Andrea Lecchini-Visintini

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Iterative Feedback Tuning (IFT) is a widely used procedure for controller tuning. It is a sequence of iteratively performed special experiments on the plant interlaced with periods of data collection under normal operating conditions. In this paper we derive the asymptotic convergence rate of IFT for disturbance rejection, which is one of the main fields of(More)
We introduce bounds on the finite-time performance of random searches based on Markov chain Monte Carlo methods for approaching the global solution of stochastic optimization problems defined on continuous domains. In contrast to existing results these bounds can be used in practice as rigorous stopping criteria. Our results are inspired by the concept of(More)
—We introduce novel tests utilizing a limited amount of experimental and possibly noisy data obtained with an existing known stabilizing controller connected to an unknown plant for verifying that the introduction of a proposed new controller will stabilize the plant. The tests depend on the assumption that the unknown plant is stabilized by a known(More)
This paper delivers an analysis of the least-squares estimation of the Laguerre coefficients of a linear discrete-time system from step response data. The original contribution consists in an explicit formula for the bias error on the estimated coefficients due to the under-modelling of the system. The formula, jointly with some a-priori information on the(More)
The safety of the flights, and in particular separation assurance, is one of the main tasks of Air Traffic Control. Conflict resolution refers to the process used by air traffic controllers to prevent loss of separation. Conflict resolution involves issuing instructions to aircraft to avoid loss of safe separation between them and, at the same time, direct(More)
Iterative Feedback Tuning (IFT) is a data-based method for the tuning of restricted complexity controllers. At each iteration, an update for the controller parameters is estimated from data obtained partly from the normal operation of the closed loop system and partly from a special experiment, in which the output signal obtained under normal operation is(More)
Iterative Feedback Tuning (IFT) is a data-based method for the iterative tuning of restricted complexity controllers. A " special experiment " in which a batch of previously collected output data is fed back at the reference input allows one to compute an unbiased estimate of the gradient of the control performance criterion. We show that, by performing an(More)
Simulated annealing is a popular method for approaching the solution of a global optimization problem. Existing results on its performance apply to discrete com-binatorial optimization where the optimization variables can assume only a finite set of possible values. We introduce a new general formulation of simulated an-nealing which allows one to guarantee(More)
— We define the concept of approximate domain optimizer for deterministic and expected value optimization criteria. Roughly speaking, a candidate optimizer is an approximate domain optimizer if only a small fraction of the optimization domain is more than a little better than it. We show how this concept relates to commonly used approximate optimizer(More)