A Progressive Output Strategy for Real-time Feedback Control Systems
Intelligent Automation and Soft Computing
First page number:
Last page number:
The real-time requirements imposed on a feedback control system are often hard to be met, as the controller spends a disproportionately large amount of time waiting for a control cycle to reach its final state. When such a final state is established, multiple tasks have to be prioritized and launched altogether simultaneously, and the system is given an extremely short time window to generate its output. This huge gap between the wait and action times, perceived as a load unbalancing problem, hinders a control decision to be made in real time. To address this challenging problem, in this paper, we present a progressive output strategy that divides a control cycle into a few fine-grained control intervals, and the entire workload is scheduled across these control intervals. Dubbed as Progressive Output Strategy (PROS), this approach actively requests intermediate states be created between adjacent control cycles in an adaptive manner. Specifically, as the sensing information is arriving, a system that adopts PROS can generate a series of intermediate solutions that eventually converge to the final optimal control signal. This way, the controller will no longer waste its time idling while waiting for the arrival of all the data for one-shot decision-making. Rather the system actually cuts down the waiting time and is able to act on the intermediate data/states throughout the entire control cycle. Experimental results have confirmed that adopting the PROS in a feedback control loop can evenly distribute the workload over a control cycle, and thus, the time delay is reduced by as much as two orders of magnitude, which is essential to meet the most stringent timing requirements.
Real-Time Feedback Control; Online Optimization; Adaptive Sampling
Electrical and Computer Engineering | Engineering
A Progressive Output Strategy for Real-time Feedback Control Systems.
Intelligent Automation and Soft Computing, 26(3),