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  • 1.
    Gao, Yulong
    et al.
    KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Jafarian, Matin
    KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Johansson, Karl H.
    KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Xie, Lihua
    Distributed Freeway Ramp Metering: Optimization on Flow Speed2017In: 2017 IEEE 56th Annual Conference on Decision and Control, CDC 2017, IEEE , 2017Conference paper (Refereed)
    Abstract [en]

    This paper studies the distributed freeway ramp metering problem, for which the cell transmission model (CTM) is utilized. Considering the jam density and the upper bounds on the queue lengths and the ramp metering, we first provide feasibility conditions with respect to the external demand to ensure the controllability of the freeway. Assuming that the freeway is controllable, we formulate an optimization problem which tradeoffs the maximum average flow speed and the minimum waiting queue for each cell. Although the cells of the CTM are dynamically coupled, we propose a distributed backward algorithm for the optimization problem and prove that the solution to the problem is a Nash equilibrium. Furthermore, if the optimization problem is simplified to only maximization of the average flow speed, we argue that the obtained explicit distributed controller is globally optimal. A numerical example is given to illustrate the effectiveness of the proposed control algorithm.

  • 2.
    Jafarian, Matin
    KTH, School of Electrical Engineering and Computer Science (EECS), Automatic Control.
    Robust consensus of unicycles using ternary and hybrid controllers2017In: International Journal of Robust and Nonlinear Control, ISSN 1049-8923, E-ISSN 1099-1239, Vol. 27, no 17, p. 4013-4034Article in journal (Refereed)
    Abstract [en]

    This paper presents consensus of the orientations and average positions for a group of unicycles using ternary and hybrid controllers. The decentralized controllers designed to reach consensus of the average positions take only values in the set {-1; 0; +1}. In addition, a hybrid controller is introduced to control the orientations. Finite-time practical consensus of the average positions is proven despite the simple ternary control laws together with asymptotic consensus of the orientations. Furthermore, the consensus problem is studied in the presence of matched input disturbances that are locally rejected using an internal-model-based controller. The analysis is performed in a hybrid framework. Simulation results illustrate the effectiveness of the design.

  • 3.
    Jafarian, Matin
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control).
    Mamduhi, Mohammad H.
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control).
    Johansson, Karl H.
    KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Decision and Control Systems (Automatic Control).
    Stochastic phase-cohesiveness of discrete-time Kuramoto oscillators in a frequency-dependent tree network2019In: Proceedings 2019 18th European Control Conference (ECC), IEEE , 2019, p. 1987-1992Conference paper (Refereed)
    Abstract [en]

    This paper presents the notion of stochastic phase-cohesiveness based on the concept of recurrent Markov chains and studies the conditions under which a discrete-time stochastic Kuramoto model is phase-cohesive. It is assumed that the exogenous frequencies of the oscillators are combined with random variables representing uncertainties. A bidirectional tree network is considered such that each oscillator is coupled to its neighbors with a coupling law which depends on its own noisy exogenous frequency. In addition, an undirected tree network is studied. For both cases, a sufficient condition for the common coupling strength (kappa) and a necessary condition for the sampling-period are derived such that the stochastic phase-cohesiveness is achieved. The analysis is performed within the stochastic systems framework and validated by means of numerical simulations.

  • 4.
    Jafarian, Matin
    et al.
    KTH, School of Electrical Engineering and Computer Science (EECS), Automatic Control.
    Yi, Xinlei
    KTH, School of Electrical Engineering and Computer Science (EECS), Automatic Control.
    Pirani, Mohammad
    KTH, School of Electrical Engineering and Computer Science (EECS), Automatic Control.
    Sandberg, Henrik
    KTH, School of Electrical Engineering and Computer Science (EECS), Automatic Control.
    Johansson, Karl H.
    KTH, School of Electrical Engineering and Computer Science (EECS), Automatic Control.
    Synchronization of Kuramoto oscillators in a bidirectional frequency-dependent tree network2018In: 2018 IEEE CONFERENCE ON DECISION AND CONTROL (CDC), IEEE , 2018, p. 4505-4510Conference paper (Refereed)
    Abstract [en]

    This paper studies the synchronization of a finite number of Kuramoto oscillators in a frequency-dependent bidirectional tree network. We assume that the coupling strength of each link in each direction is equal to the product of a common coefficient and the exogenous frequency of its corresponding source oscillator. We derive a sufficient condition for the common coupling strength in order to guarantee frequency synchronization in tree networks. Moreover, we discuss the dependency of the obtained bound on both the graph structure and the way that exogenous frequencies are distributed. Further, we present an application of the obtained result by means of an event-triggered algorithm for achieving frequency synchronization in a star network assuming that the common coupling coefficient is given.

  • 5.
    Yoo, Jaehyun
    et al.
    KTH, School of Electrical Engineering (EES). KTH, School of Electrical Engineering and Computer Science (EECS), Centres, ACCESS Linnaeus Centre.
    Molin, Adam
    Jafarian, Matin
    KTH, School of Education and Communication in Engineering Science (ECE). KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Esen, Hasan
    Dimarogonas, Dimos V.
    KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Johansson, Karl H.
    KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Event-triggered Model Predictive Control with Machine Learning for Compensation of Model Uncertainties2017In: 2017 IEEE 56th Annual Conference on Decision and Control, CDC 2017, Institute of Electrical and Electronics Engineers (IEEE), 2017, p. 5463-5468Conference paper (Refereed)
    Abstract [en]

    As one of the extensions of model predictive control (MPC), event-triggered MPC takes advantage of the reduction of control updates. However, approaches to event-triggered MPCs may be subject to frequent event-triggering instants in the presence of large disturbances. Motivated by this, this paper suggests an application of machine learning to this control method in order to learn a compensation model for disturbance attenuation. The suggested method improves both event-triggering policy efficiency and control accuracy compared to previous approaches to event-triggered MPCs. We employ the radial basis function (RBF) kernel based machine learning technique. By the universial approximation property of the RBF, which imposes an upper bound on the training error, we can present the stability analysis of the learningaided control system. The proposed algorithm is evaluated by means of position control of a nonholonomic robot subject to state-dependent disturbances. Simulation results show that the developed method yields not only two times less event triggering instants, but also improved tracking performance.

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