AUTOMATION

Dr. Ibrahim Al-Naimi

Chapter four

Industrial Control

Systems

Process and Discrete Industries

• Level of automation.

• Variables and parameters.

Continuous and Discrete

Variables/Parameters

Continuous and Discrete

Control System

Controller Control element

/Actuator

Feedback

sensor

process Input parameter

(set point )

Output

variable

Continuous and Discrete

Control System

Continuous Control Systems

• The objective is to maintain the value of an

output variable at a desired level (feedback

control system).

• Most Continuous processes consist of many

separate feedback loops.

• Examples: – Control the chemical reactions of that depends on

temperature, pressure, and flow rate.

– Control of the position of a work part relative to a cutting

tool (x, y, and z coordinate values).

Categories of Continuous

Control Systems

• Regulatory Control

• Feedforward Control

• Steady State Optimization

• Adaptive Control

Regulatory Control

The objective is to maintain process performance at a certain

level. Compensation action is taken only after a disturbance

has affected the process output.

Feedforward Control

Process

Feedforward

Control element Controller

Performance target level

Index of performance

Output variables

Disturbance

Measured variables

Input parameters

Adjustment to input parameters

• The strategy is to anticipate the effect of disturbances and

compensate for them before they can affect the process.

Steady State (Open Loop)

Optimization Control

(2) Mathematical Model of process and IP

Process

Controller

Input parameters Output variables

Adjustment to input parameters

(1) Index of performance(IP)

Performance measure

(3) Algorithm to determine optimum input parameter values

Steady State (Open Loop)

Optimization Control

• System Characteristics:

– Well defined IP, such as production rate.

– Known relationship between IP and Process

variable.

– The values of the system parameters that optimize

the IP can be determined mathematically.

• When these characteristics apply, the control

algorithm is designed to make adjustment in

the process parameters to drive the process

toward the optimal state.

Steady State (Open Loop)

Optimization Control • Steady state optimal control works

successfully when there are no disturbances

that invalidate the known relationship

between process parameters and process

performance.

Adaptive Control

Performance measure

Process

Modification

Decision

Identification

Input parameters

Adjustment to input parameters

Output variables

Index of performance

Measured variables

Adaptive Controller

Adaptive Control

• Adaptive control combines feedback control and

optimal control by measuring the relevant process

variables during operation and using control

algorithm that attempts to organize some IP.

• Adaptive control has a unique capability to cope

with time varying environment.

• Adaptive control system is designed to compensate

for its changing environment by monitoring its own

performance and altering some aspect of its control

mechanism to achieve optimal performance.

Adaptive Control

• Adaptive control functions:

– Identification.

– Decision.

– Modification.

• Example: Adaptive control machining, in

which changes in process variables, such as

cutting force and power are used to effect

control over process parameters such as

cutting speed and feed rate.

Discrete Control System

• Combinational Logic Control (Event-driven

changes)

• Sequential Control (Time-driven changes)

Computer Process Control

• Control requirements

• Capabilities of computer control

• Forms of computer process control

Control Requirements

• Whether the application involves continuous

control, discrete control, or both, there are

certain basic requirements that tend to be

common for all process control application.

• These requirements are concerned with the

need to communicate and interact with the

process in real time basis.

Control Requirements

• Real time controller is a controller that is able to respond to the process within a short

enough time period that process performance

is not degraded.

• Real time control usually requires the

controller to be capable of multitasking, which

means coping with tasks simultaneously

without the tasks interfering with one other.

Control Requirements

• Process initiated interrupts (Event driven

changes)

Depending on the relative importance of the signals, the

computer may interrupt execution of current program to

service a higher priority need of the process, often triggered

by abnormal condition .

• Timer initiated actions (Time driven changes):

The controller must be capable of executing certain actions

at specified points in time.

• Computer commands and process.

• System and program initiated events.

• Operator initiated events.

Capabilities of Computer Control

• Polling (Data sampling)

• Interlocks.

• Interrupt system.

• Exception handling.

Polling (Data Sampling)

• Polling refers to the periodic sampling of

data that indicates the status of the process.

• The tend is to shorten the cycle time required

for polling

– Polling frequency.

– Polling order.

– Polling format.

Interlocks

• Safeguard mechanism for coordinating the

activities of two or more devices and

preventing one device from interfering with

the other(s).

Interrupt System

• An interrupt system is a computer control feature

that permits the execution of the current program to

be suspended to execute another program or

subroutine in response to an incoming signal

indicating a higher priority event.

• Interrupt conditions:

– Internal interrupts: generated by the computer itself (time)

– External interrupts: process/operator inputs (event)

A higher priority function can interrupt a lower

priority function.

A function at a given priority level cannot interrupt a

function at the same priority level.

Interrupt System

Priority Level ( ranking ) Computer Function / Control Function

1 (Lowest priority ) Most operator inputs

2 System & program interrupts

3 Timer interrupts

4 Commands to process

5 Process interrupts

6 (Highest priority ) Emergency stop ( operator input )

Exception Handling

• An exception is an event that is outside the

normal or desired operation of the process.

• Examples: Production quality problem,

variables outside normal ranges, shortage of

raw materials, hazard conditions, controller

malfunction.

Forms of Computer Process

Control

• Computer Process Monitoring.

• Direct Digital Control (DDC).

• Numerical Control and Robotics.

• Programmable Logic Controllers.

• Supervisory Control.

• Distributed Control System.

• PCs in Process Control.

• Enterprise Wide Integration of Factory Data.

Computer Process Monitoring

Computer Process Monitoring

• Control remains in the hands of humans.

• Categories of data collected by the computer:

1. Process data: input parameters, output variables, …

2. Equipment data: status of the equipment in the work

cell, machine utilization, schedule, tool changes,

diagnosis,…

3. Product data: maybe required by regulations for the firm

own use.

Direct Digital Control (DDC)

Direct Digital Control (DDC)

• Improvement to the DDC system include:

1. More control options than traditional analog,

such as on/off or nonlinear functions.

2. Integration and optimization of multiple

loops. Such as feedback measurements

integration.

3. Ability to edit the control programs, more

flexibility to reprogram, no need for

hardware changes as in analog control.

Numerical Control and Robotics

• Numerical control (NC): a microcomputer

directs a machine tool through a sequence

of steps defined by a program of

instructions.

• Industrial robotics: the joints of the robot

arm are controlled to move the end of the

arm through a sequence of positions

during the work cycle.

Programmable Logic Controller

(PLC)

• Introduced in 1970 as an improvement on the

electromechanical relay controllers used to

implement discrete control.

• A PLC is a microprocessor-based controller

that uses stored instructions to implement

logic, sequencing, timing, counting, etc…for

controlling machines and processes. It is

used for both continuous and discrete control.

Supervisory Control

• It corresponds to cell or system level control

(higher level than NC and PLC)

• It is superimposed on those process-level

control systems (NC and PLC).

• Has economic objectives.

• Could be regulatory control, feedforward

control, or optimal control.

Supervisory Control

Distributed Control Systems

(DCS) • Multiple microcomputers are connected

together to share and distributed the process

control work load.

• Component and features:

Multiple process control stations.

A central control room for supervisory control.

Local operator stations (for redundancy).

Communications network for process and

operator stations interaction.

Distributed Control Systems

(DCS)

Distributed Control Systems

(DCS) • Benefits and advantages of DCSs:

Can be enhanced in the future (after installation).

Parallel multitasking is possible with multiple

computers.

It has built-in redundancy.

Networking facilities plant management.

• Example : Multiple PLC’s through a factory,

connected by network.

PCs in Process Control

Categories of PC implementations in process control :

1. Operator Interface: The PC is interfaced to one or

more PLCs or other devices that directly control the

process.

Advantages :

- The PC is user-friendly.

- It can be used for other functions.

- The failure of the PC does not disrupt the PLCs

functions.

- Can be easily upgraded.

PCs in Process Control

• 2. Direct Control: The PC is interfaced directly to the

process and controls its operations in real time.

• Problems :

- If the PC fails, the process fails.

- PC is not designed for process control.

- PC is designed to be used in an office environment.

• However, There is a trend for PC deployment for

direct control due to several factors :

• Wide spread familiarity with PCs.

• Availability of high performance PCs.

PCs in Process Control

• “Open architecture philosophy” in control systems

design, which means procuring hardware and

software from a diverse pool of vendors; not

getting the whole system from the same supplier.

• Availability of PC operating systems that facilities

real-time control, multitasking and networking.

• Industrial-grade PCs can be used to cope with the

harsh factory environment.

• Data integration is easier using one PC than using

a PC and a PLC.

Enterprise-Wide Integration of

Factory Data: • It entails less management levels and more

empowerment of front line workers.

• Enterprise Resource Planning (ERP) is a

software that achieves company-wide

integration of all business functions, including

factory data.

• A key features of ERP is to use of a singe

central database that is accessible from

anywhere in the company.

Enterprise-Wide Integration of

Factory Data:

Capability resulting from integrating process data:

1. Managers have direct access to factory operations.

2. Production planners have access to most current data on

production to help in scheduling future orders.

3. Sales personnel can provide realistic delivery dates.

4. Customers can track the status of their orders.

5. Quality performance is more predictable.

6. Production cost accounting can be updated.

7. Production personnel have access to product design.