Multivariable controller tuning

Abstract

The problem of tuning individual loops in a multivariable controller is investigated. It is shown how the performance of a specific loop relates to a row in the controller matrix. Several interpretations of this relation are given. An algorithm is also presented that estimates the model required for the tuning via a relay feedback experiment. The algorithm does not need any prior information about the system or the controller. The results are illustrated by an example.Qc 2012022

The quadruple-tank process: a multivariable laboratory process with an adjustable zero

“…This is intuitive and agrees also with the RGA analysis, which suggested a permutation of and . Multivariable controller-tuning method based on relay feedback experiments have also been investigated on the quadruple-tank process [17], [29]. It was shown in [29] that several of the methods proposed in the literature cannot handle automatic control design for both the minimum-phase and the nonminimum-phase setting.…”

Auto-Tuning of Pid Controllers for Mimo Processes by Relay Feedback

“…More complex phenomena can occur in relay feedback systems, such as multiple limit cycles, multimodal limit cycles, and even chaotic behavior (Arruda, 2003), (Goncalves, 2001) and (Johansson et al, 1998), which complicates and sometimes invalidates the relay experiment as a means to determine the ultimate quantities of the process. Safeguards against these phenomena and/or further signal processing to extract the ultimate quantities even in their presence must be provided in PID auto-tuning with relay feedback.…”

“…The most usual applications are based on Ziegler-Nichols and related tuning formulae, in which the tuning is performed based on the identification of one point of the process' frequency response -the ultimate point (Astrom and Hagglund, 1995). More sophisticated tuning, providing better robustness and performance, can also be achieved by relay feedback, using different experiments which identify several points of the frequency response (Arruda, 2003), (Johansson et al, 1998), (Arruda et al, 2002), (Goncalves, 2001). Such methods have shown to be quite effective for many years, and many auto-tuning techniques and commercial products are based on them.…”

Optimal tuning of multivariable disturbance‐observer‐based control for flicker mitigation using individual pitch control of wind turbine

“…Modern control systems are based on state‐space model and have capability to handle multiple‐input multiple‐output system in an efficient manner. Multivariable control algorithm [20, 21] is used to generate demanded collective pitch angle by a combination of output of PI to regulate generator speed and IPC to reduce structural load. Boukhezzar et al [22] used a multivariable control strategy by combining of non‐linear state feedback torque control with a linear blade pitch angle control for the above‐rated power operating condition of wind turbine and overall instantaneous turbine control input is the combination of collective pitch angle and perturbed IPC pitch angle demand input.…”