A programmable logic controller is an industrial solid state monitoring computer system that helps in making logic decisions on the machine and automated processes by monitoring their inputs and outputs levels (Johnson, 2006). Richard Morley invented the model in the late 1960s, and it later got incorporated in the automobile manufacturing industry to replace hard-wired relays since it was easily understandable and was capable of being programmed without much difficulty. Johnson (2006) finds that a program with a programmable logic controller can withstand harsh environmental conditions such as extreme moisture and server temperature conditions. A logic controller is also flexible and can be incorporated into different setups ( Harrison, Vera & Ahmad, 2016 ). Programming languages have seen tremendous modifications and developments since their inception in the 20th century. Each language entails unique structuring and definitions, whose usability varies with the system and the purpose intended. The following languages can be used to program a programmable logic controller.
Ladder Logic Language
Ladder logic language is one of the best visual programming languages. The language is well articulated in a standard referred to as IEC 61131-3 and looks similar to the electrical relay circuits. Ladder logic language is a graphical programming language in that it exhibits graphic elements known as symbols instead of text as it was created for technicians and electricians ( Mizuno, 2016 ). For example, lines in a ladder logic diagram could include opening or closing a switch or turning a motor on or off. Mizuno (2016) argues that ladder logic applies to various industrial setups especially in programming for manufacturing processes and operation. Also, the language is applicable in restructuring old systems into new programmable systems. Moreover, heavily automated systems rely on ladder logic language in instances such as automated factories. The symbols used in ladder logic language are also conventional making it possible for persons without any programming background to program using the language ( Mizuno, 2016 ).
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Function Block Diagram
Function block diagram describes the functions and interrelationship that exist in a
System. As a result, it may include additional schematic symbols to show the representation of particular properties. They help in thoroughly understanding the complex system design, especially from the exterior design the operating system and the relationship that exists between different parts in the operating system (Johnson, 2016). This, therefore, make the language used in a wide range of application especially in software engineering and system engineering. The techniques in functional block diagram can also be useful in the building of software development methodologies. Function block diagram is depicted as a rectangular block where the inputs are entering are placed on the left whereas the inputs exiting are placed on the right ( Harrison, Vera & Ahmad, 2016 ). Here the signal flow is expected to move from the output of one function to the input of another function.
Structured Text Language
Structured text language as compared to ladder logic language and function block
A diagram which is graphically based is text-based. IEC 61131-3 stipulates the provisions and implementation of the language ( Palma, Rosas, Pecorelli & Gil, 2015 ). Structured text language takes a much smaller space. Structured text language flow is easier to read and can be easily understood. It uses a high-level programming language, for example, PHP and Python, therefore, its syntax is developed to look like that of a high-level programming language with loop variables and conditions. The language is composed of written statements separated by semicolons. The statements are made up of variables which are defined by values, inputs, and outputs.
ON-OFF Controller Vs. PID Controller
The on-off controller is considered as the simplest, cheapest and most used controller is available. The controller applies to refrigeration, water tanks, and heating systems for home purposes ( Ulpiani, Borgognoni, Romagnoli & Di Perna, 2016 ). The output signal attains a mazimum value when the known variable drops below the set point, thereby turning the controller on. On the other hand, when the measured variable goes above the set point, then the controller is off, and in return, the output level turns to zero. As a result of mechanical friction due to an electrical contact, the controller may move slightly below the set point or slightly above the set point leading to a differential gap. The temperature must raise beyond the set point for the output turns on or off again and applies mostly in systems which cannot manage on and off turning of energy at close intervals. The mass of the system should also be significant such that the temperature changes slow down. The control may also not be precise.
On the other hand, the proportional and derivative controller is expressed in a time-based unit referred to as reset and rate. The control system provides accurate and stable control as compared to the on and off control. It is usually tuned to a particular system through trial and error by adjusting the proportional, integral and derivative terms ( Ulpiani, Borgognoni, Romagnoli & Di Perna, 2016 ). As compared to on and off control it is best suited to systems with a smaller mass, systems that can react quickly to extreme changes in energy. The controller is useful in systems where the controller has compensated automatically with the dynamic changes in set point, and the load is being changed frequently. This means that the controllers should be able to tune themselves and are referred to as autotune controllers.
Proportional Controller Vs. Proportional-and-Derivative Controller
Proportional controls are generally designed to get rid of cycling which is usually present in the on and off control mechanism ( Yadav, 2015 ). The controls apply mostly in the heater scenario in which the controller minimizes the power generated when the system atains a temperature close to the set point. As a result, the heat will approach the set point to maintain a stable temperature by preventing overshooting of the heater ( Yadav, 2015 ). Proportioning can be established turning the output on and off after shorter periods within a proportional band since outside the band the controller will function as either entirely off or entirely on. Also, the output takes place much longer if the temperature falls below the set point and vice versa. Yadav (2015) argues that in proportional control, the output power and the control error are directly proportional to one another that is the higher the proportion coefficient, the lesser the output power and vice versa at the same control error. It is more applicable to systems with large transmission coefficient that is fast in response. The proportional controller can be adjusted by setting the maximum proportion coefficient wherein the output power it reduces to zero. After the measured value is stabilized a specified value is then set and the proportional coefficient gradually reduced which in turn reduce the control error. If by any chance periodic oscillations arise in the system, then the proportion coefficient should be increased to minimize the limitations of the control error during the oscillation period ( Harrison, Vera & Ahmad, 2016 ).
Conversely, a proportional integral derivative is a combination of proportional and other additional adjustments, in this case, integral and derivative ( Badri & Tavazoei, 2015 ). These two adjustments are vital since they enable the system to compensate spontaneously to changes in the system. The proportional, integral and derivative terms undergo a series of trial and error for them to be turned into a particular system hence providing the most accurate and stable control as compared to proportional control ( Badri & Tavazoei, 2015 ). It is for this reason that modern PID controllers have autotuned function. There are no standard autotuning algorithms. Therefore, each PID manufacturer may use his algorithm resulting in different outcomes upon application. The output power in the PID control equals to the sum of its three coefficient proportional integral and derivative whereby if the proportion is high then the output is less, and if the integration is high then its accumulation becomes slow and finally, if the derivative is high, then disturbance in the system will rise. The technology is mostly applicable in inertial systems which have a low measuring level channel for noise. The PID is more advantageous as compared to the proportional controller in that it has an accurate set point temperature control with fast warm-up time and its fast to while reacting to disturbances.
References
Harrison, R., Vera, D., & Ahmad, B. (2016). Engineering methods and tools for cyber– physical automation systems. Proceedings of the IEEE , 104 (5), 973-985.
Johnson, C. D. (2006 ). Process Control Instrumentation Technology (8 th end). Upper Saddle River, NJ: Pearson/Prentice-Hall.
Mizuno, N. (2016). U.S. Patent Application No. 15/134,995 .
Palma, L. B., Rosas, J. A., Pecorelli, J., & Gil, P. S. (2015, June). Simulation of structured text language for PLC programming. In Experiment@ International Conference (exp. at'15), 2015 3rd (pp. 296-301). IEEE.
Ulpiani, G., Borgognoni, M., Romagnoli, A., & Di Perna, C. (2016). Comparing the performance of on/off, PID and fuzzy controllers applied to the heating system of an energy-efficient building. Energy and Buildings , 116 , 1-17.
Yadav, S. K. (2015). DC motor position control using fuzzy proportional-derivative controllers with different defuzzification methods. IOSR Journal of Electrical and Electronics Engineering (IOSR-JEEE) e-ISSN , 10 (1), 2278-1676.
Badri, V., & Tavazoei, M. S. (2015). Achievable performance region for a fractional-order proportional and derivative motion controller. IEEE Transactions on Industrial Electronics , 62 (11), 7171-7180.
