Journal: IFAC-PapersOnLine

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Elsevier

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2405-8963

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2015 - 2019842020 - 202696
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Publications1 - 10 of 180
  • A distributed voltage stability margin for power distribution networks
    Item type: Conference Paper
    Aolaritei, Liviu; Bolognani, Saverio; Dörfler, Florian (2017)
    IFAC-PapersOnLine ~ 20th IFAC World Congress. Proceedings
    We consider the problem of characterizing and assessing the voltage stability in power distribution networks. Different from previous formulations, we consider the branch-flow parametrization of the power system state, which is particularly effective for radial networks. Our approach to the voltage stability problem is based on a local, approximate, yet highly accurate characterization of the determinant of the power flow Jacobian. Our determinant approximation allows us to construct a voltage stability index that can be computed in a fully scalable and distributed fashion. We provide an upper bound on the approximation error, and we show how the proposed index outperforms other voltage indices recently proposed in the literature.
  • Linear Time-Periodic System Identification with Grouped Atomic Norm Regularization
    Item type: Conference Paper
    Yin, Mingzhou; Iannelli, Andrea; Khosravi, Mohammad; et al. (2020)
    IFAC-PapersOnLine ~ 21st IFAC World Congress
    This paper proposes a new methodology in linear time-periodic (LTP) system identification. In contrast to previous methods that totally separate dynamics at different tag times for identification, the method focuses on imposing appropriate structural constraints on the linear time-invariant (LTI) reformulation of LTP systems. This method adopts a periodically-switched truncated infinite impulse response model for LTP systems, where the structural constraints are interpreted as the requirement to place the poles of the non-truncated models at the same locations for all sub-models. This constraint is imposed by combining the atomic norm regularization framework for LTI systems with the group lasso technique in regression. As a result, the estimated system is both uniform and low-order, which is hard to achieve with other existing estimators. Monte Carlo simulation shows that the grouped atomic norm method does not only show better results compared to other regularized methods, but also outperforms the subspace identification method under high noise levels in terms of model fitting.
  • Multiscale Thermal Management of Computing Systems - The MULTITHERMAN approach
    Item type: Conference Paper
    Bartolini, Andrea; Conficoni, Christian; Diversi, Roberto; et al. (2017)
    IFAC-PapersOnLine ~ 20th IFAC World Congress. Proceedings
    This work presents the research findings of the Multithermal ERC-Advanced project, in terms of multi-scale thermal control of complex large scale computing platforms such as High Performance Computing Systems, and datacenters. In this respect, the challenges and opportunities concerning thermal control of computing systems are discussed, along with the proposed innovative solutions. Control and management problem is divided at different hierarchical, and scale, levels (compute node and system). Then, given the control knobs and sensors available at each level, advanced control and system identification techniques are proposed, to achieve a holistic thermal management of complex computing platforms, with benefits for thermal stability guarantees and energy consumption optimization. Such features are of utmost importance for advancements in next generation large scale computing solutions.
  • A framework for distributed control based on overlapping estimation for cooperative tasks
    Item type: Conference Paper
    Stürz, Yvonne R.; Eichler, Annika; Smith, Roy (2017)
    IFAC-PapersOnLine ~ 20th IFAC World Congress. Proceedings
    We present a framework for distributed control where the subsystems estimate overlapping components of the state of the overall system. This enables the implementation of decentralized state feedback controllers, which depend on the overlapping state estimates. For a distributed framework, communication can be added. By choosing the amount of communication and the degree of overlap in the state estimates, a trade-off between increasing computational effort, communication, and performance can be made. This approach is especially suited for systems with strongly coupled subsystems, which are restricted in communication in their operation. The computational effort of each subsystem depends only on the chosen degree of overlap in the estimates and thus stays constant with an increasing number of subsystems, which makes the approach convenient for the operation of large systems. The design problem of the distributed control and estimation is formulated with bilinear matrix inequalities in an augmented state space. A numerical example of a cooperative manipulation task illustrates the performance of the distributed control and estimation scheme.
  • Inexact GMRES Policy Iteration for Large-Scale Markov Decision Processes
    Item type: Conference Paper
    Gargiani, Matilde; Liao-McPherson, Dominic; Zanelli, Andrea; et al. (2023)
    IFAC-PapersOnLine ~ 22nd IFAC World Congress
    Policy iteration enjoys a local quadratic rate of contraction, but its iterations are computationally expensive for Markov decision processes (MDPs) with a large number of states. In light of the connection between policy iteration and the semismooth Newton method and taking inspiration from the inexact variants of the latter, we propose inexact policy iteration, a new class of methods for large-scale finite MDPs with local contraction guarantees. We then design an instance based on the deployment of the generalized minimal residual method (GMRES) for the approximate policy evaluation step, which we call inexact GMRES policy iteration. Finally, we demonstrate the superior practical performance of inexact GMRES policy iteration on an MDP with 10000 states, where it achieves a ×5.8 and ×2.2 speedup with respect to policy iteration and optimistic policy iteration, respectively.
  • Robustness analysis and tuning for pressure control in managed pressure drilling
    Item type: Conference Paper
    Li, Qin; Kamel, Minn (2016)
    IFAC-PapersOnLine ~ 11th IFAC Symposium on Dynamics and Control of Process Systems Including Biosystems, DYCOPS-CAB 2016. Proceedings
    In this paper, we present a general framework for robustness analysis for pressure control in managed pressure drilling (MPD). In particular, we apply the analysis to the pressure controller proposed in the work Godhavn et al. (2011), based on which we also give an approach to search for controller tuning parameters with the goal of maximizing the robustness of system stability and control performance to various sorts of uncertainties, disturbances and noise. The resulting tuning table can be used for online computation of the controller parameters. The method proves effective in a simulation study.
  • Modelling long-term vibration monitoring data with Gaussian Process time-series models
    Item type: Conference Paper
    Avendaño Valencia, Luis David; Chatzi, Eleni (2019)
    IFAC-PapersOnLine ~ 3rd IFAC Workshop on Linear Parameter Varying Systems, LPVS 2019. Proceedings
    Gaussian Process (GP) time-series models are a special type of models for Linear Parameter Varying (LPV) systems in which the parameters are represented as stochastic variables following a Gaussian Process regression of the scheduling variables. GP time-series models are ideal for the representation of LPV systems where some of the scheduling variables are uncertain or immeasurable, as is the case in most real-life Structural Health Monitoring (SHM) applications. In this work, a fully parametric version of GP is adopted, most suitable for identification based on large datasets typically originated in SHM campaigns. Here, the model identification problem is addressed via global and local approaches, while is demonstrated that the latter case corresponds to a sub-optimal version of the global optimization. Finally, the GP time-series modelling methodology is demonstrated on the identification of the simulated vibration response of a wind turbine blade, where temperature and wind speed act as scheduling parameters.
  • Follow the Clairvoyant: an Imitation Learning Approach to Optimal Control
    Item type: Conference Paper
    Martin, Andrea; Furieri, Luca; Dörfler, Florian; et al. (2023)
    IFAC-PapersOnLine ~ 22nd IFAC World Congress
    We consider control of dynamical systems through the lens of competitive analysis. Most prior work in this area focuses on minimizing regret, that is, the loss relative to an ideal clairvoyant policy that has noncausal access to past, present, and future disturbances. Motivated by the observation that the optimal cost only provides coarse information about the ideal closed-loop behavior, we instead propose directly minimizing the tracking error relative to the optimal trajectories in hindsight, i.e., imitating the clairvoyant policy. By embracing a system level perspective, we present an efficient optimization-based approach for computing follow-the-clairvoyant (FTC) safe controllers. We prove that these attain minimal regret if no constraints are imposed on the noncausal benchmark. In addition, we present numerical experiments to show that our policy retains the hallmark of competitive algorithms of interpolating between classical H2 and H∞ control laws – while consistently outperforming regret minimization methods in constrained scenarios thanks to the superior ability to chase the clairvoyant.
  • Sequential linear quadratic optimal control for nonlinear switched systems
    Item type: Conference Paper
    Farshidian, Farbod; Kamgarpour, Maryam; Pardo, Diego; et al. (2017)
    IFAC-PapersOnLine ~ 20th IFAC World Congress. Proceedings
    In this contribution, we introduce an efficient method for solving the optimal control problem for an unconstrained nonlinear switched system with an arbitrary cost function. We assume that the sequence of the switching modes are given but the switching time in between consecutive modes remains to be optimized. The proposed method uses a two-stage approach as introduced by Xu and Antsaklis (2004) where the original optimal control problem is transcribed into an equivalent problem parametrized by the switching times and the optimal control policy is obtained based on the solution of a two-point boundary value differential equation. The main contribution of this paper is to use a Sequential Linear Quadratic approach to synthesize the optimal controller instead of solving a boundary value problem. The proposed method is numerically more efficient and scales very well to the high dimensional problems. In order to evaluate its performance, we use two numerical examples as benchmarks to compare against the baseline algorithm. In the third numerical example, we apply the proposed algorithm to the Center of Mass control problem in a quadruped robot locomotion task.
  • Zero-Order Robust Nonlinear Model Predictive Control with Ellipsoidal Uncertainty Sets
    Item type: Conference Paper
    Zanelli, Andrea; Frey, Jonathan; Messerer, Florian; et al. (2021)
    IFAC-PapersOnLine ~ 7th IFAC Conference on Nonlinear Model Predictive Control (NMPC 2021)
    In this paper, we propose an efficient zero-order algorithm that can be used to compute an approximate solution to robust optimal control problems (OCP) and robustified nonconvex programs in general. In particular, we focus on robustified OCPs that make use of ellipsoidal uncertainty sets and show that, with the proposed zero-order method, we can efficiently obtain suboptimal, but robustly feasible solutions. The main idea lies in leveraging an inexact sequential quadratic programming (SQP) algorithm in which an advantageous sparsity structure is enforced. The obtained sparsity allows one to eliminate the variables associated with the propagation of the ellipsoidal uncertainty sets and to solve a reduced problem with the same dimensionality and sparsity structure of a nominal OCP. The inexact algorithm can drastically reduce the computational complexity of the SQP iterations (e.g., in the case where a structure exploiting interior-point method is used to solve the underlying quadratic programs (QPs), from O(N center dot (n(x)(6) + n(u)(3))) to O(N center dot (n(x)(3) + n(u)(3)))). Moreover, standard embedded QP solvers for nominal problems can be leveraged to solve the reduced QP. Copyright (C) 2021 The Authors.
Publications1 - 10 of 180