What are the steps in control system design?

Darrell Halterman, director, PACSystems controllers, Emerson, explains how to design a modern, flexible and secure machine control system in the June 15 webcast, “Truly open and secure machine control.” Courtesy: Emerson

Learning Objectives

• Automation design should be flexible and secure.
• Open communication improves productivity and enables a wider use of equipment in the design.
• 7 steps can help with machine control system design.

The webcast, “Truly open and secure machine control,” explores how to design a modern, flexible and secure machine control system. In the June 15 webcast, archived for a year, Darrell Halterman, director of PACSystems controls products at Emerson, details seven steps needed to design a modern, flexible and secure machine control system.

Staffing shortages, supply chain issues and ever-increasing cybersecurity incidents have changed the face of production across nearly every industry, Halterman said. Whether they are machine builders creating the newest technologies or end users upgrading existing process equipment, companies are finding traditional design principles and products no longer meet the flexibility, efficiency and security needs of today’s global marketplace. As original equipment manufacturers (OEMs) and project teams look for new control solutions to address these needs, they must wade through a variety of options.

Automation design flexibility

In the webcast, Halterman shares strategies for pursuing flexibility, open communication and defense-in-depth in automation design to help harness best-in-breed control technologies to gain a competitive advantage in the future of production.

The webcast will help those attending:

• Understand why traditional design philosophies and tools will not meet the modern challenges around staffing, supply chain issues and cybersecurity.
• Learn how open communication improves productivity, enabling flexibility to use a wide variety of equipment in the design
• Identify key capabilities in a new control solution to address the full scope of cybersecurity risks while unlocking flexibility in component selection.
• Understand ways and tools to make personnel more efficient at their jobs so you can accomplish more with existing staff (or even less).

7 steps: Machine control system design

Halterman provides a programmable logic controller (PLC) design challenge checklist applicable for machine control systems and other industrial applications.

1. Accommodate supply chain constraints
2. Match the skill sets of a new industrial worker.
3. Reduce risk and protect against cyberattacks.
4. Enhance and simplify OEM/user partnership.
5. Move at the pace of automation evolution.
6. Increase productivity in the face of constrained resources.
7. Fit your existing budget and footprint.

An application overview is included, going over the control system architecture for a filling machine as an example.

– Edited by Mark T. Hoske, content manager, Control Engineering, CFE Media and Technology, mhoske@cfemedia.com, using materials for the June 15, 2022, Emerson webcast.

KEYWORDS: Machine design, PLCs, motion control

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Typically a control system should be designed to work together with an already existing process. The control design problem can be stated at local, supervisory or even plant-wide level.

If we consider the local level, the typical steps in designing the control are:

1. Define the components of the process to be controlled, manipulated, or which are fixed. Additional define which are the variables of interest.
2. Define the user control goals.
3. Get a draft model of the process and the attached signals.
4. Select which will be the manipulated and measured variables.
5. Choose a suitable control structure.
6. Translate into a control language (also suitable for the selected control structure) the user requirements.
7. Apply the controlled design methodology based on the decision taken in the previous steps (variables, models, goals).
8. Validate (by simulation or experimentally) the design and tune the controller parameters.
9. Define the controller implementation. In the case of digital controllers, select the hardware and software to fulfil the control requirements.
10. Install the control in the process.
11. Evaluate the controlled system performances.

Most of these activities should be performed iteratively. For example, if the designed controller does not match the requirements, either the control structure or the variables should be revisited. If the implementation of the controller introduces additional constraints like time delays, computation time, etc. the design should be reconsidered, taking into account the new requirements. If the controlled system is driven out of the region of the validation of the model, a new model should be acquired or the uncertainties should be reduced.

Once the user goals have been stated, some decisions should be made to design the controller:

• Which variables are selected as controlled, measured and manipulated and which ones are going to be treated as disturbances.
• Based on this choice, which are the achievable performances?
• To meet the control requirements, which is the most suitable control design approach?

Control Goals

From the set of usually unclear “user” control goals, mainly based on qualitative and economical requirements as well as operational constraints, the desirable controlled plant performances for control design should be derived. They may concern different properties such as:

• Reference tracking, to follow the changes in the set-points or references.
• Control decoupling, to better understand and tune the different sub processes or control variables.
• Disturbance rejection, to cope with non-manipulated external variables.
• Measurement noise rejection, to be able to use “imperfect” sensor and transmission systems.
• Robustness against changes in the plant (model) or expected disturbances.

Control Structures

The choice of the variables to be used as control variables as well as the information used to generate the control actions will determine the control structure.

The control structures to be considered are:

• Open loop vs. closed loop – In an open loop control structure, the control actions are generated based on external information: set-points or objectives, initial conditions, disturbances, operator data, etc. A good model of the process is needed and there is no option to cope with unexpected changes in the plant (either disturbances or plant changes). In contrast, closed loop control uses the information from the plant to generate the control. There are many options for dealing with disturbances, reference tracking and uncertainties. The main drawback of closed loop control is the requirement of the existence of errors to act on. Therefore a combination of both structures may allow better results.
• Single/multiple loop – In feedback controlled multiple-input-multiple-output (MIMO) systems, the vector of the input actions may computed altogether from the full set of measurements and available data or alternatively,  the information is split into blocks to determine each of the control actions. In this case, for each input, the remaining blocks of information can be considered as disturbances. This structure could also be designated as centralized/decentralized control.
• Two degree of freedom – The control action may be computed into two phases. Foremost, the control error is evaluated, and the control is based on the error. This is a feedback control action. Subsequently, an additional control action is computed based on external inputs. There are two degrees of freedom to design the controller and the design can be split to achieve tracking (references) and regulation (output feedback) performances.
• Multi-level control – In this control structure, groups of input (output) variables can be treated jointly to control a process variable. They will act locally, receiving commands (set-points) from the higher decision levels, and sending information back to these coordination levels.

Related: Principles of Control Systems

Performance Analysis

There are several criteria for defining “good nominal performance” in terms of the close-loop transfer matrices:

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• Settling time – it is related to position of the system poles and its internal structure i.e. controllability and observability.
• Overshoot – although this can be checked for particular input/output combinations, it is not used during the design phase.
• Steady-state gain – position errors for reference tracking are established, in most loops by the DC gain of the sensitivity function. For disturbance rejection, the steady-state gain may be determined.
• Bandwidth – many control requirements may be cast in the frequency domain, in particular, ability to track references varying up to maximum rate and ability to reject disturbances whose frequency components are mainly concentrated on a particular band.

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Author:

John Mulindi

John Mulindi is an Industrial Instrumentation & Control Professional with a wide range of experience in electrical and electronics, process measurement, control systems and automation. He writes on technical as well as business related topics. In free time he spends time reading, taking adventure walks and watching football. View all posts by John Mulindi