Optimal Triggering of Networked Control Systems
This addresses resource allocation in control systems, but appears incremental as it builds on existing triggering methods by focusing on optimality rather than stability.
The paper tackles optimal triggering in nonlinear networked control systems, developing an approximate dynamic programming approach that handles fixed final time and infinite horizon problems, with extensions to stochastic networks and unknown dynamics, achieving demonstrated performance through numerical examples.
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.