Giuseppe De Nicolao

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This paper describes a model predictive control (MPC) algorithm for the solution of a state-feedback robust control problem for discrete-time nonlinear systems. The control law is obtained through the solution of a finite-horizon dynamic game and guarantees robust stability in the face of a class of bounded disturbances and/or parameter uncertainties. A(More)
This paper describes a new kernel-based approach for linear system identification of stable systems. We model the impulse response as the realization of a Gaussian process whose statistics, differently from previously adopted priors, include information not only on smoothness but also on BIBO-stability. The associated autocovariance defines what we call a(More)
Most of the currently used techniques for linear system identification are based on classical estimation paradigms coming from mathematical statistics. In particular, maximum likelihood and prediction error methods represent the mainstream approaches to identification of linear dynamic systems, with a long history of theoretical and algorithmic(More)
Standard single-task kernel methods have recently been extended to the case of multitask learning in the context of regularization theory. There are experimental results, especially in biomedicine, showing the benefit of the multitask approach compared to the single-task one. However, a possible drawback is computational complexity. For instance, when(More)