Non linear PID and its application in process control

Zaidner, Guy; Korotkin, Sammy; Shteimberg, E.; Ellenbogen, Amir; Arad, M.; Cohen, Yosef

doi:10.1109/eeei.2010.5662155

Cited by 19 publications

(11 citation statements)

References 3 publications

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“…The most widely used controllers in industry are PID controllers because of their simple structure and satisfactory performance as long as they are properly tuned [3,4]. PID is a linear-type controller and hence is only efficient for a limited operating range when used to control non-linear processes [5]. But most of the industrial processes exhibit nonlinear characteristics and inherit long dead-time such as flow, pH and temperature [5,6].…”

Section: Introductionmentioning

confidence: 99%

“…PID is a linear-type controller and hence is only efficient for a limited operating range when used to control non-linear processes [5]. But most of the industrial processes exhibit nonlinear characteristics and inherit long dead-time such as flow, pH and temperature [5,6]. Moreover, parameters tuning is sensitive and has to be changed with changing process conditions and requires expertise in order to be tuned properly [7].…”

Section: Introductionmentioning

confidence: 99%

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Design of a Fuzzy Logic process Controller for flow applications and implementation in series tanks Pilot Plant

AL-Qutami

Ibrahim

2015

2015 International Conference on Industrial Instrumentation and Control (ICIC)

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Flow control is essential in many industrial applications such as chemical reactors, heat exchangers and distillation columns. Most industrial processes exhibit nonlinearities and inherit dead time, which limit the performance of conventional PID controllers. This Project is about the design and implementation of Fuzzy Logic Controller (FLC) for flow control applications. The objective is to overcome problems inherited with conventional PID control scheme such as handling unpredictable disturbance, non-measurable noise as well as further improve the transient state and steady state response performance. The proposed control scheme is implemented in Flow Control and Calibration Pilot Plant. The design is done using Matlab/Simulink software package and is connected to the Pilot Plant through USB-type DAQ cards. Simulation and implementation results showed that the developed controller has less overshoot, good control performance, better disturbance handling ability, great robustness and is more flexible and intuitive to tune. It is expected that this advanced controller improves efficiency and productivity of industrial processes through proper handling of any disturbance or noise and increase the robustness of controller actions.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Design of a Fuzzy Logic process Controller for flow applications and implementation in series tanks Pilot Plant

AL-Qutami

Ibrahim

2015

2015 International Conference on Industrial Instrumentation and Control (ICIC)

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“…Intelligent control system is consisted of MS51 series chips, possessing strong processing and storage capabilities. At the top layer [4] stands the uppercomputer system.…”

Section: The System Constitutingmentioning

confidence: 99%

The improvement of centralized intelligent control architecture and data collection algorithm

Zhu

Chunjie

2016

J Wireless Com Network

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The commonly used structure of centralized temperature control systems is the bi-layer structure with upper computer directly controlling thermostats. But this kind of structure has obvious drawbacks, such as high cost, inability to scale up, low degree of intelligence, etc.. This paper presents a design scheme of an intelligent three-tier structure, namely thermostats, intelligent logging devices, and upper computer. The upper computer can read data directly from both thermostats and logging devices. This scheme can solve the problems in bi-layer structure well, and it is also suitable for other types of large-scale control system. However, there still exists data loss problem in this scheme when systems reach a great scale. A method of using historical data in the database and linear feature of temperature curves is also proposed in this paper to improve data collection algorithm, which preferably solve this data loss problem. Appropriate products have been developed based on the techniques of this scheme, and have been applied in practical industrial production and experimental teaching.

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“…Definition 1 A memoryless function  : →  is said to belong to the sector [k 1 , k 2 ], if there exist two non-negative numbers k 1 and k 2 (k 2 > k 1 ) such that 12 ,…”

Section: Stability Analysismentioning

confidence: 99%

“…One is the nonlinear PID controller introduced by Korkmaz et al [9] and Isayed [10], whose gains are gradually changed based on error and/or error rate. The other is the nonlinear PID controller introduced by Seraji [11] and Zaidner et al [12], where a nonlinear gain in cascade with a linear PID controller produces the scaled error. These methods presented rather satisfactory results in different control environments, but further improvements are needed.…”

Section: Introductionmentioning

confidence: 99%

Temperature Control of a Regasification System for LNG fuelled Marine Engines

Jin¹,

So²

2018

Eighth International Conference on Advances in Mechanical, Aeronautical and Production Techniques - MAPT 2018

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Abstract-This paper presents a nonlinear PID (NPID) controller which controls the glycol temperature of a regasification system for LNG-fuelled engines. The NPID controller has a parallel structure of linear PD action and nonlinear I action. A nonlinear function is employed to scale the error as input of the integral and implemented as a TakagiSugeno (T-S) fuzzy model. The controller parameters are optimally tuned by assuming that it is operated in both warming-up and running modes, and the parameters are switched according to the operation mode. Furthermore, the stability problem of the overall system is verified based on the circle criterion. A set of simulation works is carried out to validate the efficiency of the proposed controller.

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Non linear PID and its application in process control

Cited by 19 publications

References 3 publications

Design of a Fuzzy Logic process Controller for flow applications and implementation in series tanks Pilot Plant

Design of a Fuzzy Logic process Controller for flow applications and implementation in series tanks Pilot Plant

The improvement of centralized intelligent control architecture and data collection algorithm

Temperature Control of a Regasification System for LNG fuelled Marine Engines

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