Observer Synthesis for Switched Discrete-Time Linear Systems using Relaxed Dynamic Programming
In this paper, state estimation for Switched Discrete-Time Linear Systems is performed using relaxed dynamic programming. Taking the Bayesian point of view, the estimation problem is transformed into an infinite dimension al optimization problem. The optimization problem is then solved using relaxed dynamic programming. The estimate of both the mode and the continuous state can then be computed fr