Optimum design of fractional order PIλDμ controller for AVR system using chaotic ant swarm

Tang, Yinggan; Cui, Mingyong; Chen, Hua; Li, Lixiang; Yang, Yixian

doi:10.1016/j.eswa.2012.01.007

Cited by 181 publications

(108 citation statements)

References 30 publications

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“…The Oustaloup's approximation is based on the value of N u as there is always a conflict between the value of N u and its performance [40,41]. The present method is preferred over others due to the possibilities of implementing it in real time in the form of higher-order infinite impulse response type digital or analog filters [40].…”

Section: Design and Implementation Of Tl-foflc Approachmentioning

confidence: 98%

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Design of two-layered fractional order fuzzy logic controllers applied to robotic manipulator with variable payload

Sharma

Gaur

Mittal

2016

Applied Soft Computing

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Section: Design and Implementation Of Tl-foflc Approachmentioning

confidence: 98%

“…Thus, ı is the order of fractional differentiator; 2N u + 1 represents the order of approximation; w hu, w bu represents the frequency range [40]. The Oustaloup's approximation is based on the value of N u as there is always a conflict between the value of N u and its performance [40,41].…”

Section: Design and Implementation Of Tl-foflc Approachmentioning

confidence: 99%

Design of two-layered fractional order fuzzy logic controllers applied to robotic manipulator with variable payload

Sharma

Gaur

Mittal

2016

Applied Soft Computing

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“…A FOPID is identified by five parameters: a proportional gain, integral gain, derivative gain, integral order, and derivative order. Previous research results in various applications have shown that FOPID controller has an improved performance and robustness compared to conventional PID [9]. …”

Section: Introductionmentioning

confidence: 99%

“…Analytical based methods include; Pole distribution [10], frequency domain approach [11], state-space design [12], two-stage or hybrid approach [13] and piecewise orthogonal functions approach [14]. On the other hand, heuristic methods include; particle swarm optimization (PSO) [15], chaotic ant swarm (CAS) [9] and Genetic algorithm [16]. …”

Section: Introductionmentioning

confidence: 99%

Optimum Design of PIλDμ Controller for an Automatic Voltage Regulator System Using Combinatorial Test Design

et al. 2016

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Combinatorial test design is a plan of test that aims to reduce the amount of test cases systematically by choosing a subset of the test cases based on the combination of input variables. The subset covers all possible combinations of a given strength and hence tries to match the effectiveness of the exhaustive set. This mechanism of reduction has been used successfully in software testing research with t-way testing (where t indicates the interaction strength of combinations). Potentially, other systems may exhibit many similarities with this approach. Hence, it could form an emerging application in different areas of research due to its usefulness. To this end, more recently it has been applied in a few research areas successfully. In this paper, we explore the applicability of combinatorial test design technique for Fractional Order (FO), Proportional-Integral-Derivative (PID) parameter design controller, named as FOPID, for an automatic voltage regulator (AVR) system. Throughout the paper, we justify this new application theoretically and practically through simulations. In addition, we report on first experiments indicating its practical use in this field. We design different algorithms and adapted other strategies to cover all the combinations with an optimum and effective test set. Our findings indicate that combinatorial test design can find the combinations that lead to optimum design. Besides this, we also found that by increasing the strength of combination, we can approach to the optimum design in a way that with only 4-way combinatorial set, we can get the effectiveness of an exhaustive test set. This significantly reduced the number of tests needed and thus leads to an approach that optimizes design of parameters quickly.

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“…A few studies have been done for the application of the FOPID controller to the design of the AVR in a power system. In [6][7], the FOPID parameters are tuned for an AVR system with swarm based optimization algorithms which show better time domain performance over that with conventional PID structure. In [8] a multi-objective formalism has been Manuscript received November 17, 2013; revised February 18, 2014 developed and the time domain performance trade-offs of various design objectives have been investigated.…”

mentioning

confidence: 99%

On the Mixed <inline-formula><tex-math notation="TeX">${{\rm H}_2}/{{\rm H}_\infty }$</tex-math></inline-formula> Loop-Shaping Tradeoffs in Fractional-Order Control of the AVR System

Das

Pan

2014

IEEE Trans. Ind. Inf.

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I. INTRODUCTIONPPLICATION of fractional calculus based control system designs have gained impetus in recent times due to the flexibility and effectiveness that can be gained through such methodologies [1][2]. These controllers use the FO integrodifferential operators as extra tuning knobs which can be tuned to meet additional design constraints and provide additional robustness to the design [3][4]. Merging the concepts of computational intelligence techniques with FO control designs are also being recently explored with encouraging results [5].However applications of fractional order controllers for electrical power and energy systems are still largely unexplored. A few studies have been done for the application of the FOPID controller to the design of the AVR in a power system. In [6][7], the FOPID parameters are tuned for an AVR system with swarm based optimization algorithms which show better time domain performance over that with conventional PID structure. In [8] a multi-objective formalism has been Manuscript received November 17, 2013; revised February 18, 2014 developed and the time domain performance trade-offs of various design objectives have been investigated. The obtained results are mixed, with the PID performing better under some objectives and the FOPID performing better at others. However, all these investigations suffer from a common problem. The objective functions are framed in time domain and the evolutionary algorithms minimize some time domain performance index. Therefore, the obtained results give no indication of other important design focuses like robust stability, disturbance rejection capability etc. To alleviate these issues, the problems in power system control can be framed in frequency domain and the system performance can be expressed in terms of system norms which need to be minimized or maximized. In [9], the FOPID design for an AVR system has been done in frequency domain. The conflicting objectives being maximization of phase margin (for better oscillation damping) and gain crossover frequency (for higher speed of operation) are studied by coupling them with multi-objective evolutionary algorithms. In this paper, we extend this concept to an exhaustive set of frequency domain design criteria for the AVR system and show the achievable set of performances with the FOPID controller structure.The rest of the paper is organized as follows. Section II briefly introduces the theoretical background of the problem with the basics of FO control using system norms. Section III shows the design trade-offs for several combinations of conflicting objectives and compares the best compromise solutions for each controller structure. The paper ends with the conclusion as section IV. II. THEORETICAL BACKGROUND OF FRACTIONAL ORDER CONTROL DESIGN FOR AVR SYSTEM A. Fractional Calculus Based Control SystemsFractional calculus extends the common notion of integer order integration or differentiation to any arbitrary real number. B. AVR System and Controller StructureIn the conventional AVR loop in...

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Optimum design of fractional order PIλDμ controller for AVR system using chaotic ant swarm

Cited by 181 publications

References 30 publications

Design of two-layered fractional order fuzzy logic controllers applied to robotic manipulator with variable payload

Design of two-layered fractional order fuzzy logic controllers applied to robotic manipulator with variable payload

Optimum Design of PIλDμ Controller for an Automatic Voltage Regulator System Using Combinatorial Test Design

On the Mixed <inline-formula><tex-math notation="TeX">${{\rm H}_2}/{{\rm H}_\infty }$</tex-math></inline-formula> Loop-Shaping Tradeoffs in Fractional-Order Control of the AVR System

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