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The random time delays induced by the network transmission in networked control system (NCS) degrade control performance and can even destabilize the control system. With regards to the long time delay, a distributed model is constructed in this paper. The advanced predictive functional control (PFC) algorithm is introduced in the controller nodes. The network-induced long time delays are specifically considered for predicting the future plant outputs in PFC algorithm. A distributed NCS simulation plant is constructed based on TrueTime toolbox. It is proved that the advanced PFC algorithm can give better control performance than the normal PFC algorithm.

Aiming at the characteristics of thermal process network control system, a mixed scheduling algorithm based on Synthetic Scheduling Index (SSI) is presented. Firstly, a network QoS estimator is used to acquire current network states. When network utilization is smaller than set value, taskpsilas residual time and static priority are adopted to schedule task. Otherwise, network QoS scheduler dispatches task dynamically by MEF-TOD algorithm, combined with taskpsilas residual time and priority in the SSI. Both the mechanism analysis and simulation show that this algorithm has great real-time performance to random load thermal process, with high average performance, low network time-delay and packet loss, and strong practicality.

With regards to the characteristics of uncertain transmission delay and packet dropout of networked control system (NCS), a distributed model is proposed in this paper. Queuing strategy is introduced both in controller nodes and actuator nodes while the transmission delay and packet dropout between controller nodes and actuator nodes is co-compensated by multi-step increment given by the algorithm of general predictive control (GPC) and BP neural network. Simulation experiments are practiced over Ethernet which embraces both uncertain transmission delay and packet dropout, and it is proved that the scheme of co-compensation remains a good control performance.