Nonlinear Control

Introduction

Conventional controllers like PID and many advanced control methods are useful to control linear processes. In practice, most processes are nonlinear. If a process is a just little nonlinear, it can be treated as a linear process. If a process is severely nonlinear, it can be extremely difficult to control.

CyboSoft offers an innovative Model-Free Adaptive (MFA) control solution for controlling severely nonlinear processes without having to build process models and retune controller parameters. It also eliminates the need of traditional nonlinear characterization allowing the user to easily configure and launch Nonlinear MFA controllers.

Solution

Why is Nonlinear Control Difficult?

Nonlinear control is one of the biggest challenges in modern control theory. While linear control system theory has been well developed, it is the nonlinear control problems that present the most headaches. Nonlinear processes are difficult to control because there can be so many variations of the nonlinear behavior.

Traditionally, a nonlinear process has to be linearized first before an automatic controller can be effectively applied. This is typically achieved by adding a reverse nonlinear function to compensate for the nonlinear behavior so the overall process input-output relationship becomes somewhat linear. It is usually a tedious job to match the nonlinear curve; and process uncertainties can easily ruin the effort.

Nonlinear MFA vs. PID Controller

The following trends show the Nonlinear MFA controller (top) is able to control a severely nonlinear process throughout the entire operating range (linear and nonlinear). PID works fine in the linear range but fails to control in the nonlinear range.



A flow or pressure loop is a typical nonlinear process that can cause the actuator to lose its authority in different operating conditions. Inevitable wear and tear on a valve can also make a linear valve nonlinear. The variable frequency drive (VFD) has become a popular flow regulator because it saves energy. However because of its nonlinear nature, it adds a nonlinear component to the process.

The dissolved oxygen used to cultivate cells in a bio-tech micro reactor is another example of a nonlinear process. As cells grow, they suddenly start to consume much more oxygen. Since the number of bio-tech experiments is huge and the types of cells to be grown can vary significantly, it is difficult and costly to apply traditional nonlinear-characterization in solving this nonlinear problem.

Summary

Nonlinear MFA can tightly control a nonlinear process within its full control range, even when the process gain changes hundreds of times. In a Nonlinear MFA, there is no linearization calculation or process model. The MFA controller gain is simply set at its nominal point and not retuned.

This general-purpose, powerful solution can be easily implemented. No special knowledge or experience with advanced control, nonlinear characterization, or process modeling is required. Tough nonlinear loops can be controlled resulting in smoother operations, longer actuator life, higher product quality and production efficiency, and reduced waste in energy and material.

Case Studies

To read more about implementations of CyboSoft’s Nonlinear MFA solutions, click on the following case studies:

MFA Control and Optimization of Crude Oil Separators

Model-Free Adaptive Control on Fluidized-Bed Boilers

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