Initial conditions optimization of nonlinear dynamic systems with applications to output identification and control

The paper presents a gradient-based algorithm for initial conditions optimization of nonlinear multivariable systems with boundary and state vectors constraints. The algorithm has a backward-in-time recurrent structure similar to the backpropagation-through-time (BPTT) algorithm, which is mostly used as a learning algorithm for dynamic neural networks. It is shown that dynamic parameter optimization problem can be formulated as the initial conditions optimization problem. Further, it is shown that output parameter identification and output controller design problems can be formulated as dynamic parameter optimization problem. The effectiveness of the proposed algorithm is demonstrated on the problem of output identification and control of a nonlinear two-mass torsional system.