Engineering And Instrumentation In Modern Industry Computer Science Essay


Control systems in industries simply refers to regulating industrial processes to maintain the desired output regardless of all external influences.

It applies the concept of control theory where a control loop system consisting basically of a sensor and a controller manipulates variables. The controller analyses variables sensed by the sensor, compares it with a reference and sends the result to a control system which takes an action with the aim of maintaining the reference value.

Instrumentation is using instruments to measure processes, converts the measured quantities into signals and sends it to a remote system which then makes adjustment to the process.

With control system engineering, an operator can monitor his process from the office/control room.

This paper discusses control system engineering, PID controllers, feedback systems, instrumentation and the aims of control engineering in industries with references provided at the end of the paper.

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Keywords: PID Controllers, feedback, Control loops.


The application of Control system engineering in industrial processes is fundamental to the successful operation of modern industry. Advanced manufacturing, processing and transportation arrangement are to a great extent dependent on robot-like control systems. The gains of these robot-like control systems are consistency in operation, greater efficiency,

reduction in operating cost as a result of improved utilization, safety in the process and ultimately reduction in the requirement of manpower.

Control systems engineering is a discipline in engineering that applies the concept of control theory to design systems that will operate in a predictable manner. It is mostly based on the principle of feedback, where the signal to be controlled is compared to a desired reference signal and the discrepancy is used to compute corrective control action[4].

The aim of control system engineering is to provide an environment where our processes can be monitored and controlled to improve productively, quality, reduce accident and to meet demands.

Control system engineering uses tools such as the feedback loop system, controllers and other instruments to achieve the goal of efficient monitoring of processes in the production industries.

A control system is basically made up of a controller and the plant (process) being monitored. The controller can either be a person, in which case is a manual control system. Alternatively, in an automatic control system the controller is a device, electronic circuit. The interface between the plant and the controller calls for a mechanism to provide the control action as shown in the figure below.





Fig. 1 A Control System showing the controller, the plant and the interface between the controller and the plant.[4]

II. Control theory

Control theory is an interdisciplinary branch of engineering and mathematics[1], that influences the behaviour of dynamical systems. The desired output of a system is called the reference[1] or the set-point. When one or more output variables of a system need to follow a certain reference over time, a controller manipulates the inputs to a system to obtain the desired effect on the output of the system[1].

The engineering aspect deals with the technical know-how and implementing the mathematical knowledge in the design of systems and processes to realize the desired aim while the mathematical aspect deals with the study of quantities such as speed, temperature, pressure e.t.c., and how they are manipulated in the design of a process to give the desired system result.

For example, if we consider a speed control system which controls the speed of a car automatically, that is, it is designed to maintain a constant speed as set by the driver. The constant speed in this case is the desired speed while the system represents the car. The system output is the speed and the control variable is the position of the accelerator (throttle) which influences the output.

In the early days, the way this speed control is implemented is simply to shut the position of the throttle when the driver engages or starts the speed control. However, on mountain surfaces, the car will slow down going up the slope and accelerates going down the slope. Any parameter different from what was assumed at design time will translate into a proportional error in the output velocity, including exact mass of the vehicle, wind resistance, and tire pressure. This type of[1] control system is called an open-loop control system because there is no direct connection between the output of the system (the vehicle's speed) and the actual conditions encountered; that is, the system does not compensate for unexpected forces[1].

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In the case of a closed-loop control system, a sensor is incorporated to monitor the output (the speed) and sends the data to a computer which unceasingly adjusts the control input (the throttle) as essential to keep the error to a barest minimum (that is, to maintain the desired speed). Feedback on how the system is actually performing allows the controller (vehicle's on board computer) to compensate for disturbances to the system, such as changes in slope of the ground or wind speed. An ideal feedback control system cancels out all errors, effectively producing a response in the system that perfectly matches the user's wish[1].

In other to quash the problems of open-loop control system, control theory introduces what is called a feedback. A feedback system is what a closed-loop system uses to control the state and output of a system or process.

In most modern systems, the closed and open-loop control are used at the same time. But in such systems, the open-loop control acts as the feed-forward and serves to improve the tracking performance of the reference. Example of a closed-loop control is the PID controller.

III. transfer functioN OF A Close-loop

The figure below represents a system with a reference value, a controller, the process or system, a sensor measurement and an output. The output is fed back via the sensor to the reference value. The controller then uses the measured error, which is the difference between the reference and the output to change the[1] system inputs to the process under control. This kind of controller is referred to as closed-loop controller or feedback controller.

Fig. 2: Feedback loop concept of a control system[1].


Feedback control system is defined as an action in which part of the output of a process is returned to its input in order to influence its further output.

In the feedback control system, the part of the system that we are interested in controlling is called the plant or the process. Input signals are applied and desired output signal is produced. This is as shown in the figure below.


Input Output

Fig. 3: A Simple Feedback System[5]

A control system designer seeks to design a feedback system that will ensure that a process operates as required. And one way of achieving this is to incorporate other components such as a controller to produce the desired behaviour of the process.


Feedback is characterized based on its response to a process.

Positive Feedback: If a feedback signal amplifies an input signal thereby modifying it further, such a feedback is referred to as positive feedback.

Negative Feedback: Negative feedback is when the output of a process tends to counterbalance the input of the system. It acts to reduce the difference between the output signal and input signal by comparing them. If the feedback is negative, the system will be stable.

Fig. 4: Ideal feedback model. The feedback is negative if B < 0[6]


A controller is a device that is used to monitor and control (effect changes to) the operational condition of a process. It is the most important component in control system engineering.

The operational conditions are referred to as output variables of the system which can be affected by adjusting certain input variables.

For example, the heating system of a house can be equipped with a thermostat (controller) for sensing air temperature (output variable) which can turn on or off a furnace or heater when the air temperature becomes too low or too high.

In this example, the thermostat is the controller and directs the activities of the heater. The heater is the processor that warms the air inside the house to the desired temperature (set point). The air temperature reading inside the house is the feedback. And finally, the house is the environment in which the heating system operates.[6]


Proportional Integral Derivative (PID) controllers are feedback control system mechanism that are widely used in industrial control systems. They compute the difference between a process value and the desired reference or set-point. The difference is known as the error value. The aim of the controller here is to minimize and if possible eliminate the error by adjusting the process input conditions.

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The PID controller computations algorithm are of three parameters.




The proportional value determines the reaction to the current error, the integral value determines the reaction which is based on the sum of recent errors, and the derivative value determines the reaction based on the rate at which the error has been changing. The[2] sum of the three activities is then used to manipulate the process through a control element such as a control valve.

By tuning the three constant in the PID controller algorithm, the controller[2] provides specific requirements actions for which it was designed. The reaction of the controller is accounted for by the way the controller responds to an error, the level to which the controller overshoots the set point value and the degree at which the system oscillates. This does not guarantee system stability.

Fig. 5: A block diagram of a PID controller[2]


A good example of a control loop system is regulating a valve. Assuming we want to maintain a tap which runs hot and cold water at a certain temperature. This would involve the mixture of the hot and cold water which acts as the process, the person measures the temperature of the water intermittently by touching it. Based on what he measures (feedback) he seeks to execute a control action to adjust either the hot or cold water valve until the desired temperature is achieved.

The measured value is known as the Process Value (PV), the desired temperature is known as the set-point value (SP), the position of the water valve acts as the input to the process and it is known as the Manipulated Value (MV). The error (e) represents the difference between the set-point value (SV) and the measured process value (PV). The magnitude of the error quantifies whether the water is too hot or cold.

After the process is measured and error calculated, the controller takes action by deciding when to change the valve position and by how much (this is dependent on the value of the error). When the valve is turned on by the controller, it turns the hot water valve slimly if warm water is desired or it opens the valve fully if hot water is desired. This is an example of the proportional control. But in the case where the hot water does not come quickly, the controller tries to hasten the process by opening the hot water valve more. This is an example of Integral control. If the controller uses only the proportional and integral control methods, the temperature may oscillate between cold and hot. But if we need to achieve a steady and gradual convergence at the set-point (SP) temperature, the controller may wish to damp the expected oscillation in the future. A compensation for this effect is for the controller to normalize their adjustments. This is known as a derivative control method. If a change made is too large when the error is relatively small, it is said to be a high gain controller and this will lead to an overshoot and causes the output to oscillate around the set-point. If the rate of oscillation increases, the system is said to be unstable otherwise it is stable.[3]

Theoretically, a controller may be used to control any process that has a measurable output (PV), an output value that is known (SP) and an input to the process.

Controllers are generally used in industries to regulate process parameters such as temperature, pressure, speed and flow rate. Their simplicity, maintenance requirements and simple set-up makes them a good choice for many applications.


The acronym PID is named after the three correcting terms and the sum output of these three correcting terms forms the manipulating value (MV). Thus,

MV = Pout + Iout + Dout


Pout, Iout, and Dout represents the contributions to the output from a PID controller from each of the three terms as[2] explained below.


The proportional term referred also as the gain makes a change in the output that is proportional to the current error value. The relative response can be adjusted by multiplying the error by a constant Kp, called the proportional gain.

The proportional term is given by:[2]

P out = Kp e(t)

Where Pout: proportional term of output.

Kp: Proportional gain.

e: Error = SP â€" PV

t: Time.

If the proportional gain is high it will result to a large change in the output for a given difference in the error. But if it is too high, the system becomes unstable. In contrast, when the gain is small, it will result to a small output response to a large input error and a less responsive controller. But when it is too low, the control action may be too small when responding to system disturbances.[2]

Fig. 6: Plot of PV vs time, for three values of Kp (Ki and Kd held constant)[2]


The integral term is sometimes referred to as the reset. It is proportional to the magnitude of the error and time of the error. The combined error is then multiplied by the integral gain and added to the output of the controller. The overall control action of the integral term is determined by the magnitude of the integral gain, K. The integral is defined as:


Iout: output of the integral term.

Ki: Integral gain

e: Error = SP âˆ' PV

t: Time o

Ï„: a dummy integration value.

If the integral term is added to that of the proportional term, the process movement towards the set-point is increased and the residual steady-state error which occurs with a proportional controllers are eliminated.

Fig.7: Plot of PV vs time, for three values of Ki (Kp and Kd held constant)[2]


The rate of change of the process error[2] can be calculated by determining the gradient of the error over time. This rate of change is then multiplied by the derived gain Kd. The derivative gain Kd is the magnitude of the derivative term which is sometimes referred to as the rate. The derivative term is represented as:


Dout: Output of the derivative term.

Kd: Derivative gain

e: Error = SP â€" PV

t: Time

The derivative term retards the rate at which the controller output changes. It is detectable near the controller set-point. Thus, the magnitude of overshoot is reduced using the derivative control and hence improve the process stability.

Fig. 8: Plot of PV vs time, for three values of Kd (Kp and Ki held constant)[2]

In summary, the proportional, integral, and derivative terms are added together to estimate the output of the PID controller. If we represent the controller output as U(t), the PID algorithm will look thus:

where the parameters tuned are:

Proportional gain, Kp

If the values are large, it results to faster response because when the error is large, the proportional term compensation will be large. If the gain is too large, the process will be unstable.

Integral gain, Ki

If the values are large, it means that steady state errors are eradicated quickly.

Derivative gain, Kd

If the values are large, the over shoot decreases but reduces the transient response and thus leads to instability of the process due to amplification of noise signal in the differentiation of the error.


Instrumentation is an arm of engineering that handles the aspect of measurement in control engineering. It uses instruments to observe, measure and control industrial processes.

Instruments are devices that measures physical quantities such as flow-rate, temperature, pressure, humidity. These instruments are embedded with gadgets such as valves, transmitters, displays.

It should be noted that the main aim of instrumentation is process control; and this is achieved by using control instrumentation devices such as valves, circuit breakers and relays. Field parameters could can be changed by using these devices and providing automated control capabilities.

Instrumentation in industries is very significant in the area of information gathering from the field and changing the field parameters. This makes it a key part in control loop.


The importance of control and instrumentation engineering in modern industries can never be over emphasized. Below are list of highlighted areas why control system is important.

OVERVIEW OF THE PLANT: Control system engineering provides an overview of the whole plant, and this eases troubleshooting.

PRODUCT MAXIMIZATION: Every industry wishes to maximize profit and this can be achieved if productivity is maximized.

QUALITY: Controlling your process ensures that quality goods are produced as every production stage is being monitored and corrective measures being taken at each stage of production.

SAFETY: Safety is very important to any industry. Be it safety to employees or safety to equipments. In control engineering safety gadgets such as circuit

breakers are put in place to trip every machine when there is an overload to prevent injurious accidents.


Given the challenges of profit maximization, wastes reduction, meeting industrial standards, and operating a system that works round the clock with little or no supervision; control systems engineering will continue to play an important role in modern industrial environment.





[4] Jack Golten and Andy Vermer, Control System Design and Simulation, McGraw-Hill Publishing, YEAR/page

[5] Gene H. Hostetter, Clement J. Savant Jr., and Raymond T. Stefanni, Design of Feedback Control Systems, Sanders College Publishing, 2nd Edition.