摘要：AbstractWe present a method for data-based estimation of the H2-norm of a linear time-invariant system from input-output data in a probabilistic setting by employing the recent advances in Gaussian process system identification using stable-spline kernels. Advantages of this starting point include that the norm can be estimated for the continuous-time system and over infinite horizon, even though only a finite number of measurements are available. We approximate theH2-norm distribution as Gaussian, whose expectation can even be obtained analytically, while we use a numerical scheme based on Gaussian process quadrature for the variance. Not only do we utilize the posterior variance of the Gaussian process to derive an error estimate for theH2-norm, but also to tune the estimation by optimizing the input sequence. The performance of the developed scheme is thoroughly evaluated in simulation.
关键词：KeywordsNonparametric methodsBayesian methodsData-based controlIdentification for controlInputexcitation design
