Fractional order controller robust to time delay variations for water distribution in an irrigation main canal pool

Feliu-Batlle, Vicente; Rivas-Pérez, Raul; Castillo-García, Fernando J.

doi:10.1016/j.compag.2009.08.005

Cited by 66 publications

(21 citation statements)

References 37 publications

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“…• For higher order systems, the performance of classical PID controller deteriorates, whereas fractional PID controller provides better results [25,26] . • For a system with long time delay, a fractional PID controller provides better results than classical PID controller [27][28][29][30] . • A classical PID controller provides lower robust stability [18] , whereas fractional PID controller has more robust and stable [1,31,32] .…”

Section: Fractional Pid Controllermentioning

confidence: 99%

Review of fractional PID controller

Shah

Agashe²

2016

Mechatronics

599

285

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Section: Fractional Pid Controllermentioning

confidence: 99%

Review of fractional PID controller

Shah

Agashe²

2016

Mechatronics

599

285

View full text Add to dashboard Cite

“…A good tuning rule should be capable of taking these uncertainties into account while also maintaining the control performance. In order to test the inherent robustness of these optimal PID/FOPID tuning rules simulations are carried out for variation in process dcgain ( ), maximum time constant ( , [52]- [53], time constant [54]- [56] and time delay [57]- [58] and improvement in control performance has also been shown. Similar to the mentioned literatures ±10% variation in dc-gain [52], ±20% variation in dominant time constant [54] and ±50% variation in time-delay [57] has been done with the controller parameters reported in Table 5.…”

Section: Effect Of Plant Perturbation On the Tuning Rulesmentioning

confidence: 99%

Improved model reduction and tuning of fractional-order PIλDμ controllers for analytical rule extraction with genetic programming

et al. 2012

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Abstract:Genetic Algorithm (GA) has been used in this paper for a new approach of sub-optimal model reduction in the Nyquist plane and optimal time domain tuning of PID and fractional order (FO) PI λ D μ controllers. Simulation studies show that the Nyquist based new model reduction technique outperforms the conventional H 2 norm based reduced parameter modeling technique. With the tuned controller parameters and reduced order model parameter data-set, optimum tuning rules have been developed with a test-bench of higher order processes via Genetic Programming (GP). The GP performs a symbolic regression on the reduced process parameters to evolve a tuning rule which provides the best analytical expression to map the data. The tuning rules are developed for a minimum time domain integral performance index described by weighted sum of error index and controller effort. From the reported Pareto optimal front of GP based optimal rule extraction technique a trade-off can be made between the complexity of the tuning formulae and the control performance. The efficacy of the single-gene and multi-gene GP based tuning rules has been compared with original GA based control performance for the PID and PI λ D μ controllers, handling four different class of representative higher order processes. These rules are very useful for process control engineers as they inherit the power of the GA based tuning methodology, but can be easily calculated without the requirement for running the computationally intensive GA every time. Three dimensional plots of the required variation in PID/FOPID controller parameters with reduced process parameters have been shown as a guideline for the operator. Parametric robustness of the reported GP based tuning rules has also been shown with credible simulation examples.

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“…Recently, to improve performance and especially the stability robustness, an extension of the PID controller has been proposed: it's the fractional PID (PI α D β ) [1] which has two extra-parameters that leads to a more flexible structure. Different design methods are proposed since the development of the first control approach using the fractional PID controller [1], [2], [3], [4], [5], [6]. Most of these methods are used to design a fractional controller for a First Order Plus Dead Time (FOPDT) system [7], [8], [6], [9], [10], [11] or for fractional order system [12].…”

Section: Introductionmentioning

confidence: 99%

Fractional PI design for time delay systems based on min-max optimization

Saidi

Amairi

Najar

et al. 2014

ICFDA'14 International Conference on Fractional Differentiation and Its Applications 2014

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This paper presents a new design method of a fractional order PI (FO-PI) for time delay systems based on the min-max numerical optimization. The proposed method uses a constrained optimization algorithm to determine the unknown parameters of the controller and has an objective to improve the transient response, stability margin, stability robustness and load disturbance rejection. A simulation example is presented to show the effectiveness of the proposed design method for a First Order Plus Dead Time system (FOPDT).

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Fractional order controller robust to time delay variations for water distribution in an irrigation main canal pool

Cited by 66 publications

References 37 publications

Review of fractional PID controller

Review of fractional PID controller

Improved model reduction and tuning of fractional-order PIλDμ controllers for analytical rule extraction with genetic programming

Fractional PI design for time delay systems based on min-max optimization

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