A fractional order fuzzy PID controller for binary distillation column control

Mishra, Puneet; Kumar, Vineet; Rana, K.P.S.

doi:10.1016/j.eswa.2015.07.008

Cited by 90 publications

(27 citation statements)

References 43 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…One of the fundamental tasks in intelligent control is the controller tuning problem (Jiménez et al, 2015;Mishra et al, 2015;Ponce et al, 2015;Sabzi et al, 2016). Such problem consists in finding suitable values for the tuneable parameters of a given control structure.…”

Section: Introductionmentioning

confidence: 97%

Evolutionary multi-objective optimisation with preferences for multivariable PI controller tuning

Reynoso-Meza

Sanchis

Blasco

et al. 2016

Expert Systems with Applications

View full text Add to dashboard Cite

Highlights• We present an evolutionary multiobjective optimisation approach for PI controller tuning.• This approach incorporates designer´s preferences into the optimisation process.• The methodology is evaluated in a multivariable process.• It is possible to improve pertinency of the approximated Pareto front. AbstractMulti-objective optimisation design procedures have shown to be a valuable tool for control engineers. They enable the designer having a close embedment of the tuning process for a wide variety of applications. In such procedures, evolutionary multi-objective optimisation has been extensively used for PI and PID controller tuning; one reason for this is due to their flexibility to include mechanisms in order to enhance convergence and diversity. Although its usability, when dealing with multi-variable processes, the resulting Pareto front approximation might not be useful, due to the number of design objectives stated. That is, a vast region of the objective space might be impractical or useless a priori, due to the strong degradation in some of the design objectives. In this paper preference handling techniques are incorporated into the optimisation process, seeking to improve the pertinency of the approximated Pareto front for multi-variable PI controller tuning. That is, the inclusion of preferences into the optimisation process, in order to seek actively for a pertinent Pareto front approximation. With such approach, it is possible to tune a multi-variable PI controller, fulfilling several design objectives, using previous knowledge from the designer on the expected trade-off performance. This is validated with a well-known benchmark example in multi-variable control. Control tests show the usefulness of the proposed approach when compared with other tuning techniques.

show abstract

Section: Introductionmentioning

confidence: 97%

Evolutionary multi-objective optimisation with preferences for multivariable PI controller tuning

Reynoso-Meza

Sanchis

Blasco

et al. 2016

Expert Systems with Applications

View full text Add to dashboard Cite

show abstract

“…In Taher et al, imperialist competitive algorithm is utilized for tuning parameters of FOPID controller for LFC system. Recently, by combination of FLC and fractional calculus, a novel controller has been introduced which is called FOFPID controller . Therefore, it has attracted scholars to search for practical applications of the proposed control method.…”

Section: Introductionmentioning

confidence: 99%

“…Recently, by combination of FLC and fractional calculus, a novel controller has been introduced which is called FOFPID controller. [43][44][45] Therefore, it has attracted scholars to search for practical applications of the proposed control method. FOFPID using backtracking search algorithm has been implemented for a link planar rigid manipulator system.…”

mentioning

confidence: 99%

A fractional order fuzzy PID for load frequency control of four-area interconnected power system using biogeography-based optimization

Mohammadikia

Aliasghary

2018

Int Trans Electr Energ Syst

View full text Add to dashboard Cite

Summary In this study, a fractional order fuzzy proportional‐integral‐derivative (FOFPID) controller is designed for load frequency control of four‐area interconnected power systems. The model of system consists of three reheat thermal turbine units (Area1, Area2, and Area3) and one hydro power plant (Area4). Therefore, the proposed controller (with same and different parameters) is employed for the first three zones, and different controller parameters are considered for Area4 due to existence of hydro turbine. In order to minimize frequency and power tie‐line deviations in the system, biogeography‐based optimization (BBO) algorithm is utilized to tune controller parameters. Finally, the proposed technique is compared with three different controllers including PID, fuzzy PID (FPID), and fractional order PID. For a fair comparison, the parameters of aforesaid controllers are also tuned by BBO algorithm. The results of different simulation cases indicate that the FOFPID controller has a superior performance and transient response compared with the other approaches against various load disturbances.

show abstract

“…Conventional PID control technology due to its simple principle and adaptable nature has been widely applied in various fields of process control; however, PID is highly ineffective for strongly nonlinear, time varying and control of complex systems with uncertain parameters [3,4,5]. On the other side, SMC has some attractive feature such as a fast response, good transient performance, insensitive to match parameters and disturbances.…”

Section: Introductionmentioning

confidence: 99%