  The problem of resource allocation of nonlinear networked control systems is
investigated, where, unlike the well discussed case of triggering for
stability, the objective is optimal triggering. An approximate dynamic
programming approach is developed for solving problems with fixed final times
initially and then it is extended to infinite horizon problems. Different cases
including Zero-Order-Hold, Generalized Zero-Order-Hold, and stochastic networks
are investigated. Afterwards, the developments are extended to the case of
problems with unknown dynamics and a model-free scheme is presented for
learning the (approximate) optimal solution. After detailed analyses of
convergence, optimality, and stability of the results, the performance of the
method is demonstrated through different numerical examples.
