Processes with inherently more than one variable to be controlled are frequently encountered in the industries and they are known as multivariable or multi-input multi-output (MIMO) processes. Interactions usually exist between control loops, which account for the renowned difficulty in their control compared to single-input single-output (SISO) processes. The goal to achieve satisfactory loop performance has hence posed a great challenge in the area of control design. Depending on the application and requirement, either a fully cross-coupled or a multi-loop controller can be adopted for MIMO processes. Although multivariable controllers are capable of providing explicit suppression of interactions, their designs are usually more complicated and practical implementation inevitably more costly. Multi-loop controllers, sometimes known as decentralized controllers, have a simpler structure and, accordingly, less tuning parameters than the fully cross-coupled one. In addition, in the event of component failure, it is relatively easy to stabilize manually, since only one loop is directly affected by the failure (Palmor 1996; Skogestad and Morari 1989). Hence for processes with modest interactions, multi-loop controllers are often more favorable than multivariable controllers.
KeywordsStep Response Tuning Method Transfer Function Matrix Equivalent Process Nyquist Curve
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