Predictive Functional Control for Difficult Second-Order Dynamics with a Simple Pre-compensation Strategy

Abstract

Predictive functional control (PFC) is a fairly straightforward model-based technique for controlling stable and monotonically convergent dynamics in a systematic fashion. However, owing to simplified design assumptions, the control performance generally degrades with oscillatory or unstable processes. This paper focuses on pre-stabilising such difficult dynamics, represented as second-order prediction models, before implementing the PFC. In this proposal, the pre-compensator is designed with a root locus method that shifts the undesirable open-loop poles to the stable break-in/breakaway position by varying compensator gain. It has been highlighted that such dynamics transformation enables PFC application in the standard manner by preserving design simplicity and intuitiveness in terms of parameter tuning and constraint handling. Two simulation examples are included to study the pros and cons of the proposal against the conventional PFC algorithm.