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This paper proposes the use of Sequential Monte Carlo (SMC) as the computational engine for general (non-convex) stochastic Model Predictive Control (MPC) problems. It shows how SMC methods can be used to find global optimisers of non-convex problems, in particular for solving open-loop stochastic control problems that arise at the core of the usual… (More)

- Andrea Lecchini-Visintini, John Lygeros, Jan M. Maciejowski
- IEEE Trans. Automat. Contr.
- 2010

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)

- Andrea Lecchini-Visintini, William Glover, John Lygeros, Jan M. Maciejowski
- IEEE Transactions on Intelligent Transportation…
- 2006

The safety of flights, and, in particular, separation assurance, is one of the main tasks of air traffic control (ATC). Conflict resolution refers to the process used by ATCs 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 them to their… (More)

- Roland Hildebrand, Andrea Lecchini-Visintini, Gabriel Solari, Michel Gevers
- IEEE Trans. Automat. Contr.
- 2004

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)

- Roland Hildebrand, Andrea Lecchini-Visintini, Gabriel Solari, Michel Gevers
- IEEE Trans. Automat. Contr.
- 2005

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)

- Arvin Dehghani, Andrea Lecchini-Visintini, Alexander Lanzon, Brian D. O. Anderson
- IEEE Trans. Automat. Contr.
- 2009

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)

- Andrea Lecchini-Visintini, Michel Gevers
- Systems & Control Letters
- 2004

Abstract 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… (More)

- Roland Hildebrand, Andrea Lecchini-Visintini, Gabriel Solari, Michel Gevers
- IEEE Trans. Automat. Contr.
- 2005

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)

Simulated annealing is a popular method for approaching the solution of a global optimization problem. Existing results on its performance apply to discrete combinatorial optimization where the optimization variables can assume only a finite set of possible values. We introduce a new general formulation of simulated annealing which allows one to guarantee… (More)

- Andrea Lecchini-Visintini, Alexander Lanzon, Brian D. O. Anderson
- Automatica
- 2006

A controller change from a current controller which stabilises the plant to a new controller, designed on the basis of an approximate model of the plant and with guaranteed bounds on the stability properties of the true closed loop, is called a safe controller change. In this paper, we present a model reference approach to the determination of safe… (More)