Manfred Morari

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

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Publications (659)884.24 Total impact

  • [Show abstract] [Hide abstract]
    ABSTRACT: We present a computationally-efficient approach for solving stochastic, multiperiod optimal power flow problems. The objective is to determine power schedules for controllable devices in a power network, such as generators, storage, and curtailable loads, which minimize expected short-term operating costs under various device and network constraints. These schedules are chosen in a multistage decision framework to include planned power output adjustments, or reserve policies, which track errors in the forecast of power requirements as they are revealed, and which may be time-coupled. Such an approach has previously been shown to be an attractive means of accommodating uncertainty arising from highly variable renewable energy sources. Given a probabilistic forecast describing the spatio-temporal variations and dependencies of forecast errors, we formulate a family of stochastic network and device constraints based on convex approximations of chance constraints, and show that these allow economic efficiency and system security to be traded off with varying levels of conservativeness. Our formulation indicates two broad approaches, based on conditional value and risk and distributional robustness, that provide alternatives to existing methods based on chance and robust constraints. The results are illustrated using a case study, in which conventional generators plan schedules around an uncertain but time-correlated wind power injection.
    International Journal of Electrical Power & Energy Systems 11/2015; 72. DOI:10.1016/j.ijepes.2015.02.024 · 3.43 Impact Factor
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    ABSTRACT: This paper presents a study on the design of linear model predictive control (MPC) for wind turbines, with a focus on the controller's tuning tradeoffs. A continuously linearized MPC approach is described and applied to control a 3-bladed, horizontal axis, variable speed wind turbine. The tuning involves a multiobjective cost function so that the performance can be optimized with respect to five defined measures: power variation, pitch usage, tower displacement, drivetrain twist and frequency of violating the nominal power limit. A tuning approach based on the computation of sensitivity tables is proposed and tested via numerical simulations using a nonlinear turbine model. The paper further compares the performance of the MPC controller with that of a conventional one.
    Renewable Energy 08/2015; 80:664-673. DOI:10.1016/j.renene.2015.02.057 · 3.36 Impact Factor
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    ABSTRACT: This manuscript contains technical details of recent results developed by the authors on the algorithm for direct design of controllers for nonlinear systems from data that has the ability to to automatically modify some of the tuning parameters in order to increase control performance over time.
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    ABSTRACT: A technique for the detection and measurement of the agglomeration of needle-like particles is presented. A novel image analysis routine, based on a supervised machine learning strategy, is used to identify agglomerates that are subsequently characterized by their volume. Through repeated measurement of a large number of agglomerates, a 1D particle size distribution of agglomerates is reconstructed. Concurrently, established tools are used to characterize needle-like primary crystals, whose shape is described by cylinders and whose population can be described by a separate two-dimensional particle size and shape distribution. The performance of the classifier is evaluated, and the reproducibility of the measurement is demonstrated for the case of β l-glutamic acid. For the same system, the agglomeration behavior is studied for varying operating conditions, and general trends are analyzed.
    Crystal Growth & Design 04/2015; 15(4):1923-1933. DOI:10.1021/acs.cgd.5b00094 · 4.56 Impact Factor
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    ABSTRACT: The growth rate dispersion of needle-like β L-glutamic acid in the length direction is measured using a stagnant solution hot stage microscopy setup. Possible causes of the observed dispersion are analyzed and the resulting distribution of growth rates is used to motivate and reconstruct a distribution of an internal, growth affecting property of the crystals. The latter is then used as the initial condition for a multidimensional, morphological population balance model, whose outputs are fitted to 2D particle size distribution measurements obtained from seeded batch desupersaturation experiments. It is shown, through analysis of both types of data, that a non-zero rate of change in the direction of the new coordinate is required and a phenomenological description of this rate is proposed. The resulting model is able to quantitatively describe experimental data obtained from independent measurement devices, operating at different scales simultaneously.
    Chemical Engineering Science 02/2015; 133. DOI:10.1016/j.ces.2015.02.026 · 2.61 Impact Factor
  • IEEE Transactions on Power Systems 01/2015; DOI:10.1109/TPWRS.2015.2394354 · 3.53 Impact Factor
  • IEEE Transactions on Power Systems 01/2015; DOI:10.1109/TPWRS.2015.2391233 · 3.53 Impact Factor
  • IEEE Transactions on Control Systems Technology 01/2015; DOI:10.1109/TCST.2015.2415411 · 2.52 Impact Factor
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    ABSTRACT: We investigate the control of constrained stochastic linear systems when faced with limited information regarding the disturbance process, i.e. when only the first two moments of the disturbance distribution are known. We consider two types of distributionally robust constraints. In the first case, we require that the constraints hold with a given probability for all disturbance distributions sharing the known moments. These constraints are commonly referred to as distributionally robust chance constraints. In the second case, we impose conditional value-at-risk (CVaR) constraints to bound the expected constraint violation for all disturbance distributions consistent with the given moment information. Such constraints are referred to as distributionally robust CVaR constraints with second-order moment specifications. We propose a method for designing linear controllers for systems with such constraints that is both computationally tractable and practically meaningful for both finite and infinite horizon problems. We prove in the infinite horizon case that our design procedure produces the globally optimal linear output feedback controller for distributionally robust CVaR and chance constrained problems. The proposed methods are illustrated for a wind blade control design case study for which distributionally robust constraints constitute sensible design objectives.
    IEEE Transactions on Automatic Control 01/2015; DOI:10.1109/TAC.2015.2444134 · 3.17 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 · 2.65 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 · 1.54 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; 36(3). DOI:10.1002/oca.2136 · 1.54 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; 72. DOI:10.1016/j.compchemeng.2014.06.005 · 2.45 Impact Factor

Publication Stats

25k Citations
884.24 Total Impact Points


  • 1996–2014
    • Eawag: Das Wasserforschungs-Institut des ETH-Bereichs
      Duebendorf, Zurich, Switzerland
    • Purdue University
      • School of Chemical Engineering
      West Lafayette, IN, United States
  • 1970–2014
    • 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
    • GE Global Research
      Niskayuna, New York, United States
  • 2007–2008
    • Università degli Studi del Sannio
      • Department of Energy Engineering
      Benevento, Campania, Italy
  • 1980–2008
    • University of Minnesota Duluth
      Duluth, Minnesota, United States
  • 2004
    • Hochschule für Technik Zürich
      Zürich, Zurich, Switzerland
    • University of Toronto
      Toronto, Ontario, Canada
    • Imperial College London
      • Department of Electrical and Electronic Engineering
      London, ENG, United Kingdom
  • 2000–2003
    • Università degli Studi di Siena
      Siena, Tuscany, Italy
    • University of Cambridge
      • Department of Engineering
      Cambridge, England, United Kingdom
  • 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
  • 1970–1997
    • California Institute of Technology
      • Division of Chemistry and Chemical Engineering
      Pasadena, CA, United States
  • 1986–1994
    • Pasadena City College
      Pasadena, Texas, United States
    • INTEC
      Santa Fe, New Mexico, United States
  • 1987–1988
    • University of Maryland, College Park
      CGS, Maryland, United States
  • 1978–1988
    • University of Wisconsin–Madison
      • Department of Chemical and Biological Engineering
      Madison, Wisconsin, United States