Design of an Intelligent Temperature Control System Based on the Fuzzy Self-Tuning PID

Jiang, Wei; Jiang, Xuchu

doi:10.1016/j.proeng.2012.08.053

Cited by 43 publications

(20 citation statements)

References 2 publications

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“…Applications like temperature process control of an electric heating furnace, resistance furnace temperature system requires to maintain the proper temperature. Mathematically, the transfer function of these types of systems is represented by a first-order system [16].…”

Section: Pid Controller and Performance Indicesmentioning

confidence: 99%

“…So, the example of Temperature control system is considered which is approximated by a first order plus time delay system is given as [16]: PID Controller is a feedback controller widely used in G 3 (s) = 80e 20s industrial control systems [17]. It consists of three important parameters (proportional, integral and differential gains) 150s + 1 (10) which are tuned appropriately to satisfy the desired system A. Tuning of PID by MOSA specifications.…”

Section: Pid Controller and Performance Indicesmentioning

confidence: 99%

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Designing of Multi-objective Simulated Annealing Algorithm tuned PID controller for a temperature control system

Bansal

Jain

Bhushan

2014

2014 6th IEEE Power India International Conference (PIICON)

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In spite of new advanced control techniques have been developed, Proportional-Integral-Derivative (PID) controllers are still popular in industrial control systems. But tuning of parameters for these controllers is an important matter of concern. This paper presents an intelligent approach to tune PID parameters using Multi-objective Simulated Annealing Algorithm (MOSA) and the performance of MOSA tuned PID controller is compared with single objective Simulated Annealing Algorithm (SA) tuned PID as well as with the conventionally tuned PID controller. Performance analysis for indexes like ISE, IAE and IATE is also observed. Here, the aim is to show the flexibility in PID tuning using MOSA. To show the effectiveness of the proposed method, simulation results on a temperature control system are analyzed and compared with another very popular intelligent technique of PID tuning which is Multiobjective Genetic Algorithm (MOGA).

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Section: Pid Controller and Performance Indicesmentioning

confidence: 99%

Section: Pid Controller and Performance Indicesmentioning

confidence: 99%

Designing of Multi-objective Simulated Annealing Algorithm tuned PID controller for a temperature control system

Bansal

Jain

Bhushan

2014

2014 6th IEEE Power India International Conference (PIICON)

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“…In Hanchevici and Dumitrache (2012), the genetic algorithm is used to optimize PID controller parameters. Further, a fuzzy adaptive PID algorithm is proposed (Dettori et al, 2018; Jiang and Jiang, 2012; Tian et al, 2010; Wang et al, 2018). Although the control performance has been improved greatly by combining these intelligent algorithms, we may encounter the complex calculation and the troublesome problem in selecting proper network, gene population, and fuzzy rules such that these PID control methods are too complicated to be implemented in realistic applications.…”

Section: Introductionmentioning

confidence: 99%

Model-free adaptive PID control for nonlinear discrete-time systems

Zhang

Chi

2020

Transactions of the Institute of Measurement and Control

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This work explores a model-free adaptive PID (MFA-PID) control for nonlinear discrete-time systems with rigorous mathematical analysis under a data-driven framework. An improved compact form dynamic linearization (iCFDL) is proposed to transfer the original nonlinear system into an affined linear data model including a nonlinear residual term. Both a time-difference estimator and a gradient parameter estimator are designed to estimate the nonlinear residual uncertainties and the unknown parameters in the iCFDL model. Subsequently, a novel improved CFDL based MFA-PID (iCFDL-MFA-PID) control is proposed by incorporating these two estimators. The results are extended by the use of improved partial format dynamic linearization (iPFDL) and full format dynamic linearization (iFFDL). The theoretical results are shown using contraction mapping principle-based mathematical analysis, as well as simulations.

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“…And they show that their proposed method is more effective than Cohen-Coon method, ZN method, and Direct Synthesis method. In [8] Wei Jiang and Xuchu Jiang implement a Fuzzy tuned PID for a first order system (temperature controlling) and compared its response with the conventional PID controller. In paper [5] they apply the genetic algorithm based PID controller for a gas mixture system and found sharp and prompt control over the conventional PID controllers.…”

Section: Introductionmentioning

confidence: 99%

Optimal PID tuning for controlling the temperature of electric furnace by genetic algorithm

2019

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Temperature is one of the exigent parameters that needs to be controlled in today's industries. Importantly this temperature control should be precise and fast. As the conventional controllers are not optimally tuned, the controller used for controlling the temperature of the electric furnace does not exhibit better performance. Its rise time and settling time is too large as well as it has a sizable amount of overshoot. This paper presents a Genetic Algorithm based PID controller to overcome the low precision, long rise time and settling time of the controller. In this algorithm, Integral of Absolute Error is taken as the object function for minimizing the error. Using this function, the algorithm engenders the optimum value of the gain parameters (K p , K i , K d) for the PID controller. It shows better control over the conventional controllers. As the overshoot, settling time, and rise time are substantially improved, it provides sharp and prompt control over the temperature. This precise and instant control of temperature has a great impact on the food and medicine industries. As the temperature could be controlled precisely and instantly, we can avoid the change/degradation of the physical properties of the materials that are under process.

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Design of an Intelligent Temperature Control System Based on the Fuzzy Self-Tuning PID

Cited by 43 publications

References 2 publications

Designing of Multi-objective Simulated Annealing Algorithm tuned PID controller for a temperature control system

Designing of Multi-objective Simulated Annealing Algorithm tuned PID controller for a temperature control system

Model-free adaptive PID control for nonlinear discrete-time systems

Optimal PID tuning for controlling the temperature of electric furnace by genetic algorithm

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