A dynamic programming algorithm for input estimation on linear time-variant systems

A time domain input estimation algorithm for linear systems with general time-varying parameters is developed. The algorithm is an extension of an existing approach for time-invariant state space models and several new features, such as higher order input approximations and an extended time-variant output relation including direct input influence, are introduced. Numerical examples are given to illustrate the new features and show that the algorithm is valid in a general time-variant setting. In particular, excellent results are obtained for an ill-posed moving force identification problem with noise-contaminated data, treated with Tikhonov regularization.