Due to the variation of the networks’ workload, control networks with resource constraints usually run in an unpredictable open environment. A feedback scheduler based on least squares support vector machines (LSSVM) is designed in order to guarantee the stability of the system. It periodically monitors the network resources, predicates the available utilization for the next period by using LSSVM technique, and adopts interpolated method to calculate the next sampling period from predicative value. Consequently, the system’s resources are dynamically allocated by this feedback scheduling mechanism. Three different strategies, which are fixed bandwidth allocation, LSSVM based feedback scheduling technique and Elman neural network based feedback scheduling technique, are compared respectively. The results of simulation indicate that the predictive feedback scheduling strategy can guarantee the stability of the system under flexible workload, and prove that the feedback scheduling strategy is an effective tradeoff method between quality of control and quality of service. Simultaneity, the proposed algorithm has better performances than another algorithm based on Elman neural network, especially at online training speed.