Prerequisite

None

Overview

This one-day course will cover how to perform effective and time-efficient tuning of single-input single-output PID controllers. The lecture material is supported and reinforced through detailed exercises which are performed using a process simulator and INTUNE PID Tuning Tools software.

This course begins with a review of the fundamentals of process control, moves to a practical treatment of PID algorithms, shows how to identify models for each of your applications and culminates in the use of these models in determining the optimal PID tuning parameters. Along the way, you are exposed to easy-to-use techniques for tuning PID controllers and trained on how to recognize those applications for which PID controller tuning is not necessarily the root cause of substandard process control.

Course Agenda

• The Basics and Language of Process Control
  Process Control is a field of expertise which uses terminology and acronyms which are very specific to control. Concepts such as reverse and directing acting, open-loop vs. closed-loop, and parallel vs. interactive PID control will be reviewed and discussed.
• Setting Goals for Your PID Controllers
  What is most important for your applications? The ability to effectively and quickly reject disturbances? The ability to track setpoint changes as quickly as possible? Minimizing any process variable “overshoot” of the setpoint? An in-depth discussion of how to set your goals and how your goals will impact your controller tuning will be presented.
• The Mathematics Behind PID Control
  What are the P, I, D parameters? How do they work? Are all the PID algorithms offered by the control system vendors the same? If not, how do they differ? The class will go into great detail to investigate the differences between the various “flavors” of PID algorithms on the market today. There will also be a review of the different forms of tuning parameters used by these various algorithms. .
• Identification of Processes in Your Facility / How to Model these Processes
  Is the process you are trying to control an “integrating” process? Or is it a non-integrating process? Does it exhibit a large amount of “deadtime” in its response? A detailed discussion is presented on the attributes of different processes and the techniques used to form a “mathematical model” of these processes.
• Guidelines for Trail-and-Error Tuning of PID Controllers
  If you are unable to perform tests on your process in order to obtain an estimate of its model you may have to resort to the “trial-and-error” method of tuning. Guidelines are presented that will provide a structured framework around trial-and-error tuning giving the user a much greater chance at arriving at “good tuning parameters” in a shorter amount of time when compared to random trial-and-error practices.
• Conversion of Process Models into Optimal PID Tuning Parameters for Controlling the Process
  If you have the ability to perform tests which will facilitate the determination of a mathematical model for your process, then finding the best tuning parameters becomes a straightforward three-step process. A detailed discussion of how to “convert” the model of your process into optimal PID tuning parameters is presented. Also a discussion on how to convert between various PID tuning parameters is presented as well.
• Adaptive Tuning
  The benefits of adaptive tuning and non-intrusive loop diagnostics for different process loops is presented along with an exercise which demonstrates the power and utility of this method of tuning PID controllers.

The post PID Controller Tuning first appeared on ControlSoft.

Prerequisite

PID Controller Tuning

Overview

This one-day course will cover advanced techniques for process control strategies beyond basic PID control. Topics included are cascade control, override control, gain scheduling, feedforward compensation, and model-based control.

Review applications that will require these more advanced strategies, along with common implementation pitfalls that often produce less than desirable results. Attendees will practice implementing each technique against a simulated process and will perform a what-if analysis for each example.

The class is comprised of both classroom instruction (PowerPoint presentations that are provided in hard copy to all attendees) and laboratory work using process simulation examples.

Students will receive copies of all the simulations used in class. Examples will range from basic flow, temperature, pressure and level control, to distillation control, furnace firing control, blending applications, pH control and extrusion control. This list is just a sample of the examples which will be covered during the training.

Course Agenda

• Cascade Control
  Benefits of cascade control; examples of typical applications; Common implementation issues; implementation of reset windup algorithms; simulation and examples.
• Feedforward Control
  Benefits of feedforward control; examples of typical applications; Implementation techniques; simulation and examples which demonstrate how feedforward control can minimize process disturbances.
• Gain Scheduling and Multiple PID
  Discussion and definition of process nonlinearity; How nonlinearity can affect your process; Implementation of gain scheduling; Demonstration of the benefits of gain scheduling.
• Override Control
  Discussion of override control for process safety; Implementation of override control; Discussion of the importance of controller tracking; Simulation.
• Long Deadtime Processes
  Discussion of other common control issues and their solutions; how to deal with deadtime created by process analyzers; how to use laboratory results in your controls.
• Model-based Control
  Definition of model-based control. Review of applications which require model-based control; examples and simulation of how model-based controllers can improve your process control.

The post Advanced Process Control Techniques first appeared on ControlSoft.

Prerequisite

None

Overview

This one-day course will cover how to perform effective and time-efficient tuning of single-input single-output PID controllers. The lecture material is supported and reinforced through detailed exercises which are performed using a process simulator and INTUNE PID Tuning Tools software.

This course begins with a review of the fundamentals of process control, moves to a practical treatment of PID algorithms, shows how to identify models for each of your applications and culminates in the use of these models in determining the optimal PID tuning parameters. Along the way, you are exposed to easy-to-use techniques for tuning PID controllers and trained on how to recognize those applications for which PID controller tuning is not necessarily the root cause of substandard process control.

Course Agenda

• The Basics and Language of Process Control
  Process Control is a field of expertise which uses terminology and acronyms which are very specific to control. Concepts such as reverse and directing acting, open-loop vs. closed-loop, and parallel vs. interactive PID control will be reviewed and discussed.
• Setting Goals for Your PID Controllers
  What is most important for your applications? The ability to effectively and quickly reject disturbances? The ability to track setpoint changes as quickly as possible? Minimizing any process variable “overshoot” of the setpoint? An in-depth discussion of how to set your goals and how your goals will impact your controller tuning will be presented.
• The Mathematics Behind PID Control
  What are the P, I, D parameters? How do they work? Are all the PID algorithms offered by the control system vendors the same? If not, how do they differ? The class will go into great detail to investigate the differences between the various “flavors” of PID algorithms on the market today. There will also be a review of the different forms of tuning parameters used by these various algorithms. .
• Identification of Processes in Your Facility / How to Model these Processes
  Is the process you are trying to control an “integrating” process? Or is it a non-integrating process? Does it exhibit a large amount of “deadtime” in its response? A detailed discussion is presented on the attributes of different processes and the techniques used to form a “mathematical model” of these processes.
• Guidelines for Trail-and-Error Tuning of PID Controllers
  If you are unable to perform tests on your process in order to obtain an estimate of its model you may have to resort to the “trial-and-error” method of tuning. Guidelines are presented that will provide a structured framework around trial-and-error tuning giving the user a much greater chance at arriving at “good tuning parameters” in a shorter amount of time when compared to random trial-and-error practices.
• Conversion of Process Models into Optimal PID Tuning Parameters for Controlling the Process
  If you have the ability to perform tests which will facilitate the determination of a mathematical model for your process, then finding the best tuning parameters becomes a straightforward three-step process. A detailed discussion of how to “convert” the model of your process into optimal PID tuning parameters is presented. Also a discussion on how to convert between various PID tuning parameters is presented as well.
• Adaptive Tuning
  The benefits of adaptive tuning and non-intrusive loop diagnostics for different process loops is presented along with an exercise which demonstrates the power and utility of this method of tuning PID controllers.

The post PID Controller Tuning first appeared on ControlSoft.

Prerequisite

PID Controller Tuning

Overview

This one-day course will cover advanced techniques for process control strategies beyond basic PID control. Topics included are cascade control, override control, gain scheduling, feedforward compensation, and model-based control.

Review applications that will require these more advanced strategies, along with common implementation pitfalls that often produce less than desirable results. Attendees will practice implementing each technique against a simulated process and will perform a what-if analysis for each example.

The class is comprised of both classroom instruction (PowerPoint presentations that are provided in hard copy to all attendees) and laboratory work using process simulation examples.

Students will receive copies of all the simulations used in class. Examples will range from basic flow, temperature, pressure and level control, to distillation control, furnace firing control, blending applications, pH control and extrusion control. This list is just a sample of the examples which will be covered during the training.

Course Agenda

• Cascade Control
  Benefits of cascade control; examples of typical applications; Common implementation issues; implementation of reset windup algorithms; simulation and examples.
• Feedforward Control
  Benefits of feedforward control; examples of typical applications; Implementation techniques; simulation and examples which demonstrate how feedforward control can minimize process disturbances.
• Gain Scheduling and Multiple PID
  Discussion and definition of process nonlinearity; How nonlinearity can affect your process; Implementation of gain scheduling; Demonstration of the benefits of gain scheduling.
• Override Control
  Discussion of override control for process safety; Implementation of override control; Discussion of the importance of controller tracking; Simulation.
• Long Deadtime Processes
  Discussion of other common control issues and their solutions; how to deal with deadtime created by process analyzers; how to use laboratory results in your controls.
• Model-based Control
  Definition of model-based control. Review of applications which require model-based control; examples and simulation of how model-based controllers can improve your process control.

The post Advanced Process Control Techniques first appeared on ControlSoft.

Prerequisite

None

Overview

This one-day course will cover how to perform effective and time-efficient tuning of single-input single-output PID controllers. The lecture material is supported and reinforced through detailed exercises which are performed using a process simulator and INTUNE PID Tuning Tools software.

This course begins with a review of the fundamentals of process control, moves to a practical treatment of PID algorithms, shows how to identify models for each of your applications and culminates in the use of these models in determining the optimal PID tuning parameters. Along the way, you are exposed to easy-to-use techniques for tuning PID controllers and trained on how to recognize those applications for which PID controller tuning is not necessarily the root cause of substandard process control.

Course Agenda

• The Basics and Language of Process Control
  Process Control is a field of expertise which uses terminology and acronyms which are very specific to control. Concepts such as reverse and directing acting, open-loop vs. closed-loop, and parallel vs. interactive PID control will be reviewed and discussed.
• Setting Goals for Your PID Controllers
  What is most important for your applications? The ability to effectively and quickly reject disturbances? The ability to track setpoint changes as quickly as possible? Minimizing any process variable “overshoot” of the setpoint? An in-depth discussion of how to set your goals and how your goals will impact your controller tuning will be presented.
• The Mathematics Behind PID Control
  What are the P, I, D parameters? How do they work? Are all the PID algorithms offered by the control system vendors the same? If not, how do they differ? The class will go into great detail to investigate the differences between the various “flavors” of PID algorithms on the market today. There will also be a review of the different forms of tuning parameters used by these various algorithms. .
• Identification of Processes in Your Facility / How to Model these Processes
  Is the process you are trying to control an “integrating” process? Or is it a non-integrating process? Does it exhibit a large amount of “deadtime” in its response? A detailed discussion is presented on the attributes of different processes and the techniques used to form a “mathematical model” of these processes.
• Guidelines for Trail-and-Error Tuning of PID Controllers
  If you are unable to perform tests on your process in order to obtain an estimate of its model you may have to resort to the “trial-and-error” method of tuning. Guidelines are presented that will provide a structured framework around trial-and-error tuning giving the user a much greater chance at arriving at “good tuning parameters” in a shorter amount of time when compared to random trial-and-error practices.
• Conversion of Process Models into Optimal PID Tuning Parameters for Controlling the Process
  If you have the ability to perform tests which will facilitate the determination of a mathematical model for your process, then finding the best tuning parameters becomes a straightforward three-step process. A detailed discussion of how to “convert” the model of your process into optimal PID tuning parameters is presented. Also a discussion on how to convert between various PID tuning parameters is presented as well.
• Adaptive Tuning
  The benefits of adaptive tuning and non-intrusive loop diagnostics for different process loops is presented along with an exercise which demonstrates the power and utility of this method of tuning PID controllers.

The post PID Controller Tuning first appeared on ControlSoft.

Prerequisite

PID Controller Tuning

Overview

This one-day course will cover advanced techniques for process control strategies beyond basic PID control. Topics included are cascade control, override control, gain scheduling, feedforward compensation, and model-based control.

Review applications that will require these more advanced strategies, along with common implementation pitfalls that often produce less than desirable results. Attendees will practice implementing each technique against a simulated process and will perform a what-if analysis for each example.

The class is comprised of both classroom instruction (PowerPoint presentations that are provided in hard copy to all attendees) and laboratory work using process simulation examples.

Students will receive copies of all the simulations used in class. Examples will range from basic flow, temperature, pressure and level control, to distillation control, furnace firing control, blending applications, pH control and extrusion control. This list is just a sample of the examples which will be covered during the training.

Course Agenda

• Cascade Control
  Benefits of cascade control; examples of typical applications; Common implementation issues; implementation of reset windup algorithms; simulation and examples.
• Feedforward Control
  Benefits of feedforward control; examples of typical applications; Implementation techniques; simulation and examples which demonstrate how feedforward control can minimize process disturbances.
• Gain Scheduling and Multiple PID
  Discussion and definition of process nonlinearity; How nonlinearity can affect your process; Implementation of gain scheduling; Demonstration of the benefits of gain scheduling.
• Override Control
  Discussion of override control for process safety; Implementation of override control; Discussion of the importance of controller tracking; Simulation.
• Long Deadtime Processes
  Discussion of other common control issues and their solutions; how to deal with deadtime created by process analyzers; how to use laboratory results in your controls.
• Model-based Control
  Definition of model-based control. Review of applications which require model-based control; examples and simulation of how model-based controllers can improve your process control.

The post Advanced Process Control Techniques first appeared on ControlSoft.

Prerequisite

PID Controller Tuning & APC Techniques

Overview

This one-day course will cover model-based control theory and practice, including the design, tuning, and evaluation of applications best suited for model-based control.

Using the provided software, students will simulate and build control strategies for the following difficult applications: Long Deadtime Processes, Multi-OutputControl and Control of Interacting Processes The results can be seen and evaluated instantly.

• Several advanced control loops will be simulated and demonstrated using software-based process simulations.
• Hands-on simulations and exercises let you practice these techniques in real-time simulation.

Course Agenda

1. Model Based Control Overview
2. Smith Predictor
3. Internal Model Control (IMC)
4. Coordinated Control (CC)
5. Modular Multivariable Control (MMC)
6. Predictive Control

The post Model-based Control first appeared on ControlSoft.