Study on Fuzzy Logic and PID Controller for temperature regulation of a system with time delay
10 Apr 2013-pp 274-277
TL;DR: A unique FLC system using direct implementation involving less number of rules is proposed to be developed to solve the problems related to temperature control process based on known and unknown parameters such as dead zone saturation and hysteresis.
Abstract: Temperature plays an important role in determining the efficiency of any industrial process. Taking into consideration the demand for energy efficient and conservative system the industries are looking forward to implement the new systems to control the process. Fuzzy logic and PID are the most recognized approaches taken to effectively control the process. FLC system is gaining momentum as an alternative to replace current Proportional Integral Derivative (PID) controllers, as PID constants are incorrect at times due to the lack of understanding of the temperature control process and its parametric changes making the system complex. Therefore in order to reduce the complexity of the system and to solve the problems related to temperature control process based on known and unknown parameters such as dead zone saturation and hysteresis, a unique FLC system using direct implementation involving less number of rules is proposed to be developed. In this paper we try to establish the performance levels of PID controller and Fuzzy Logic Controllers (FLC) for the same system. FLC is implemented using a smaller rule set and PID Controller is tuned and the parameters are calculated using the Ziegler Nichols method. The study is conducted using the help of MATLAB Simulink Software.
Citations
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01 Jan 2018
TL;DR: In this paper transfer function of a heat exchanger is implemented in MATLAB and is controlled by a PID controller implemented in PLC (S7-1200) and interfaced using Arduino.
Abstract: In this paper transfer function of a heat exchanger is implemented in MATLAB and is controlled by a PID controller. The controller is implemented in PLC (S7-1200). They both are interfaced using Arduino. The advantage of this method is that without having the actual plant, its response can be studied if the transfer function of a system is available. We have used Arduino Uno for interfacing PLC with MATLAB. The response of the system for three set points was noted. This project also shows that even though Arduino can be used for interfacing, the maximum output voltage of 5 V becomes the limitations. Transfer function of Heat exchanger is used in the recovery of soda in a paper pulp industry.
3 citations
01 Jan 2015
TL;DR: In this article, the control of pH in the high pressure-rated environmental continuous stirred tank reactor (CSTR) where deep sea conditions are mimicked is considered, where the pressure inside the environmental CSTR is also kept at an elevated value.
Abstract: In this paper, control of pH in the high-pressure-rated environmental continuous stirred tank reactor (CSTR) where deep sea conditions are mimicked is considered. Since the pressure in deep sea is higher, the pressure inside the environmental CSTR is also kept at an elevated value. Temperature inside the system is considered to be constant. Control of pH inside such a high-pressure-rated environmental CSTR system is a tedious task. First-order plus dead time (FOPDT) model is derived from the real-time system open-loop pH curve. Conventional PI controller and fuzzy logic controller are developed for the control of pH and their performance indices also compared. MATLAB Simulink block is used for controller simulation and result comparison. PI controller is tuned using minimum ISE Zhuang and Atherton method tuning protocols. Simulation result of PI controller and fuzzy logic controller are presented for comparing their efficiency; also various performance indices are calculated, and the best control action is identified. Fuzzy logic controller gives better servo and regulatory response than PI controller.
2 citations
TL;DR: A soft computing based ANFIS method has been proposed to execute the rapid speed response in electric vehicle and the results are evaluated and compared with existing methods like conventional PI and fuzzy based tuned PID controllers.
Abstract: The global concern for clean energy generation paved the way for technological inventions and provided scope for researchers. More prominently, integration of heterogeneous renewable sources, storage systems, and electric vehicles became the pioneer solutions. In this article, a soft computing based ANFIS method has been proposed to execute the rapid speed response in electric vehicle. Here, Brushless DC motor was used as a propulsion system to drive the vehicle. Electric Vehicle is basically a time variant system, whose operating parameters and road conditions vary continuously. To address these uncertainties, a novel control strategy is proposed. The fuel cell battery is used as the auxiliary power supply for the electric vehicle. To demonstrate the performance of the controllers, a case study has been considered with parameter uncertainties for an ECE-15 test cycle. To evaluate the proficiency of the proposed soft computing control method, the speed response results are evaluated and compared with existing methods like conventional PI and fuzzy based tuned PID controllers. In addition, the performance of proposed technique is benchmarked with other controllers reported in the literature. Received: 03/03/2020 Accepted: 11/06/2020
1 citations
TL;DR: In this work, different controllers such as PID, Cascade, IMC controller, Differential Evolution, and Fuzzy Logic Controller have been evaluated and analyzed which controller provides the most linear response of the CSTR.
Abstract: Continuous Stirred Tank Reactor (CSTR) is one of the most important unit operations in chemical industries which exhibits highly non-linear behaviour and usually has wide operating ranges. Chemical reactions in the reactor are exothermic and require that energy can either be removed or added to the reactor to maintain a constant temperature. In this work, different controllers such as PID, Cascade, IMC controller, Differential Evolution, and Fuzzy Logic Controller have been evaluated. The objective is to control the temperature of CSTR in presence of the set point and analyzed which controller provides the most linear response. Model design and simulation are done in the MATLAB/SIMULINK software. The response of the CSTR, which is observed with applying step input and improved by designing conventional and intelligent controllers.
Cites background from "Study on Fuzzy Logic and PID Contro..."
...(2) Energy balance equation – Rate of energy accumulation = rate of energy input – rate of energy output – rate of energy removed by coolant + rate of energy added by exothermic reaction...
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...(3) From the above equation, (1), (2), and (3), it has been known that CSTR system is considered in steady state form....
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References
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TL;DR: Tuning the input and output gains is done here for various range of inputs of the proposed self-tuning fuzzy logic controller (FLC) for a temperature control process.
Abstract: An improvement over the existing conventional fuzzy logic approach, based on a self-tuning fuzzy logic controller (FLC), for the design of a temperature control process, capable of providing optimal performance over the entire operating range of the process, was proposed. Since an optimum response of the FLC can be expected only for a limited range of inputs, tuning the input and output gains is done here for various range of inputs. The proposed control system has the advantages of self-tuning FLC schemes. To evaluate the performance of the proposed control system methods, the results from the simulation of the process are presented.
38 citations
25 Jun 2010
TL;DR: The simulation results show that the validity of the proposed strategy is more effective to control temperature and it's fit for the complicated variable temperature control system.
Abstract: The temperature control system is increasingly playing an important role in industrial production. Recently, lots of researches have been investigated for the temperature control system based on various control strategies. The temperature control system based on fuzzy self-tuning PID is proposed in this paper. The new algorithm based on fuzzy self-tuning PID can improve the performance of the system. Also, it's fit for the complicated variable temperature control system. The simulation results show that the validity of the proposed strategy is more effective to control temperature.
12 citations
09 Jul 2010
TL;DR: Based on real temperature control system, an intelligent control system with PID control and fuzzy control was designed and has some adaptive abilities, strong robustness and stability and can be applied in the system which is difficult to be built as an exact mathematical model.
Abstract: In this paper, the advantages and disadvantages of traditional PID controller and fuzzy logic controller algorithm were analyzed on the control characteristics of electric furnace. According to the intelligent control theory, fuzzy PID (Fuzzy-PID) compound control was selected to realize intelligent control of electric furnace with two-input and two-output system. Based on real temperature control system, an intelligent control system with PID control and fuzzy control was designed. It has some adaptive abilities, strong robustness and stability and can be applied in the system which is difficult to be built as an exact mathematical model.
8 citations