Block-oriented Continuous-time Modeling for Nonlinear Systems under Sinusoidal Inputs

Discrete-time modeling (DTM) dominates the systems engineering literature in the applications of block-oriented modeling. The discrete environment of computer-based process control systems and discrete sampling are the two major reasons [1]. Also, a DTM is easier to obtain because all input changes are approximated by piecewise step input sequences. Nonetheless, DTM has (potentially) two critical drawbacks relative to continuous-time modeling (CTM). DTM requires constant and frequent sampling and can only predict at those points. DTM is not potentially as accurate as CTM since, at best, it can only approximate continuous-time processes. In addition, if the error term for a model is stochastically continuous (as in Brownian motion) and must be treated as such, then DTM is not useful. For Hammerstein and Wiener CTM, our research group [2, 3] introduced a non-compact CTM approach without restriction as well as a compact approach with restriction to piece-wise step inputs. This article extends their work and proposes compact CTM algorithms under sinusoidal input sequences for Hammerstein and Wiener modeling. The proposed algorithm depends only on the most previous input change and