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  • 1. de Sousa, Joao Borges
    et al.
    Johansson, Karl H.
    KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Silva, Jorge
    Speranzon, Alberto
    A verified hierarchical control architecture for co-ordinated multi-vehicle operations2007In: International journal of adaptive control and signal processing (Print), ISSN 0890-6327, E-ISSN 1099-1115, Vol. 21, no 02-3, p. 159-188Article in journal (Refereed)
    Download full text (pdf)
    multirobot_ijacsp06
  • 2.
    Giannis, Roussos
    et al.
    NTUA.
    Dimarogonas, Dimos V.
    Kyriakopoulos, Kostas J.
    NTUA.
    3D Navigation and Collision Avoidance for Nonholonomic aircraft-like vehicles2010In: International journal of adaptive control and signal processing (Print), ISSN 0890-6327, E-ISSN 1099-1115, Vol. 24, no 10, p. 900-920Article in journal (Refereed)
    Abstract [en]

    This paper extends the Navigation Function methodology to the case of 3D nonholonomic vehicles, both in single agent and multi-agent problems. The kinematic, nonholonomic, three-dimensional model considered is chosen to resemble the motion of an aircraft by preventing any movement along the lateral or perpendicular axis, as well as preventing high yaw rotation rates. The discontinuous feedback control law used is based on the artificial potential field generated by Dipolar Navigation Functions and steers the agents away from obstacles or each other and towards their destinations, while respecting the nonholonomic constraints present. The convergence properties of the proposed control strategies are formally guaranteed and verified by non-trivial simulation results

  • 3.
    Hjalmarsson, Håkan
    KTH, Superseded Departments (pre-2005), Signals, Sensors and Systems.
    Efficient tuning of linear multivariable controllers using iterative feedback tuning1999In: International journal of adaptive control and signal processing (Print), ISSN 0890-6327, E-ISSN 1099-1115, Vol. 13, no 7, p. 553-572Article in journal (Refereed)
    Abstract [en]

    Iterative feedback tuning is a direct tuning method using closed-loop experimental data. The method is based on numerical optimization and in each iteration an unbiased gradient estimate is used. Due to these unbiased gradient estimates, the method converges to a stationary point of the control criterion provided the closed loop signals remain bounded throughout the iterations. In this contribution, it is shown how such unbiased estimates can be obtained for multivariable linear time-invariant systems. Particular attention is given to the issue of keeping the experiment time to a minimum and several efficient algorithms are presented. It is shown that, for tuning an arbitrary linear time-invariant multivariable controller with nw inputs and nu outputs, 1+nu×nw experiments are sufficient in each iteration of the algorithm. For disturbance rejection, an alternative algorithm is proposed which requires nu+nw experiments. As an illustration, the method is applied to a simulation model of a gas turbine engine.

  • 4.
    Hjalmarsson, Håkan
    KTH, Superseded Departments (pre-2005), Signals, Sensors and Systems.
    Iterative feedback tuning - an overview2002In: International journal of adaptive control and signal processing (Print), ISSN 0890-6327, E-ISSN 1099-1115, Vol. 16, no 5, p. 373-395Article in journal (Refereed)
    Abstract [en]

    Adaptive and iterative control algorithms based on explicit criterion minimization are briefly reviewed and an overview of one such algorithm, iterative feedback tuning (IFT), is presented. The basic IFT algorithm is reviewed for both single-input/single-output and multi-input/multi-output systems. Subsequently the application to non-linear systems is discussed. Stability and robustness aspects are covered. A survey of existing extensions, applications and related methods is also provided.

  • 5.
    Händel, Peter
    et al.
    Uppsala universitet.
    Tichavsky, P.
    Adaptive estimation for periodic signal enhancement and tracking1994In: International journal of adaptive control and signal processing (Print), ISSN 0890-6327, E-ISSN 1099-1115, Vol. 8, no 5, p. 447-456Article in journal (Refereed)
    Abstract [en]

    A fast algorithm for enhancement and tracking of periodic signals with fixed or slowly varying central frequency in additive noise is proposed. The algorithm is based on results from discounted least squares identification of a harmonic signal model combined with a recursive update scheme for the estimated fundamental frequency. In particular the gain sequence of the algorithm is determined by the fundamental frequency and an eligible forgetting factor. The performance of the algorithm is compared with theoretical lower bounds and it is shown that the algorithm yields good results.

  • 6.
    Händel, Peter
    et al.
    KTH, Superseded Departments (pre-2005), Signals, Sensors and Systems.
    Tichavsky, P.
    Savaresi, S. M.
    Large error recovery for a class of frequency tracking algorithms1998In: International journal of adaptive control and signal processing (Print), ISSN 0890-6327, E-ISSN 1099-1115, Vol. 12, no 5, p. 417-436Article in journal (Refereed)
    Abstract [en]

    The performance of a recently proposed high-order adaptive notch filter (HANF) for frequency estimation and tracking is studied. An analysis technique utilizing approximations with linear filters is employed to derive closed-form performance expressions for a noisy sinusoidal input signal. Important performance measures, such as stability, noise rejection, statistical efficiency, and tracking ability, are studied in detail, and rules for the design variables are given. A study is presented where the performance of HANF is compared with the performance of a minimal order adaptive notch filter (ANF), as well as with a frequency tracker based on least squares-modelling-the multiple frequency tracker (MFT). The study reveals that HANF is a competitive alternative to ANF, but also that, in general, the MFT is the method of choice.

  • 7.
    Jansson, Henrik
    et al.
    KTH, Superseded Departments (pre-2005), Signals, Sensors and Systems.
    Hjalmarsson, Håkan
    KTH, Superseded Departments (pre-2005), Signals, Sensors and Systems.
    Gradient approximations in iterative feedback tuning for multivariable processes2004In: International journal of adaptive control and signal processing (Print), ISSN 0890-6327, E-ISSN 1099-1115, Vol. 18, no 8, p. 665-681Article in journal (Refereed)
    Abstract [en]

    Iterative feedback tuning (IFT) is a model free control tuning method using closed-loop experiments. For single-input single-output (SISO) systems only 2 or 3, depending on the controller structure, closed-loop experiments are required. However for multivariable systems the number of experiments increases to be proportional to the dimension of the controller. In this contribution several methods are proposed to reduce the experimental time by approximating the gradient of the cost function. One of these methods uses the same technique of shifting operators as is used in IFT for SISO systems. This method is further analysed and sufficient conditions for local convergence are derived. It is shown that even if there are commutation errors due to the approximation method, the numerical optimization may still converge to the true optimum.

  • 8. Krishnamurthy, Vikram
    et al.
    Wahlberg, Bo
    Department of Electrical Engineering, Linköping University, Linkoping, Sweden.
    Moore, John B.
    Factorizations that relax the positive real condition in continuous-time and fast-sampled ELS schemes1990In: International journal of adaptive control and signal processing (Print), ISSN 0890-6327, E-ISSN 1099-1115, Vol. 4, no 5, p. 389-414Article in journal (Refereed)
    Abstract [en]

    This paper proposes extended least-squares (ELS) for ARMAX model identification of continuous-time and certain discrete-time systems. The schemes have a relaxed strictly positive real (SPR) condition for global convergence. The relaxed SPR scheme is achieved by introducing overparametrization and prefiltering but without introducing ill-conditioning. The schemes presented are the first such proposed for continuous-time systems. The concepts developed in continuous time carry through to fast-sampled continuous-time systems and associated discrete-time ELS algorithms. For such situations, in comparison with previously proposed discrete-time schemes, the degree of overparametrization required in the proposed scheme of this paper is significantly lower. The reduction is achieved by using more suitable prefiltering and overparametrization techniques than previously proposed. We also establish the persistence of excitation (PE) of the regression vectors in the proposed ELS schemes to assure strong consistency, obtain convergence rates and provide robustness to unmodelled dynamics. To prove the PE of continuous-time regression vectors, we develop output reachability characterization for MIMO linear continuous-time systems.

  • 9.
    Larsson, Christian A.
    et al.
    KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Hägg, Per
    KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Hjalmarsson, Håkan
    KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.
    Generation of signals with specified second-order properties for constrained systems2016In: International journal of adaptive control and signal processing (Print), ISSN 0890-6327, E-ISSN 1099-1115, Vol. 30, no 3, p. 456-472Article in journal (Refereed)
    Abstract [en]

    This contribution considers the problem of realizing an input signal with a desired autocorrelation sequence satisfying both input and output constraints for the system it is to be applied to. This is an important problem in system identification, firstly, because the quality and accuracy of the identified model are highly dependent on the excitation signal used during the experiment and secondly, because on real processes, it is often important to constrain the input and output of the process because of actuator saturation and safety considerations. The signal generation is formulated as a model predictive controller with probabilistic constraints to make the algorithm robust to model uncertainties and process noise. The corresponding optimization problem is then solved with tools from scenario-based stochastic optimization. To reduce the model uncertainties, the method is made adaptive where a new model of the system and its uncertainties are reidentified. The algorithm is successfully applied to a simulation example and in a practical experiment for the identification of a quadruple tank lab process.

  • 10. Wigren, T.
    et al.
    Remle, S.
    Wahlberg, Bo
    KTH, Superseded Departments (pre-2005), Signals, Sensors and Systems.
    Analysis of a low-complexity change detection scheme2000In: International journal of adaptive control and signal processing (Print), ISSN 0890-6327, E-ISSN 1099-1115, Vol. 14, no 5, p. 481-503Article in journal (Refereed)
    Abstract [en]

    In many applications, for example in fault detection, it is important to discriminate between changes in system dynamics and abrupt changes in the disturbance level. A new low-complexity change detection method based on the average behaviour of the estimated impulse response parameters of the normalized least mean-square (NLMS) algorithm is presented. The solution includes second-order Kalman filters based on exponential transient models for parameter convergence. Explicit formulas for time-varying state covariances and Kalman gains are given. The receiver operating characteristics (ROC) is also computed and used for performance evaluation. The effects of the approximations in the averaging analysis that occur for high adaptation gains are handled with an experimental ROC analysis.

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