International Journal of Engineering Trends and Technology (IJETT) – Volume 22 Number 9 -April 2015
1
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N.Praneeth
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2
3
1,2,3
Electrical And Electronics Engineering Department, Mother Teresa Institute Of Science And Technology, Khammam,
Abstract: Direct sampled multidimensional systems have been mulled over in numerous applications, most
Telangana and/or unstructureduncertainties, such as mechanical parameter variations, conspicuously in exhibit sign transforming, picture handling, and numerical techniques for unraveling fractional differential mathematical statements. In multidimensional systems optimization is very hard task.
In our approach we implemented genetic algorithm to optimized parameters. We implemented plant model and it
Unknown disturbances, and unknown vibration modes, deterioratethe control performances, which cause the e.g., and – resonant vibrationsand/or the unstable responses. The robust feedback controldesign approaches, based on fitness function to implement the vibration repression. It worked very effectively and we presented graphical representation of the genetic algorithm.
I. INTRODUCTION
Synthesis frameworks,are the promising solutions to realize robustness against theuncertainties [1], [2]. The feed-forward compensator design,on the other hand, should be powerful to achieve the desiredservo characteristics for control command.
Direct sampled multidimensional systems have been mulled over in numerous applications, most conspicuously in exhibit sign transforming, picture handling, and numerical techniques for unraveling fractional differential mathematical statements. Shortly, control applications for systems joining extensive actuator and sensor exhibits are getting to be progressively essential. Hypothesis and utilizations of show sign transforming are entrenched. In the meantime, connected examination ways to deal with control systems with expansive circulated actuator and sensor exhibits are a great deal less created. This paper plans to secure principal vigorous solidness investigation ideas for functional examination of multidimensional systems like existing ideas for multivariable control systems [1,2]. Despite the fact that the primary center and inspiration for this work is in input control applications, it is accepted that the outcomes and ideas can be helpful in other applications.
Although these feedback/feed-forward controllers can attain the desired control performance, the optimal controller design effective controllerdesign. The application of soft computing techniques tothe intelligent controller design should be one of the solution.A wide variety of strategies using, e.g., neural networks, fuzzyinference, and genetic algorithms (GAs) have already beenreported to overcome these problems. essentially requirescomplicated theoretical and numerical procedures. Therefore,an autonomous design for both the structuring and parameterizationof the compensators is expected from the practicalstandpoints of the labor saving and/or the cost
IN high-performance motion control applications, suchas robots, table drive systems, and numericalcontrolledmachine tools, the fast and precise robust speed/positioncontrol characteristics are of critical importance. In suchmechatronic applications, the structured
The degree of freedom of a control system is definedas the number of closed-loop transfer functionsthat can be adjusted independently [1]. The design ofcontrol systems is a multi-objective problem, so atwo-degree-offreedom (abbreviated as 2DOF) controlsystem naturally has advantages over a onedegree-of-freedom (abbreviated
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International Journal of Engineering Trends and Technology (IJETT) – Volume 22 Number 9 -April 2015 as 1DOF) controlsystem. This fact was already stated by
Horowitz [1],but did not attract a general attention from engineersfor a long time. It was only in 1984, two decades afterHorowitz's work, that a research to exploit theadvantages of the 2DOF structure for PID controlsystems was made [2].In [2-4], various 2DOF PID controllers were proposedfor industrial use and detailed analyses weremade including equivalent transformations, interrelationshipwith previously proposed
“advancedtype”PID (i.e., the preceded-derivative PID and theI-PD) controllers, explanations of the effects of the2DOF structure, and a list of optimal parameters.Consequently, the results obtained were adopted byvendors [5-7], and further studies were made aboutoptimal tuning [8-10], methods for digital implementationwith magnitude and/or slope limiters [11], ananti-reset-windup method [11], and other topics arisingin industrial applications [12-14].Most of the above researches were published inJapanese and have not been translated into Englishyet. The purpose of this article is to survey recentresults on 2DOF controllers, so that engineers interestedin this topic can easily exploit the results.
G yr2
=P(s)(C(s)+Cf(s))/1+P(s)C(s)H(s)
G yd2
=P d
(s)/1+P(s)(C(s)H(s)
Here, the subscript “2” means that the quantities areof the
2DOF control system.
It can be shown that the steady-state error to theunit step change of the set-point variable, ε r,step ,and the steadystate error to the unit step disturbance ,ε d,step , become zero robustly if
Lim t

0
C(s)=∞, lim s

0
Cf(s)/C(s)=0
Lim s

0
H(s)=1
Lim s

0
P(s)≠0, lim s

0
Pd(s)/P(s)<∞ imposes conditions on the controller. The simplestcase that satisfies these conditions is the one thatC(s) includes an integrator and Cf (s) does not. requires that the detector is accurate in the steadystate. When this condition is violated, the steady-stateerror given by
II. RELATED WORK
A general form of the 2DOF control system isshown in Fig.1, where the controller consists of two compensators C(s) and Cf (s) , and the transfer function Pd
(s) from the disturbance d to the controlledvariable y is assumed to be different from thetransfer function P(s) from the manipulated variableu to y. C(s) is called the serial (or main) compensator and Cf (s) the feed forward compensator.The closed-loop transfer functions from r to y anddto y are, respectively, given by
E r,step
=H(0)-1/H(0)
Arises, provided that and are satisfied. Are the conditions on the plant, where the first equationrequires that P ( s ) is not of differentiating and thesecond that the disturbance is not integrated moretimes than the manipulated variable?
Strictly speaking,this statement is correct only when the plant is describedby the minimum realization of the transfer matrix [ P ( s ), Pd ( s )]. From the mathematical standpoint,are nothing but sufficient conditionsthat make the steady-state errors zero robustly. Butfrom the industrial viewpoint they can be regarded asnecessary.
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International Journal of Engineering Trends and Technology (IJETT) – Volume 22 Number 9 -April 2015
The responses of the controlled variable y to theunit change of the set-point variable r and to the unitstep disturbance d are called “set-point response” and“disturbance response,” respectively. They have beentraditionally used as measures of the performance intuning the PID controllers. We will use these responsesin our consideration, too, and see how theyare improved as a whole by the introduction of the2DOF structure. Note that these responses are nothingbut the indicial responses of the closed-looptransfer functions Gyr2 (s) and Gyd2 (s) given by above equations. Here, note that Assumptions1 and 2 are adopted so that H(s) of is 1 and Pd (s) is P(s) . The simulation studiescarried out for this section were made assuming thatthe approximate derivative (9) is nearly ideal, i.e., thederivative gain δ was set to 1000.
Since its inception over eighty years ago [1], theproportional-integral-derivative (PID) control structure hasremained the most commonly used single-input singleoutput(SISO) technique in industry, primarily because it is one of thesimplest [2,3]. On the other hand, modern design structuresoften use linear-time-invariant (LTI) models to approximatephysical systems that are time-varying and nonlinear inpractice. The lack of efficient tuning techniques and thegeneral need for the end-user to have an extensive backgroundin control theory prevents model-based techniques from beingfound in industrial control applications. In this paper, the focusis turned from the development of complex control structuresto that of a model-independent control framework andinsightful tuning techniques.Active Disturbance Rejection Control (ADRC) wasoriginally proposed as a nonlinear solution to industrial controlproblems [4-6], but the large number of gains made tuning anart. The structure was simplified to its linear form andparameterized down to a few gains in [7]. It has been appliedto many practical problems [8-11]. As a natural extension ofthe concepts behind ADRC and PID, this paper introduces anew robust two-degree-of-freedom (2DOF) control designtechnique. Parameterization is geared toward the ease oftuning in the industrial environment. sake of clarity, theideas are conveyed using a few useful examples.
III. PROPOSED SYSTEM
At in the first place, the compensator applicants are produced as an beginning populace for "(a)
Compensator Design". The time-premise information of burden position are computed utilizing the plant numerical model including the compensator hopefuls. At that point, the wellness is assessed for the time reaction and for the controller request in "(b) Fitness Evaluation ". After the emphasis of the wellness assessment for all people, another populace for the cutting edge is overhauled by the hereditary operations, i.e., choice, hybrid, and change. The above arrangement of hereditary courses of action is iterated until the predefined number of eras.
The organizing and parameterization for the compensators and are evolutionally transformed as the determination of, relating to the structure. A single person with the expectation of complimentary parameters (and) in the hereditary operations is made out of two chromosomes.




PID is used in over ninety percent of all industrial controlapplications. Although it is simple to use, PID is limited inperformance, particularly when dealing with dynamicuncertainties. The difficulty of tuning is an obstacle inimplementation. Even the widely used pole placementtechnique is not straightforward because PID controllersoperate by adding zero(s) to the loop. Based on
ADRC, thePID structure is reformulated to make it more robust, easier totune, and extendable to nth order. For the
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International Journal of Engineering Trends and Technology (IJETT) – Volume 22 Number 9 -April 2015
One is "basic chromosome" communicated in a
18-bit parallel scalar, which decides the structure of as a mix of first- and/or second-arrange polynomials. Here, every polynomial is known as a "compensator module” and the structure of is dictated by consolidating the compensator modules under the limitation of the compensator orders noted. The other is "parameter chromosome" communicated in (15-bit dim code) (number of parameters) vectors, which recognizes and the parameters of polynomials for the basic chromosome. The hereditary operations are freely handled for the relating chromosomes.
In the implementation of autonomous design, specificationsof the GA process were selected as the population size of 20, thetournament selection with the elitist preserving, the multipointcrossover with its rate of
0.9, and the bit mutation with its rate of0.03. The genetic process was iterated up to 300 generations. Inthe genetic process, from the practical standpoints of the fasterand/or higher convergence, it is indispensable to predeterminethe appropriate solution space to be searched. In the followingexperiments, the variable range of plant model parameter wasset between 0.6–2.6, which corresponded to the same range asof the variation of . The order of the compensatorwas limited up to eighth, while the variable range for those parameterswas set as and , respectively.
The first term of the right-hand side in is the penalty forthe positioning performance by an integral of squired error forthe load position. The second term is for the residual vibrationsuppression performance, where the differentiation of load positionis evaluated after the settling of the motor position command.The third term is for the suppression of torsional anglebetween motor and load during the whole response period. Thefourth term is for the settling time.
K(s)/F(s)=
0.325.w1w2w3w4/(s+w1)(s+w2)(s+w3)(s+w4)
R=2.073
Where
W1=2498 w2=1488 timewithin 120 ms could be attained regardless of the variations ofload inertia. As a result, the fast and precise positioning can beachieved, satisfying the desired control specifications, togetherwith the robust performance.
IV.CONCLUSION
In our approach compensator using genetic algorithm was presented. In this we used structured parameters. By using Genetic algorithm for satisfying the given control on specifications. It increases the robust of the system against the uncertainties. We have done testing for the effectiveness of the system.
REFERENCES
[1] J. C. Doyle, “Analysis of feedback systems with structured uncertainties,”Proc. Inst. Elect. Eng., pt. D, vol.
129, no. 6, pp. 242–250, 1982.
[2] G. J. Balas and J. C. Doyle, “Robustness and performance tradeoffsin control design for flexible structures,” in Proc. CDC’90, 1990, pp.2999–3010.
[3] P. Vas, Artificial-Intelligence-Based Electrical
Machines andDrives. Oxford, U.K.: Oxford Science, 1999.
[4] M. K. Ciliz and M. Tomizuka, “Modeling and compensation of frictionaluncertainties in motion control,” in Proc. ACC’95, 1995, pp. 3269–3273.
[5] T. Iwasaki et al., “Fuzzy auto-tuning for PID controller with model classification,”in Proc. NAFIPS’90, 1990, pp.
90–93.
[6] K. S. Tang, K. F. Man, and D. W. Gu, “Structured genetic algorithm forrobust H control systems design,”
IEEE Trans. Ind. Electron., vol. 43,pp. 575–582, Oct. 1996.
[7] K. Itoh, M. Iwasaki, and N. Matsui, “GA-based practical compensatordesign for a motion control system,”
IEEE/ASME Trans. Mechatron.,vol. 6, pp. 143–148, June
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[8] T. Umeno and Y. Hori, “Robust speed controlof DC servomotors usingmodern two-degrees-of-freedom controller design,” IEEE Trans. Ind.Electron., vol. 38, pp.
363–368, Oct. 1991.
This optimized fourth-order compensator means that K(s) issynthesized as a constant gain which only allows the positionloop gain to be unity.The settling
[9] G. J. Balas, J. C. Doyle, K. Glover, A. Packard, and R.
Smith, -Analysis& Synthesis Toolbox. Minneapolis, MN:
MUSYN Inc., 1991.
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International Journal of Engineering Trends and Technology (IJETT) – Volume 22 Number 9 -April 2015
[10] M. Iwasaki, K. Sakai, and N. Matsui, “High-speed and high-precisiontable positioning system by using mode switching control,” in Proc.IEEE IECON’98, 1998, pp.
1727–1732.
BIOGRAPHIES
Kurapati abhilash , born in Krishna district, India, on march 11,1994. He is pursuing his Bachelor of technology at
Mother Teresa institute of Science and technology,Telangana state. His
Research areas are Switch Gear And
Protection,Electrical Machines ,Control
Systems, Power System Operation And Control.
Kakunuri ranjith kumar , Born in khammam, India. on August 18,1994.
He is pursuing his Bachelor of technology at Mother Teresa institute of Science and technology in,
Telangana state. His Research areas are
Electrical Circuits, Electrical Machines,
Switch Gear And Protection ,Electrical Distribution
Systems, Control Systems, Power System Operation And
Control
N.PRANEETH
, born in khammam,TelanganaState,on june 9,1986.He is working as
Assistant Professor in Mother
Teresa Institute Of Science And
Technology,Telangana State .He has completed his Master Of Technology
In Control Systems Specialisation .His research interests are Power Electronics And Electrical Drives,Optimal
Controlling Technics,Soft Computing Technics For
Mechatronics.
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