Saturday, November 28, 2009

Video Lecture Industrial Automation and Control - Signal Conditioning and Data Acquisition Systems

Lecture Series on Industrial Automation and Control by Prof. S. Mukhopadhyay, Department of Electrical Engineering, IIT Kharagpur.

Video Lecture Signal Conditioning

Video Lecture Signal Conditioning (Contd.)

Video Lecture Data Acquisition Systems

Thursday, November 26, 2009

Video Lecture Industrial Automation and Control -Sensor and Measurement

Lecture Series on Industrial Automation and Control by Prof. S. Mukhopadhyay, Dept.of Electrical Engineering, IIT Kharagpur.

Measurement Systems Characteristics Lecture Video

Temperature Measurement Lecture Video

Pressure, Force and Torque Sensors Lecture Video

Motion Sensing Lecture Video

Flow Measurement Lecture Video

Monday, November 23, 2009

Video Lecture Series on Industrial Automation and Control 1

Lecture Series on Industrial Automation and Control by Prof. S. Mukhopadhyay, Dept.of Electrical Engineering, IIT Kharagpur.

Introduction Lecture Video - 1

Architecture of Industrial Automation Systems Lecture Video - 2

Sunday, November 15, 2009



Simulated altitude testing of large aircraft engines is a
very expensive, but essential step in the development and
certification of gas turbines used by commercial airlines. A
significant contributor to the cost of this process is the
time-intensive task of manually tuning the facility control
system that regulates the simulated flight condition. Moreover,
control system tuning must be performed each time
the test conductor changes the flight condition. An adaptive
control system that automatically performs this task can
significantly reduce the costs associated with this type of
engine testing.

This paper examines the features of an auto-tuning
controller architecture that contains both disturbance feedforward
and PID feedback components in a two-input, twooutput
multivariable configuration. The paper reviews the
underlying concepts of an auto-tuning system and contrasts
its advantages/disadvantages with respect to other adaptive
control techniques. The algorithm used to automatically
tune the controller does not require a facility model. However,
a nonlinear facility model was developed and used to
substantiate a decoupled-loop design approach, to validate
the controller design concept, and to evaluate the resulting
adaptive control system design performance. This analysis
and other practical design issues that impact the auto-tuning
control system performance are addressed in the paper. The
paper also presents results that illustrate the automatic tuning
sequence and the disturbance rejection performance
exhibited by this system during large engine transients at
several key points in the flight envelope. The auto-tuning
controller described in the paper was implemented at a
Pratt & Whitney flight test facility used in the development
of large, high bypass ratio gas turbines.

Rationale for the Auto Tune Control Concept
Unlike the MRAC and STR concepts, the Auto-Tune adjustment
(adaptation) mechanism does not require any a
priori information about system dynamics to compute the
PID controller parameters. Moreover, an Auto-Tune system
only updates the controller on an operator-demand basis.
The MRAC and STR methods do not explicitly interact
with the system operator. These two characteristics of the
auto-tuning concept were the primary factors in selecting
this adaptive concept for the altitude test facility application.
This section examines the underlying features of the
Auto-Tune concept and motivates the rationale for selecting
a PID controller for this application.

The automatic tuning performed with this scheme can be
characterized as a crude, but robust method that identifies
two key parameters characterizing process dynamics. The
Auto-Tune adaptation algorithm approaches the control
design in a manner quite familiar to first-generation single
input/single output control system designers. The fundamental
idea centers on determining the gain and frequency
at which the system dynamics become conditionally stable
under pure proportional feedback control. These frequencydomain
characteristics of the system are designated as the
ultimate gain and ultimate frequency, respectively. Using
Ziegler-Nichols relationships, the PID controller parameters
can be determined from the ultimate gain and frequency
information. It is well known that PID control systems
designed with the Ziegler-Nichols method exhibit
very good disturbance rejection performance, but tend to
have significant overshoot when responding to set-point
changes (Astrom & Hagglund - 1995). Degraded set point
responses do not present a problem in the altitude test facility
application since the control problem focuses completely
on disturbance rejection performance. The chamber
pressure and plenum pressure set points remain at fixed
values throughout an engine transient test scenario.

As in most control system synthesis problems, both time
and frequency based methods exist for formulating an experiment
that produces the information required to compute
the Ziegler-Nichols gains. In most practical control applications,
a frequency-based experiment produces superior results
and was the method chosen in this application. The
central idea in the frequency-based approach relies on the
fact that most real systems produce stable limit-cycles under
relay feedback. The theoretical basis for this statement
was developed in Astrom - 1991. The method of harmonic
balance or describing function method (Gelb and VanderVelde
– 1968) provides the mathematical framework for
analyzing relay-induced limit-cycles and extracting the
ultimate gain and ultimate frequency from the experimental
On-line PID Controller Design via a Single Auto-tuning Neuron


A simple tuning strategy for PID controller design will be proposed in this paper. With the
use of single neural estimator (SNE), three control gains of PID controller are not fixed during the
control procedure, but will be adjusted on-line such that better output response can be achieved. In
this control strategy the exact model of plant will not need to be known and identified. Lastly, two
simulation results are provided to show the control performance by using the proposed adaptive PID controller.

1. Introduction
2. Preliminaries
2.1 Auto-tuning neuron
2.2 PID controller
3. Self-tuning Adaptive PID
3.1 MIT rule
3.2 Control structure and algorithm
3.2.1 A tuning algorithm for PID control gains
3.2.2 A tuning algorithm for the SNE
4. Illustrative Examples
5. Conclusions

Auto-Tuning of PID Controllers via Extremum Seeking
Abstract—The proportional-integral-derivative (PID) controller
is widely used in the process industry, but to various
degrees of effectiveness because it is sometimes poorly tuned.
The goal of this work is to present a method using extremum
seeking (ES) to tune the PID parameters such that optimal
performance is achieved. ES is a non-model based method
which searches on-line for the parameters which minimize a
cost function; in this case the cost function is representative
of the controllers performance. Furthermore, this method has
the advantage that it can be applied to plants in which
there is no knowledge of the model. We demonstrate the
ES tuning method on a cross section of plants typical of
those found in industrial applications. The PID parameters
are tuned based on the results of step response simulations to
produce a response with minimal settling time and overshoot.
Additionally, we have compared these results to those found
using other tuning methods widely used in industry.

Overall ES PID tuning scheme

Tuesday, November 3, 2009

Auto-Tuning Control Base on Ziegler-Nichols

Auto-Tuning Control Using Ziegler-Nichols
Automatic step tests
One of the earliest auto-tuning controllers still on the market is the 53MC5000 Process Control Station from MicroMod Automation. It uses the Easy-Tune algorithm originally developed at Fischer & Porter (now part of ABB) in the early 1980s. It automatically executes a step test similar to the open-loop Ziegler-Nichols method that forces the controller to make an abrupt change in its control effort while sensor feedback is disabled.

The amount by which the process variable subsequently changes and the time required
for it to reach 63.2% of its final value indicate the steady-state gain and time constant of the process, respectively. If the sensor in the loop happens to be located some distance from the actuator, the process’s response to such a step input may also demonstrate a deadtime between the instant that the step was applied and the instant that the process variable first began to react.

These three model parameters tell the Easy- Tune algorithm everything it needs to know about the behavior of a typical process, allowing it to predict how the process will react to any corrective effort, not just step inputs. That in
turn allows the Easy-Tune algorithm to compute tuning parameters to make the controller compatible with the process.

Closed loop tests
In 1984, Karl Åström and Tore Hägglund of the Lund (Sweden) Institute of Technology
published an improved version of Ziegler and Nichols’ closed-loop tuning method. Like the open-loop method, this technique excites the process to identify its behavior, but without disabling sensor feedback.

The Åström-Hägglund method works by forcing the process variable into a series of
sustained oscillations known as a limit cycle. The controller first applies a step input to the process and holds it at a user-defined value until the process variable passes the setpoint. It then applies a negative step and waits for the process variable to drop back below the setpoint. Repeating this procedure each time the process variable passes the setpoint in either direction forces the process variable to oscillate out of sync with the control effort, but at the same frequency. See the “Relay Test” graphic. The time required to complete a single oscillation is known as the process’s ultimate period (Tu), and the relative amplitude of the two oscillations multiplied by 4/π gives the ultimate gain (Pu). Ziegler and Nichols theorized that these two parameters could be used instead of the steady-state gain, time constant, and deadtime to compute suitable tuning parameters according to their famous tuning equations or tuning rules shown in the equation on the left.

They discovered empirically that these rules generally yield a controller that responds quickly to intentional changes in the setpoint as well as to random disturbances to the process variable. However, a controller thus tuned will also tend to cause overshoot and oscillations in the process variable, so most auto-tuning controllers offer several sets of alternative tuning rules that make the controller less aggressive to varying degrees. An operator typically only has to select the required speed of response (slow, medium, fast), and the controller chooses appropriate rules automatically.

This paper presents an analysis of the Ziegler-Nichols frequency response
method for tuning PI controllers, showing that this method has severe
limitations. The limitations can be overcome by a simple modification for
processes where the time delay is not too short. By a major modification it is
possible to obtain new tuning rules that also cover processes that are lag

2.1 The MIGO design method
2.2 The test batch
2.3 The AMIGOs tuning rules
2.4 Parameterization
3.1 Stable processes
3.2 Integrating processes
3.3 Tuning rules for balanced and
delay-dominated processes
3.4 Summary
4.1 Modified tuning procedures
5.1 Other values of Ms
5.2 How to find the frequency ωφ?
5.3 Summary