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 only a little nonlinear, it can be treated as a linear process. If a process is severely nonlinear, it can be extremely difficult to control.

CyboSoft, a NI Alliance Partner in Rancho Cordova, California 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.

Leveraging National Instruments LabVIEW Real-Time development environment, the embedded Nonlinear MFA controller can run in a PC or be downloaded into NI's FieldPoint distributed I/O or PXI instruments.

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 Model-Free Adaptive (MFA) Controller

Nonlinear MFA is a general-purpose controller that provides a more uniform solution to nonlinear control problems. It is well suited for controlling nonlinear processes or processes with nonlinear sensors, actuators, and other elements.

Nonlinear MFA vs. PID Controller

The following trends in LabVIEW 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.



Nonlinear MFA Controller Configuration in LabVIEW

Nonlinear MFA has only a few parameters to configure including Controller Gain, Kc, Process Time Constant, and Process Nonlinearity Factor. As shown in the following bitmap, the graph on the menu shows how severe the nonlinear behavior is between the process input and process output.

The Process Linearity Factor is a number between 0 and 10. A 10 represents an extremely non-linear process, and a 0 represents a linear process. Notice that the graph shows a nonlinear curve marked with a 10 on both upper and lower positions. This means, Nonlinear MFA does not care what the nonlinear characteristics are for this process. For instance, the valve can be either "fast open" or "fast close" as represented by these two convex and concave curves.

When using Nonlinear MFA, the specific characteristics of the nonlinear curve are not a concern. The curve can be concave, convex, or S-shaped. Simply advise the controller whether the process is extremely nonlinear (enter a 9 or 10), quite nonlinear (enter a 5 or 6), or somewhat nonlinear (enter a 1 or 2). The Nonlinear MFA is smart enough to handle the rest.



Nonlinear Control Application Examples

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.

Compact FieldPoint Based Nonlinear MFA Control System

National Instruments Compact FieldPoint distributed I/O products provide an ideal hardware platform to implement a nonlinear control system. The following table lists all the components and part numbers required. With both options, at least 2 MFA nonlinear controllers can be implemented. NI's FieldPoint products can be used to implement this system as well. See the "pH Control" solution for detailed product info and part numbers.

Option 1. Remote I/O Option (MFA running in PC)

Option 2. Embedded Real-Time Option. (MFA running inside FieldPoint)

CyboSoft's Embedded MFA Control Software in LabVIEW

The following table lists the required embedded MFA control software to be ordered from CyboSoft. A SISO (single-input-single-output) MFA controller can be configured as one of the following MFA controllers.

• SISO MFA to replace PID to eliminate manual tuning,
• Nonlinear MFA to control nonlinear processes,
• MFA pH controller to control pH processes,
• Feedforward MFA to deal with measurable disturbances,
• Anti-delay MFA to control processes with large time delays, and
• Robust MFA to protect the process variable from running outside certain boundaries.

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, Kc, 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 using NI's FieldPoint and CyboSoft's embedded Nonlinear MFA controllers resulting in smoother operations, longer actuator life, higher product quality and production efficiency, and reduced waste in energy and material.