The methods for reducing or cancelling crossed interactions studied so far in multivariable systems were based on centralised multiple-input multiple-output (MIMO) controllers, which is also usual in multivariable control literature. How ever, despite the performance advantages of centralised controllers, the great majority of industrial process control applications still rely on decentralised or multiloop control structures. Because of their structural constraints, decentralised controllers are not able to suppress by their own interactions of the plant, which are only taken into account at the controller tuning phase. This is not a trivial problem to be solved. In fact, even when supervisory control tools like model predictive control are employed, the coupling among the loops has to be addressed at the lower level (generally decentralised PI/PID control) because of the long sampling time of the supervisory modes. Therefore, the coupling reduction under decentralised structures is a topic of great interest when considering practical control issues. In this chapter, we shall analyse and address this problem. First, some basic concepts related with this control topology are presented. In particular, the relative gain array (RGA) is introduced as a simple interaction measure, whereas the potential effects of crossed interactions are illustrated through a simple example. Then, the sliding mode reference conditioning (SMRC) technique is exploited here in order to limit the amplitude of decentralised control interactions.

Inspec keywords: PI control; decentralised control; MIMO systems; variable structure systems; three-term control

Other keywords: PI control; multivariable system; interaction limit; PID control; industrial process control; multiple-input multiple-output controller; structural constraint; model predictive control; relative gain array; RGA; SMRC technique; decentralised control architecture; coupling reduction; MIMO controller; sliding mode reference conditioning technique; supervisory control tool

Subjects: Multivariable control systems