An improved auto-tuning scheme for PI controllers

Mudi, Rajani K.; Dey, Chanchal; Lee, Tsu‐Tian

doi:10.1016/j.isatra.2007.07.002

Cited by 84 publications

(38 citation statements)

References 22 publications

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“…The SISO benchmark consists of concentration control in the isothermal Continuous Stirred Tank Reactor (CSTR) in which a van de Vusse reaction with non-minimum phase behavior takes place. This problem has been used by various researchers including (Mudi et al, 2008;Chang, et al, 2002;Hahn and Edgar, 2001;Edward and Spurgeon, 1998) to evaluate the performance of nonlinear control methods. The benchmark used to address MIMO systems is the concentration and temperature control of a nonisothermal van de Vusse reactor which has also been used to assess performance of nonlinear control methods (Guay et al, 2005).…”

Section: Stopping Criteriamentioning

confidence: 99%

Optimum Setting of Controller Using Soft Computing Techniques for a Chemical System

Glandevadhas¹

2011

American Journal of Applied Sciences

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Problem statement:The aim of this study is to present an intelligent tuning technique for PID controller that are simple and still result in good closed loop behavior. The idea is to start with a tuned conventional PID controller, replace it with an equivalent intelligent controllers like Fuzzy, ANN, Genetic and PSO techniques implies fine tuned nonlinear PID controller which is most suitable for nonlinear process like Continuous stirred tank reactor. The performance of various optimization techniques and intelligent techniques are compared. Approach: In this study we present soft computing techniques to design and tune the PID controller. The objective wss tominimise the steady state error and to obtain the optimum response. Results: The comparisons amoung the Conventional PID, Fuzzy Sliding PID, Simulated Anneling PID and PSO tuned PID controllers PSO PID implies better result for the nonlinear chemical process. Conclusion: With the nonlinear model of CSTR process the PSO tuned PID controller implies the optimum response for both setpoint and load variations.

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Section: Stopping Criteriamentioning

confidence: 99%

Optimum Setting of Controller Using Soft Computing Techniques for a Chemical System

Glandevadhas¹

2011

American Journal of Applied Sciences

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“…However, these methodologies are found to present quite satisfactory results for first-order processes, but they usually fail to provide acceptable performance for higher-order processes and especially for nonlinear ones due to large overshoots and poor regulation on loading (Hang et al, 1991;Mudi et al, 2008). In addition, it has been quite difficult to tune properly the PID parameters, during typical operation plant, due to difficulties related to production goals (Coelho & Pessoa, 2011).…”

Section: Introductionmentioning

confidence: 99%

A Comparative Study using Bio-Inspired Optimization Methods Applied to Controllers Tuning

Souza¹,

Lobato²,

Gedraite³

2012

Frontiers in Advanced Control Systems

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“…Most of PID auto-tuning algorithms have been developed for linear first-or second-order models which are not always possible for practical processes [14]. This restriction to such simplified model classes will invariably result in severe process-model mismatches, implying that only conservative PID tuning rules can be employed [15].…”

Section: Introductionmentioning

confidence: 99%

A novel real-time fuzzy adaptive auto-tuning scheme for cascade PID controllers

Fadaei

Salahshoor

2011

Int. J. Control Autom. Syst.

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This paper presents a novel fuzzy auto-tuning methodology to continuously adapt PID control actions without interrupting the normal process operation. New auto-tuning rules are introduced to schedule a gradual and guided activation of each individual P, I and D control mode in three adjustable functional control zones. An auto-scaling procedure has been incorporated to generalize the autotuning scheme to efficiently respond to any set-point change outside a pre-defined operating span. In contrast to existing auto-tuning algorithms, the proposed scheme is not an on-demand auto-tuning methodology and hence does not require alertness of an experienced engineer to initiate and supervise its initial operation in a separate commissioning identification pre-test. This interesting feature provides a new perspective on PID auto-tuning approaches. Performance of the proposed auto-tuning scheme is practically evaluated in a real pilot plant within a networked control system (NCS) configuration, realized by industrial Ethernet and Foundation Fieldbus technologies. An extensive series of test scenarios has been conducted to explore efficiency of the proposed auto-tuning methodology to cope with fixed and varying operating set-points under uncertain and variable network transmission time delays and external disturbance.

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An improved auto-tuning scheme for PI controllers

Cited by 84 publications

References 22 publications

Optimum Setting of Controller Using Soft Computing Techniques for a Chemical System

Optimum Setting of Controller Using Soft Computing Techniques for a Chemical System

A Comparative Study using Bio-Inspired Optimization Methods Applied to Controllers Tuning

A novel real-time fuzzy adaptive auto-tuning scheme for cascade PID controllers

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