AUTOMATION Dr Dr. . Ib Ibrahim rahim Al Al-Naimi Naimi - PowerPoint PPT Presentation

AUTOMATION Dr Dr. . Ib Ibrahim rahim Al Al-Naimi 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 Output Control element Input Controller process variable /Actuator parameter (set point ) Feedback sensor

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 • The strategy is to anticipate the effect of disturbances and compensate for them before they can affect the process. Disturbance Input Output variables parameters Process Adjustment to Measured input parameters variables Feedforward Controller Control element Index of performance Performance target level

Steady State (Open Loop) Optimization Control Output variables Performance Input parameters measure Process Adjustment (1) Index of to input performance(IP) parameters Controller (3) Algorithm to (2) Mathematical Model determine optimum input of process and IP 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 Input parameters Output variables Performance measure Process Adjustment to input Modification Measured parameters variables Decision Adaptive Controller Identification Index of performance

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