cost of employing staff to redistribute the system's vehicles is significant. This paper describes how dynamic customer prices, varying geographically as a function of the current and expected future state of the system, could be used as control signals to improve service rates. Such signals could be communicated to customers using existing ICT infrastructure. We show, using an agent-based model parameterized with historical data from London's Barclays Cycle Hire scheme, that simple proportional price control rules can improve service rates without the need to resort to conventional bike redistribution staff. In addition we analyze the performance obtained and discuss system design issues.
    European Control Conference (submitted); 06/2014
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    ABSTRACT: This paper considers model predictive control (MPC) of switched mode power converters and uses a hybrid prediction model that represents the switching ripple of the converter state. The goal of the approach is to achieve both high dynamic performance and low harmonic distortion of the output at steady state. It is argued that the previously published approaches to hybrid MPC of power converters are sensitive to plant/model mismatch, and an alternative problem formulation is suggested. The new formulation describes the converter state in terms of dynamic Fourier coefficients. This allows to quantify and feed back more information about the switched converter waveform, and can thus reduce the dependence on the system model. The suggested approach is evaluated in both simulation and experiment, and it is shown to be less sensitive to plant/model mismatch than approaches from the literature. Index Terms— Dynamic phasor, LCL filter, model predictive control (MPC), optimal pulsewidth modulation (PWM), switched mode power converter, voltage source inverter.
    Control Systems Technology, IEEE Transactions on 05/2014; 1(1). DOI:10.1109/TCST.2014.2317775 · 1.86 Impact Factor

Publication Stats

23k Citations
733.70 Total Impact Points

Institutions

  • 1996–2014
    • Eawag: Das Wasserforschungs-Institut des ETH-Bereichs
      Duebendorf, Zurich, Switzerland
    • Purdue University
      • School of Chemical Engineering
      West Lafayette, IN, United States
  • 1970–2012
    • ETH Zurich
      • Automatic Control Laboratory
      Zürich, Zurich, Switzerland
  • 2010
    • Norwegian University of Science and Technology
      • Department of Chemical Engineering (IKP)
      Trondheim, Sor-Trondelag Fylke, Norway
  • 2009–2010
    • University of Auckland
      • Department of Electrical & Computer Engineering
      Auckland, Auckland, New Zealand
  • 2007–2009
    • Technische Universiteit Delft
      • Delft Center for Systems and Control (DCSC)
      Delft, South Holland, Netherlands
    • University of Newcastle
      • School of Electrical Engineering and Computer Science
      Newcastle, New South Wales, Australia
  • 2008
    • University of Minnesota Duluth
      Duluth, Minnesota, United States
    • GE Global Research
      Niskayuna, New York, United States
  • 2007–2008
    • Università degli Studi del Sannio
      • Department of Energy Engineering
      Benevento, Campania, Italy
  • 2004
    • University of Toronto
      Toronto, Ontario, Canada
    • Imperial College London
      • Department of Electrical and Electronic Engineering
      London, ENG, United Kingdom
    • University of Zurich
      • Artificial Intelligence Laboratory
      Zürich, Zurich, Switzerland
  • 2000–2003
    • Università degli Studi di Siena
      Siena, Tuscany, Italy
    • University of Cambridge
      • Department of Engineering
      Cambridge, England, United Kingdom
  • 1994–2002
    • Pasadena City College
      Pasadena, Texas, United States
  • 1970–2002
    • California Institute of Technology
      • Division of Chemistry and Chemical Engineering
      Pasadena, California, United States
  • 2001
    • Tokyo Metropolitan Institute
      Edo, Tōkyō, Japan
    • University of Zagreb
      • Faculty of Electrical Engineering and Computing (FER)
      Zagrabia, Grad Zagreb, Croatia
  • 1998
    • Lehigh University
      • Department of Chemical Engineering
      Bethlehem, PA, United States
  • 1991–1997
    • Auburn University
      • Department of Chemical Engineering
      Auburn, AL, United States
  • 1987–1988
    • University of Maryland, College Park
      CGS, Maryland, United States
    • University of Washington Seattle
      • Department of Chemical Engineering
      Seattle, Washington, United States
  • 1986
    • INTEC
      United States
  • 1980–1985
    • University of Wisconsin, Madison
      • Department of Chemical and Biological Engineering
      Madison, MS, United States