Fractional order IMC based PID controller design using Novel Bat optimization algorithm for TITO Process

Lakshmanaprabu, S.K.; Banu, U. Sabura; Hemavathy, P. R.

doi:10.1016/j.egypro.2017.05.237

Cited by 21 publications

(10 citation statements)

References 16 publications

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“…IMC based tuning of FOPID using analytical method attracted few researchers due to its simplified structure [19]. Instead of tuning five parameters in the FOPID controller, the IMC based FOPID controller design procedure reduce the controller tuning parameter into two such as filter time constant and order of filter [20]. Ranganayakulu R et.…”

Section: Introductionmentioning

confidence: 99%

Internal model controller based PID with fractional filter design for a nonlinear process

Hemavathy¹,

Y²,

Lakshmanaprabu³

2020

IJECE

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In this paper, an Internal model Controller (IMC) based PID with fractional filter for a first order plus time delay process is proposed. The structure of the controller has two parts, one is integer PID controller part cascaded with fractional filter. The proposed controller has two tuning factors λ, filter time constant and a, fractional order of the filter. In this work, the two factors are decided in order to obtain low Integral Time Absolute Error (ITAE). The effectiveness of the proposed controller is studied by considering a non linear (hopper tank) process. The experimental set up is fabricated in the laboratory and then data driven model is developed from the experimental data. The non linear process model is linearised using piecewise linearization and two linear regions are obtained. At each operating point, linear first order plus dead time model is obtained and the controller is designed for the same. To show the practical applicability, the proposed controller is implemented for the proposed experimental laboratory prototype.

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Section: Introductionmentioning

confidence: 99%

Internal model controller based PID with fractional filter design for a nonlinear process

Hemavathy¹,

Y²,

Lakshmanaprabu³

2020

IJECE

Self Cite

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“…Laifa and Boudjehem [16] introduced a new Analytical method to design a decentralized fractional order controllers based on gain and phase margin specifications (GPM) for a TITO system (2x2) with simplified decoupler. Lakshmanaprabu, S.K., et al [17] developed an independent design of multi-loop fractional order internal model control (IMC) based PID (FOIMC-PID) controller for TITO system is presented. The TITO system is decomposed by ideal decoupler to the SISO system and then it is converted into FOPDT model and controller parameters are optimally tuned independently using New Bat Optimization Algorithm (NBOA).…”

Section: Introductionmentioning

confidence: 99%

Analytical Design of Multi-loop Fractional IMC-PID-Filter Controllers for MIMO System Using Equivalent NIOPDT Models

Laifa

Boudjehem

2020

Wseas Transactions on Systems and Control

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In this paper, a multiloop fractional IMC-PID-filter controller design is proposed for 2x2 multivariable systems (two-input two-output (TITO) system). The MIMO system is decomposed by an inverted decoupler into independent loops (SISO systems) and they are approximated to equivalent new fractional order models known as non integer order plus time delay (NIOPTD). The fractional property of the suggested controller is imposed by choosing the Bode’s ideal closed loop transfer function as the reference model for each loop. The design method is based on the internal model control (IMC) paradigm. Finally, an illustrative example of MIMO process is provided and a comparative study is conducted out to demonstrate the advantages of the proposed method where the simulation results show the superior performance obtained by a multi-loop fractional IMC-PID-filter controllers in comparison with fractional PI/PID controllers based on simplified decoupling smith predictor (Fractional-SDSP and Classical-SDSP) structure as well as classical decentralized PID controllers using root locus method.

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“…In what concerns design methods, among the reported, the following can be highlighted: Ziegler-Nichols-type rules [11][12][13][14], optimal tuning [15][16][17][18], tuning for robustness purposes [19][20][21], auto-tuning [22,23], and tuning based on reducing the number of parameters [24]. Likewise, numerous advanced control schemes based on FOPID controllers were proposed, such as, Smith predictors structures [25][26][27], internal mode control [28][29][30], hybrid control [31], gain scheduling [32][33][34], gain and order scheduling [35,36], among others. Current reviews in the development of FOPIDs can be found in [37][38][39][40][41][42]; likewise, few current examples of application in industry are described in [43][44][45][46][47][48][49][50].…”

Section: Introductionmentioning

confidence: 99%

Back to Basics: Meaning of the Parameters of Fractional Order PID Controllers

et al. 2019

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The beauty of the proportional-integral-derivative (PID) algorithm for feedback control is its simplicity and efficiency. Those are the main reasons why PID controller is the most common form of feedback. PID combines the three natural ways of taking into account the error: the actual (proportional), the accumulated (integral), and the predicted (derivative) values; the three gains depend on the magnitude of the error, the time required to eliminate the accumulated error, and the prediction horizon of the error. This paper explores the new meaning of integral and derivative actions, and gains, derived by the consideration of non-integer integration and differentiation orders, i.e., for fractional order PID controllers. The integral term responds with selective memory to the error because of its non-integer order λ , and corresponds to the area of the projection of the error curve onto a plane (it is not the classical area under the error curve). Moreover, for a fractional proportional-integral (PI) controller scheme with automatic reset, both the velocity and the shape of reset can be modified with λ . For its part, the derivative action refers to the predicted future values of the error, but based on different prediction horizons (actually, linear and non-linear extrapolations) depending on the value of the differentiation order, μ . Likewise, in case of a proportional-derivative (PD) structure with a noise filter, the value of μ allows different filtering effects on the error signal to be attained. Similarities and differences between classical and fractional PIDs, as well as illustrative control examples, are given for a best understanding of new possibilities of control with the latter. Examples are given for illustration purposes.

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Fractional order IMC based PID controller design using Novel Bat optimization algorithm for TITO Process

Cited by 21 publications

References 16 publications

Internal model controller based PID with fractional filter design for a nonlinear process

Internal model controller based PID with fractional filter design for a nonlinear process

Analytical Design of Multi-loop Fractional IMC-PID-Filter Controllers for MIMO System Using Equivalent NIOPDT Models

Back to Basics: Meaning of the Parameters of Fractional Order PID Controllers

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