Manfred Morari

ETH Zurich, Zürich, Zurich, Switzerland

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Publications (714)936.92 Total impact

  • Source
    T. Geyer · G. Papafotiou · M. Morari

    Full-text · Dataset · Jan 2016
  • Source
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    ABSTRACT: An approach to design a feedback controller for nonlinear systems directly from experimental data is presented. Improving over a recently proposed technique, which employs exclusively a batch of experimental data collected in a preliminary experiment, here the control law is updated and refined during real-time operation, hence enabling an on-line learning capability. The theoretical properties of the described approach, in particular closed-loop stability and tracking accuracy, are discussed. Finally, the experimental results obtained with a water tank laboratory setup are presented.
    Preview · Article · Dec 2015
  • Martin Herceg · Colin N. Jones · Michal Kvasnica · Manfred Morari
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    ABSTRACT: This paper presents a solution method for parametric linear complementarity problems (PLCP) that relies on an enumeration technique to discover all feasible bases. The enumeration procedure is based on evaluating all possible combinations of active constraints and testing for feasibility. Although the enumeration approach is known to grow exponentially in the number of constraints, the formulation of the PLCP allows incorporation of cheap rank tests to quickly prune the infeasible directions in the exploration. The motivation for the development of the enumeration based PLCP solver is that it represents a direct method to solve parametric linear and quadratic optimization problems as well as their mixed-integer counterparts. These types of problems often arise in the field of model predictive control for linear and hybrid systems. The enumeration based PLCP solver offers another alternative to compute explicit solutions in the field of hybrid model predictive control that can be extremely effective in some important cases.
    No preview · Article · Dec 2015
  • Source
    Tyler Summers · Joseph Warrington · Manfred Morari · John Lygeros
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    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.
    Full-text · Article · Nov 2015 · International Journal of Electrical Power & Energy Systems
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    ABSTRACT: A population balance model for the agglomeration of nonspherical particles in a well-mixed batch reactor is presented. In the model, two separate distributions are used: one for primary particles, which are described by multiple characteristic sizes, and one for agglomerates, which are characterized only by their volume. The two coupled population balance equations describing the evolution of both particle populations over time are solved in parallel together with the material balance. The output of the model for varying operating conditions and using different agglomeration kernels - including two simple, shape-sensitive functions - is assessed and finally compared to experimental results for needle-like crystals reported in the first part of this series. It is found that the qualitative trends for three important characteristics, the average needle length, the average width, and the total agglomeration degree, are well-described by all kernels. However, the average aspect ratio of primary particles, as well as the particle size and shape distribution of primary particles in general, are described better with the shape-sensitive kernels. (Figure Presented).
    No preview · Article · Aug 2015 · Crystal Growth & Design
  • Source
    Achin Jain · Georg Schildbach · Lorenzo Fagiano · Manfred Morari
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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.
    Full-text · Article · Aug 2015 · Renewable Energy
  • G. Schildbach · M. Morari
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    ABSTRACT: This paper presents a practicable Scenario-Based Model Predictive Control (Scenario MPC) approach for linear, time-varying systems with additive disturbances. Robust MPC propagates uncertainty through the dynamics based on uncertainty sets and Stochastic MPC by multi-variable convolutions of probability distributions. The idea of Scenario MPC is to propagate the uncertainty by using sampled uncertainty scenarios. This approach is computationally efficient and (implicitly) accounts for the probabilistic distribution of disturbances. Moreover, there exists a mathematical connection between the violation rate of individual chance constraints over time and the required sample size. The theory builds on very recent results in scenario-based optimization for multi-stage stochastic decision problems. In many applications, Scenario MPC requires only a very small sample size (generally a few tens of samples). This fact is demonstrated for a large-scale example of supply chain management, for which Scenario MPC yields a good control performance.
    No preview · Article · Jul 2015 · Proceedings of the American Control Conference
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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.
    No preview · Article · Jun 2015
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    ABSTRACT: This paper reports the final results of the predictive building control project OptiControl-II that encompassed seven months of model predictive control (MPC) of a fully occupied Swiss office building. First, this paper provides a comprehensive literature review of experimental building MPC studies. Second, we describe the chosen control setup and modeling, the main experimental results, as well as simulation-based comparisons of MPC to industry-standard control using the EnergyPlus simulation software. Third, the costs and benefits of building MPC for cases similar to the investigated building are analyzed. In the experiments, MPC controlled the building reliably and achieved a good comfort level. The simulations suggested a significantly improved control performance in terms of energy and comfort compared with the previously installed industry-standard control strategy. However, for similar buildings and with the tools currently available, the required initial investment is likely too high to justify the deployment in everyday building projects on the basis of operating cost savings alone. Nevertheless, development investments in an MPC building automation framework and a tool for modeling building thermal dynamics together with the increasing importance of demand response and rising energy prices may push the technology into the net benefit range.
    No preview · Article · Apr 2015 · IEEE Transactions on Control Systems Technology
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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.
    No preview · Article · Apr 2015 · Crystal Growth & Design
  • S. Almér · S. Mariéthoz · M. Morari · U. Jonsson
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    ABSTRACT: Recent tools for control and analysis of hybrid systems are applied to an AC-DC converter. The topology poses particularly challenging problems since it is unusually complex and the circuit parameters are such that the dynamic coupling between the AC and DC sides cannot be ignored. The paper proposes a model predictive control scheme for direct voltage control which circumvents the bandwidth limitations associated with classical cascade control. The stability and harmonic properties of the resulting closed loop system are investigated using new tools for the analysis of switched systems.
    No preview · Article · Mar 2015
  • A.G. Beccuti · G. Papafotiou · M. Morari
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    ABSTRACT: This paper extends the recently introduced approach in optimal control methods for fixed frequency switch-mode dc-dc converters to the parallel interleaved synchronous step-down circuit topology with N branches. Due to the prohibitive complexity of modelling and controlling such potentially large systems a distributed model predictive control scheme is employed allowing for the related optimal control problem to be efficiently formulated and solved off-line for each local controller. Simulation results are provided to illustrate the outcome of the proposed approach.
    No preview · Article · Mar 2015
  • R. Vujanic · P.M. Esfahani · P. Goulart · M. Morari
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    ABSTRACT: The increased presence of Electric Vehicles (EVs) within electricity distribution systems introduces new challenges to their reliability, since uncoordinated charging of large numbers of EV can result in overload of distribution lines or transformers. In order to manage this difficulty, entities called EV aggregators are introduced whose task is to schedule charging of the EV fleet while ensuring that network constraints are respected. In this paper we propose a solution method for the type of constrained optimization problems such aggregators must solve. Our method is simple to implement and is guaranteed to produce good and feasible solutions, while performing only lightweight centralized computations which do not require the use of additional - and often expensive - constrained optimization solvers. We show that the quality of solutions produced by our method improves as the number of EVs to be controlled is increased. In addition, the computation times remain very short even for large problem instances entailing several thousands EVs.
    No preview · Article · Feb 2015 · Proceedings of the IEEE Conference on Decision and Control
  • Aldo U. Zgraggen · Lorenzo Fagiano · Manfred Morari
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    ABSTRACT: The retraction phase of a ground-based airborne wind energy system is the second part of a two phase power generation cycle. In the first phase, energy is produced by exploiting the aerodynamic lift exerted by a wing tethered to the ground and controlled to fly crosswind paths. Power is produced by unreeling the tether, wound around drums connected to generators, under high traction force. In the retraction phase, the tether is reeled-in after its maximum length has been reached. In this paper, a new dynamical model of a tethered wing for the retraction phase is derived from first principles, and a flight controller based on this model, which is straightforward to implement and tune, is proposed. Simulation results comparing this new strategy to an existing approach are presented. Besides a better performance, the main advantage of the new approach is that it uses readily available measurements for feedback control, hence resulting in a more robust and reliable solution.
    No preview · Article · Feb 2015 · Proceedings of the IEEE Conference on Decision and Control
  • Marko Tanaskovic · Lorenzo Fagiano · Manfred Morari
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    ABSTRACT: The problem of optimal worst-case experiment design for constrained linear systems with multiple inputs represented by a parametric model is addressed. A theoretical result is derived, which provides an insight on how to design experiments that minimize the worst-case identification error in ∞- and 1-norm when the input constraints are symmetric. The presented result is valid for a general model parametrization that admits the commonly used finite impulse response model as a special case. Based on this result a computationally tractable algorithm for the worst-case experiment design is proposed. Its advantages over a more standard experiment design approach are illustrated in a numerical example.
    No preview · Article · Feb 2015 · Proceedings of the IEEE Conference on Decision and Control
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    ABSTRACT: It is known that in some cases, switching some transmission lines of an electric power system off may improve the optimal economic dispatch cost. This modification of the economic dispatch problem is known as optimal transmission line switching. Unfortunately, the modified problem involves binary decision variables which make the problem difficult to solve for large-scale power systems. This paper presents a method that scales well for large power systems, based on a decomposition approach known as the Alternating Direction Method of Multipliers (ADMM). The problem is broken into a convex component and a series of binary rounding operations, coupled via a penalty function. The output of the ADMM algorithm is post-processed in order to obtain a near-optimal solution to the original problem at relatively low computational cost. We measure the ADMM solution against a convex relaxation of the original problem, thereby certifying its quality without needing to solve the original combinatorial problem. The method is illustrated using the Polish 2383-bus test system.
    No preview · Article · Feb 2015
  • T. Summers · J. Warrington · M. Morari · J. Lygeros
    [Show abstract] [Hide abstract]
    ABSTRACT: This paper presents 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 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 relaxations of chance constraints, and show that these allow economic efficiency and system security to be traded off with varying levels of conservativeness. The results are illustrated using a simple case study, in which conventional generators plan schedules around an uncertain but time-correlated wind power injection.
    No preview · Article · Feb 2015
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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.
    No preview · Article · Feb 2015 · Chemical Engineering Science
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    ABSTRACT: We present a combined look-ahead dispatch and reserve optimization formulation, which extends our recent work on time-coupled reserve policies and employs the recent notion of generalized decision rules from the robust optimization literature. This aims to improve the performance of traditional linear decision rules when applied to short-term electrical reserve operation. We derive a primal problem whose solution is a time-coupled rule for up-and down-regulating the power injections of each controllable device, such as a generator or energy storage unit, in response to discovered values of prediction errors. This rule depends on the "bin" into which measured prediction errors fall, so that, for example, up-regulation follows a different rule to down-regulation. We also derive an associated dual problem, whose solution provides a lower-bound on the best possible primal cost. This allows the suboptimality of a candidate solution based on a particular decision rule parameterization to be bounded. The primal and dual solutions are also compared to the so-called prescient case, in which the values of the uncertainty are known in advance. We demonstrate the method using numerical case studies, including the standard IEEE-118 bus network, in which a minimum possible reserve cost is identified using the dual lower-bounding approach.
    No preview · Article · Jan 2015 · IEEE Transactions on Power Systems
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    ABSTRACT: We present a rolling decision-making process for electrical power systems, in which unit commitment, dispatch and reserve policies are co-optimized in order to minimize expected short-run operating costs in the presence of uncertainty arising from demand and renewable infeeds. The uncertainty is assumed to be bounded, with estimated first and second moment statistics available. The rolling "look-ahead" process employs a planning horizon of several hours, with re-optimization taking place each time the first step has been implemented. We present an expected-cost formulation incorporating multi-stage recourse on continuous decision variables-plans for adjusting the dispatch in the light of future information to be discovered at each stage of the optimization horizon. The generic formulation allows the flexibility of devices such as energy storage units to be exploited in the reserve mechanism. We demonstrate using closed-loop numerical tests that significant reductions in the cost of accommodating uncertainty are attainable relative to a time-decoupled reserve mechanism. In contrast to previous results, we show that a time-coupled cost function is not required for this benefit to be observed. In addition, we show that relaxing binary unit commitment decisions after the first step of the horizon brings significant computational speed-ups, and in some cases also reduce closed-loop system operation costs.
    No preview · Article · Jan 2015 · IEEE Transactions on Power Systems

Publication Stats

30k Citations
936.92 Total Impact Points

Institutions

  • 1970-2015
    • ETH Zurich
      • Automatic Control Laboratory
      Zürich, Zurich, Switzerland
  • 1996-2014
    • Eawag: Das Wasserforschungs-Institut des ETH-Bereichs
      Duebendorf, Zurich, Switzerland
  • 2013
    • University of Alberta
      Edmonton, Alberta, Canada
  • 2007-2009
    • Technische Universiteit Delft
      • Delft Center for Systems and Control (DCSC)
      Delft, South Holland, Netherlands
    • Università degli Studi del Sannio
      Benevento, Campania, Italy
  • 2004
    • University of Toronto
      • Institute of Biomaterials and Biomedical Engineering
      Toronto, Ontario, Canada
  • 2002-2004
    • Hochschule für Technik Zürich
      Zürich, Zurich, Switzerland
  • 2000-2002
    • Università degli Studi di Siena
      Siena, Tuscany, Italy
    • University of Cambridge
      • Department of Engineering
      Cambridge, England, United Kingdom
  • 2001
    • University of Zagreb
      • Faculty of Electrical Engineering and Computing (FER)
      Zagrabia, Grad Zagreb, Croatia
    • Tokyo Metropolitan Institute
      Edo, Tōkyō, Japan
  • 1970-1997
    • California Institute of Technology
      • Division of Chemistry and Chemical Engineering
      Pasadena, California, 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
  • 1986
    • INTEC
      Santa Fe, New Mexico, United States
  • 1980
    • University of Minnesota Duluth
      Duluth, Minnesota, United States