Optimal Bandwidth Scheduling for Resource-constrained Networks

In networked control systems (NCSs) with resource constraints, there is an unavoidable tradeoff between the control performance and the quality of service. To address these problems, we present multi-objective programming with a set of constraints to optimize control performance and bandwidth consumption for the first time. Thanks to robust nonlinear approximate function and computational cost, a feed-forward neural network as optimal approximator is employed. The role of the neural network, which provides a good approximation to the optimal solution, dynamically allocates the bandwidth of each control loop so that the overall system performance is maximized while bandwidth consumption is minimized. Preliminary simulation results show that the proposed optimal strategy is an effective tradeoff method between the control performance and bandwidth consumption in networked control applications.