Feedforward control of a class of hybrid systems using an inverse model

• Published in: Mathematics and computers in simulation 2011-11, Vol.82 (3), p.414-427
• Main Authors: Karer, Gorazd, Mušič, Gašper, Škrjanc, Igor, Zupančič, Borut
• Format: Article
• Language: English
• Subjects: Batch reactor; Hybrid fuzzy model; Hybrid systems; Inverse-model-based control; Nonlinear systems
• ISSN: 0378-4754; 1872-7166
• DOI: 10.1016/j.matcom.2010.10.015

In this paper we describe the design of a control algorithm for MISO systems, which can be modelled as hybrid fuzzy models. Hybrid fuzzy models present a convenient approach to modelling nonlinear hybrid systems. We discuss the formulation of a hybrid fuzzy model, its structure and the identification procedure. This is followed by a derivation of the inverse model and its implementation in the control algorithm. The control scheme we are discussing splits the control algorithm in two parts: the feedforward part and the feedback part. In the paper, we deal with the feedforward part of the control algorithm, which is based on an inverse of a hybrid fuzzy model. Next, a batch-reactor process is introduced. The modelling of the batch reactor is tackled and the results of the simulation experiments using the proposed control algorithm are presented. The experiments involved controlling the temperature of a batch reactor using two on/off input valves and a continuous mixing valve. The main advantage of the proposed approach is that the feedforward part of the control algorithm can bring the system close to the desired adjusted feasible trajectory, which avoids the need for a very complex feedback part of the algorithm. Therefore, the control algorithm presents a low computational burden, particularly comparing to the standard model predictive control algorithms. These usually require a considerable computational effort, which often thwarts their implementation on real industrial systems. Nevertheless, we show that using the proposed control approach the hybrid fuzzy model framework presents a convenient option for modelling complex systems for control purposes in practice.