We characterize the feedback capacity $C_{FB}$ of general nonlinear decision models (N-DM) through the $n-$finite time or block length feedback information ($n-$FTFI) capacity, $C_{FB,n}$. For an application we consider the multiple-input multiple-output (MIMO) Gaussian DM with memory on past outputs and inputs driven by nonstationary Gaussian noise, and finite-dimensional Gaussian noise in state space form, subject to an average cost constraint of quadratic form. The main theorems show that optimal randomized control strategies that achieve $C_{FB,n}$, consist of multiple parts, that include control, estimation, and information transmission/signalling strategies, and that these strategies are determined using decentralized optimization techniques, and involve filtering Riccati equations and a control Riccati equation.