ROBUST H-INFINITY FILTERING FOR CONTINUOUS-TIME VARYING UNCERTAIN SYSTEMS WITH DETERMINISTIC INPUT SIGNALS

Many dynamical systems involve not only process and measurement noise signals but also parameter uncertainty and known input signals. When L (2) or H infinity filters that were designed based on a ''nominal'' mo del of the system are applied, the presence of parameter uncertainty w ill not only affect the noise attenuation property of the filter but a lso introduce a bias proportional to the known input signal, and the l atter may be very appreciable. In this paper, we introduce a finite-ho rizon robust H infinity filtering method that provides a guaranteed H infinity bound for the estimation error in the presence of both parame ter uncertainty and a known input signal. This method is developed by using a game-theoretic approach, and the results generalize those obta ined for cases without parameter uncertainty or without a known input signal. It is also demonstrated, via an example, that the proposed met hod provides significantly improved signal estimates.