In this chapter, we consider the identification of single-input single-output linear-parameter-varying models when both the output and the time-varying parameter measurements are affected by bounded noise. First, the problem of computing exact parameter uncertainty intervals is formulated in terms of semialgebraic optimization. Then, a suitable relaxation technique is presented to compute parameter bounds by means of convex optimization. Advantages of the presented approach with respect to previously published results are discussed.
|Title of host publication||Linear parameter-varying system identification : new developments and trends|
|Editors||P. Lopes dos Santos, T. Perdicoúlis, C. Novara, A. Ramos, D. Rivera|
|Place of Publication||Singapore|
|Publication status||Published - 2011|
|Name||Advanced Series in Electrical and Computer Engineering|