MinMax: A Sampling Interval Control Algorithm for Process Control Systems

The traditional sampling method in process control systems is based on a periodic task model. This is because controllers are executed in a strictly periodic manner. Sensors sample the process data and send it periodically to the appropriate controllers through a communication system such as the field bus. Since the field bus is shared by multiple sensors, there is some delay (control loop latency)between the sampling and control actions. In order to minimize the control loop latency, a higher than necessary sampling frequency is typically adopted, which results in unnecessary waste of energy. In this paper, we propose Min Max: a sampling interval control algorithm for tackling this problem. In Min Max, sampling tasks are not periodic but have both maximum and minimum distance constraints. This sampling model has advantages that are especially important in the domain of wireless control for industrial automation. We shall then discuss the jitter property of sampling schemes under this model and propose algorithms for controlling the sampling intervals of sensors in terms of the Min Max problem (UMin Max) which we shall introduce. Though this problem is NP-hard in general, even for special case of unit-time tasks, we show how to reduce Min Max to well-studied scheduling models such as Liu and Layland-type periodic models and pinwheel models, at the expense of some loss of schedulability. These reductions allow us to derive efficient schedulability tests that can be used to solve the sampling interval control problem in practice. Simulations are used to compare the performance of different UMin Max schedulers in two key figures of merit: the acceptance ratio and the jitter ratio. Simulation of a process control system model also shows that UMin Max can reduce about 40% of the traffic load on the communication system which is especially important for energy-aware wireless process control applications.