Optimal Triggering of Networked Control Systems

TL;DR

This paper develops an approximate dynamic programming approach for optimal resource triggering in nonlinear networked control systems, addressing both known and unknown dynamics, and demonstrating effectiveness through numerical examples.

Contribution

It introduces a novel approximate dynamic programming method for optimal triggering, including model-free schemes for unknown dynamics, extending previous stability-focused work.

Findings

Effective in various network conditions including stochastic networks

Converges to near-optimal solutions with proven stability

Demonstrated improved resource allocation in numerical simulations

Abstract

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.