Hybrid intelligent system means a software system that utilizes a combination of different approaches and techniques from artificial intelligence subfields in order to optimize the target system. Field orient system FOS is a nonlinear device due to the saturation phenomenon, conventional FOS methods provide poor performance, and limited disturbance rejection capability and longer convergence time delay.to optimize the FOS. This paper describes a simple hybrid intelligent design of a “Fuzzy Inference system (FIS)” with PD (FIS-PD) intelligent that is suitable for real-time applications and uses novel output membership functions, which do not include overlap. This increases optimization of rate response convergence, allows for a significant reduction in computational problem, and lowers the execution time. Application of FIS-PD intelligent increases the quality factor of field orient system as results to optimize field oriented system FOS for saturated nonlinear drive in order to achieve high dynamic performance and wide operating range. The performance of the proposed optimization is validated via simulation and the simulation investigation has been carried out for the FOS in this paper. A relative comparison between the FOS and the proposed FIS-PD intelligent indicates that the proposed optimization method yields superior performance. Comparative tests with a conventional system are provided to confirm the effectiveness of the proposed technique

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International Journal of Civil Engineering and Technology (IJCIET)
Volume 9, Issue 11, November 2018, pp. 70–77, Article ID: IJCIET_09_11_007
Available online at http://www.iaeme.com/ijciet/issues.asp?JType=IJCIET&VType=9&IType=11
ISSN Print: 0976-6308 and ISSN Online: 0976-6316
© IAEME Publication Scopus Indexed
FUZZY INFERENCE SYSTEM AND PD
CONTROLLER HYBRIDIZATION FOR FIELD
ORIENT SYSTEM OPTIMIZATION
Saffa Jasim Mosa
Department of computer science, Faculty of education for girls, University of kufa, Iraq,
Eman A.Gani
Department of computer science, Faculty of education for girls, University of kufa,
Ahmed Abdulrudah Abbass
Department of computer science, Faculty of education for girls, University of kufa,
Mohammed Hasan Abdulameer
Department of computer science, Faculty of education for girls, University of kufa, Iraq,
ABSTRACT
Hybrid intelligent system means a software system that utilizes a combination of
different approaches and techniques from artificial intelligence subfields in order to
optimize the target system. Field orient system FOS is a nonlinear device due to the
saturation phenomenon, conventional FOS methods provide poor performance, and
limited disturbance rejection capability and longer convergence time delay.to optimize
the FOS. This paper describes a simple hybrid intelligent design of a “Fuzzy Inference
system (FIS)” with PD (FIS-PD) intelligent that is suitable for real-time applications
and uses novel output membership functions, which do not include overlap. This
increases optimization of rate response convergence, allows for a significant reduction
in computational problem, and lowers the execution time. Application of FIS-PD
intelligent increases the quality factor of field orient system as results to optimize field
oriented system FOS for saturated nonlinear drive in order to achieve high dynamic
performance and wide operating range. The performance of the proposed optimization
is validated via simulation and the simulation investigation has been carried out for the
FOS in this paper. A relative comparison between the FOS and the proposed FIS-PD
intelligent indicates that the proposed optimization method yields superior
performance. Comparative tests with a conventional system are provided to confirm
the effectiveness of the proposed technique.
Keywords: Hybrid Artificial Intelligence FIS-PD, Fuzzy Inference System, PD
Controller, Matlab Simulink

2. Saffa Jasim Mosa, Eman A.Gani, Ahmed Abdulrudah Abbass and Mohammed Hasan
Abdulameer
http://www.iaeme.com/IJCIET/index.asp 71 editor@iaeme.com
Cite this Article: Saffa Jasim Mosa, Eman A.Gani, Ahmed Abdulrudah Abbass and
Mohammed Hasan Abdulameer, Fuzzy Inference System and Pd Controller
Hybridization for Field Orient System Optimization, International Journal of Civil
Engineering and Technology, 9(11), 2018, pp. 70–77
http://www.iaeme.com/IJCIET/issues.asp?JType=IJCIET&VType=9&IType=11
1. INTRODUCTION
These The FISs systems (Fuzzy inference systems) are introduced to take advantage of the
expert knowledge of human in monitoring various systems, for the nonlinear systems [1].
Though, the tuning process of FIS to attain membership functions (MFs) and ideal rules is
considered as disappointing exercise and time consuming [2]. Several methods have been
stated to systematize the tuning method of FISs and ANFIS technique was introduced for that
purpose [3] [24]. Therefore, to learn the mapping between the inputs and outputs, an adaptive
neural network (NN) was employed and a fuzzy system Sugeno-type can be created depends
on the neural network. To learn the data space of a “Tagaki-Sugeno-Kang (TSK)” fuzzy
controller, a quantum NN was used [3]. Recently, a hybridization learning method is developed
which is used particle swarm optimisation technique (PSO) to train the anterior part of an
ANFIS whereas an extended Kalman Filter (EKF) is utilised to train the subsequent part of
ANFIS [4].
The ANFIS technique is used as a system identifier. In addition, the evolutionary
computation algorithms like the popular genetic algorithm (GA) and PSO are also distinctive
candidates in the design of fuzzy controller. The genetic algorithm has been employed in the
fuzzy controllers design in areas of mobile robotics where it is applied in the tuning process of
both the fuzzy rule bases and the fuzzy MFs [5, 6]. Because of PSO is simple to implement, it
has acquired publicity in various engineering applications, such as in digital image processing
applications [9, 23] and in system modelling [10]. Moreover, several publications had been
utilized PSO as an automatic method for tuning the FLC parameters, see for example, [11–
15].These methods were concentrating on tuning the membership parameters that are included
in the “TS-type fuzzy controllers”. Generally, the PSO method is exploited to accomplish the
learning functions which are typically related with the NN in the TS-FLC. Furthermore, a
tuning method fuzzy MF based on PSO is presented to deal with the problem of fixed point
control, i.e. car parking in a predetermined garage locations [16]. In another work, a
combination between micro-GA and PSO is introduced by [17] to optimize the parameters of
FLC. In [17], there were two cases have been studied where the FLC was first supposed to have
a specific number of membership functions whereas the second case is deal with the MFs
setting to be tuneable via the proposed evolutionary computation methods.
In [18], and based on multi-objective optimal functions, the PSO method is used to tune
the FLC to reach finest active control result for a multi-floor building. Although there are
research results in the area of automatic fuzzy MF tuning [2], there is no report on using PSO
for the Mamdani-type of fuzzy tuning that involves the modification of not only the MFs but
the fuzzy rule structure too.

3. Fuzzy Inference System and Pd Controller Hybridization for Field Orient System
Optimization
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2. RELATED WORKS
In the fuzzy inference system, fourty nine fuzzy rules have been utilized and the centre of
gravity approach was used for the defuzzification process. The FIS employs mamdani system
that uses the fuzzy sets in subsequent part. The PID controller picks its parameters depends on
trial and error process. The PID and FIS are examined with the help of “MATLAB /
SIMULINK package program simulation”. The investigation process has founded that the FIS
is more complex in the design when it is compared with the PID controller. However, it has
advantages that make it more appropriate to satisfy the non-linear system that has non-linear
characteristics. Nevertheless, this method is very slow because it has fourty nine rules in the
design of FIS [19].
For preserving the dissolved oxygen (DO) at a desired value, fuzzy logic-based model has
been introduced by [20]. In their work, water temperature is included as one of the input
parameters because dissolved oxygen level is highly influenced by temperature. Though, the
system couldn’t avert overheating because of running time of the system is not measured
precisely. A hybrid system of an adaptive approach and fuzzy logic parameter tuning (AFLPT)
in order to managing the energy in high order nonlinear system is presented. In order to assuring
performance in various driving circumstances, the FLPT is combined with the adaptive system.
The system is an adaptive to various driving circumstances which involved normal, and
regenerative, and Specifically, the power flow between the fuel cell (FC) and the Li-ion battery
is optimize in real time to preserve necessary level while satisfying the fuzzy dynamic
restrictions. The major shortcoming of that method is the hybrid system has a sluggishness
response to improve the high order nonlinear system and it based on try and error for selecting
the fuzzy rules [21]. While numerous articles have shown that the interval type-2 fuzzy
inference system (IT2 FISs) is superior to type-1 FLCs in the general performance, the
computational cost of IT2 FISs is very high, and that in the real world hard to develop.
Therefore, (IT2 FISs) are optimized using genetic algorithms [22]. In this paper, and to
optimize the target of FOS, FIS-PD intelligent is proposed to choose the optimum number of
rules and the rules structure as well. The PD controller is used along with FIS to enhance the
response and to decrease the steady state error. This FIS-PD system is to demonstrate the
performing tracking optimization and to optimize the nonlinear system based on field orient
system.
3. MATERIALS AND METHODS
The proposed hybrid intelligent FIS-PD system has many benefits over the traditional nonlinear
FOS system: it is inexpensive to improve, it has a wider variety of working conditions, and it
is more willingly to customize. A self-establishing FIS can automatically improve an original
estimated set of fuzzy rules. The classical nonlinear FOS system has been conventionally
passed out using the large time delay due to the feedback. FIS delivers adaptive enhancing
capability to the system to better describe for realizing high accuracy. However the iteration
nature of FIS, and try with error make the system very slow for saturated nonlinear device.
Therefore, in this method, a new FIS-PD intelligent is applied on a nonlinear FOS and
developed to find optimal rules for system without the need for any recurrent process for fast
response of whole system... The input to hybrid FIS-PD system are input desired signal and
actual output. In addition, the output of hybrid FIS-PD system is shown in
equation 1.
(1)

4. Saffa Jasim Mosa, Eman A.Gani, Ahmed Abdulrudah Abbass and Mohammed Hasan
Abdulameer
http://www.iaeme.com/IJCIET/index.asp 73 editor@iaeme.com
Where is the error between the desired and actual value, are the
proportional and derivative gain respectively, is the degree of membership function,
and is the fuzzy output weight value. The Simulink of proposed hybrid FIS-PD intelligent
system is shown in figure 1.
Figure 1. Simulink of Proposed Hybrid FIS-PD Intelligent
4. SIMULATION RESULTS
Simulation examinations were approved on FOS using both a conventional and hybrid FIS-PD
intelligent. The steady state responses of the proposed had been detected under conditions, such
as fast variation in step revolution as shown in figure 2. From figure 2, the response of proposed
hybrid FIS-PD intelligent is better than conventional FOS.
Figure 2 Comparison of steady state response
The comparison of command response of system for both FOS and FIS-PD intelligent is
shown in figure 3, On the other hand, when the same test is repeated, only the FOS performance
response shows high rippling while the hybrid FIS-PD intelligent remains the same with free
of distortion.

5. Fuzzy Inference System and Pd Controller Hybridization for Field Orient System
Optimization
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Figure 3 Comparison of speed response
High fluctuating occurs in the FOS performance response compared to the smooth
performance obtained by FIS-PD intelligent, which further has a lower rise time as shown in
figure 4. Therefore, the proposed hybrid intelligent performs better than conventional system
for the above reasons.
Figure 4 Comparison results of Oscillation
From figure 5, it can be seen that the output signal for the conventional FOS and FIS-PD
intelligent are similar in their ability to track system. Therefore, the output signal based on FOS
has high overshoot with high ripple. In contrast, 4the FOS based on hybrid FIS-PD intelligent
has low overshoot, and fast response.
Figure 5 Comparison the output signal of FOS
The tracking of signal via conventional FOS is slow with low steady state. However, the
FOS based on proposed method provides optimal signal with fast response and free of ripple
as shown in figure 5.

6. Saffa Jasim Mosa, Eman A.Gani, Ahmed Abdulrudah Abbass and Mohammed Hasan
Abdulameer
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Figure 5. The tracking of output signal
5. CONCLUSION
Before the A sequence of simulation results and a relative study between the conventional FOS
and proposed hybrid FOS-PD intelligent algorithm had confirmed the authority of the novel
FIS-PD construction. Simulation results exhibit that the new algorithm has the ability to
optimize the FOS performance. Based on virtual results, the hybrid FIS-PD system is
recognized to be tough for FOS applications. Furthermore, the simplified FIS-PD intelligent
arrangement system delivers a important decrease in computational time, can be used for FOS
optimization, create adequately fast and precise output signal tracking because of rapid
convergence degrees. Finally, results showed that the FOS based on hybrid FIS-PD intelligent
are more effectiveness with high optimization. Therefore, simulation test results are given to
verify the validity of this method.
REFERENCES
[1] Lin, C.T., Lee, C.S.G, Neural fuzzy systems. Prentice Hall, Inc., Upper Saddle River, 1996.
[2] Fang, G., Kwok, N.M., Ha, Q.P, Automatic Fuzzy Membership Function Tuning Using the
Particle Swarm Optimisation, In: IEEE Pacific-Asia Workshop on Computational
Intelligence and Industrial Application (PACIIA 2008), Wuhan, China, vol. 2, pp. 324,
2008.
[3] Jang, J.S.R., “ANFIS: Adaptive-Network-Based Fuzzy Inference System. IEEE
Transactions on Systems, Man and Cybernetics 23(3), 665-685, 1993.
[4] Shoorehdeli, M.A., Teshnehlab, M., Sedigh, A.K., Training ANFIS as an identifier with
intelligent hybrid stable learning Algorithm based on particle swarm optimization and
extended Kalman filter. Fuzzy Sets and Systems (160), 922-948, 2009.
[5] Pratihar, D.K., Deb, K., Ghosh, A, A genetic-fuzzy approach for mobile robot navigation
among moving obstacles, International Journal of Approximate Reasoning 20(2), 145–172,
1999.
[6] Mucientes, M., Moreno, D.L., Bugarin, A., Barro, S, Design of a fuzzy controller in mobile
robotics using genetic algorithms, Applied Soft Computing 7, 540-546, 2007.
[7] Kennedy, J., Eberhart, R, Particle swarm optimization, IEEE International Conference on
Neural Networks., Perth, Australia, pp. 1942-1948, 1995.

7. Fuzzy Inference System and Pd Controller Hybridization for Field Orient System
Optimization
http://www.iaeme.com/IJCIET/index.asp 76 editor@iaeme.com
[8] Clerc, M., Kennedy, J, The particle swarm - explosion, stability, and convergence in a
multidimensional complex space, IEEE Transactions on Evolutionary Computation 6(1),
58-73, 2002.
[9] Kwok, N.M., Ha, Q.P., Liu, D.K., Fang, G, Contrast enhancement and intensity
preservation for gray-level images using multiobjective particle swarm optimization, IEEE
Trans. Automation Science and Engineering 6(1), 145-155, 2009.
[10] Kwok, N.M., Ha, Q.P., Nguyen, T.H., Li, J., Samali, B, A novel hysteretic model for
magneto rheological fluid dampers and parameter identification using particle swarm
optimization, Sensors & Actuators: A. Physical 132 (2), 441-451 , 2006.
[11] Karakuzu, C, Fuzzy controller training using particle swarm optimization for nonlinear
system control, ISA Transactions 47, 229-239, 2008.
[12] Kadhim Naief Kadhim (Geospatial Technology for Ground Water Quality Parameters
Assessment in Dhi-Qar Governorateiraq by Using (GIS). Volume 9, Issue 1, January
2018,pp. 358–370, Article ID: IJCIET_09_01_035
[13] Niu, B., Zhu, Y., He, X., Shen, H, A multi-swarm optimizer based fuzzy modeling approach
for dynamic systems processing, Neurocomputing 71, 1436–1448, 2008.
[14] Mukherjee, V., Ghoshal, S.P, Intelligent particle swarm optimized fuzzy PID controller
for AVR system, Electric Power Systems Research 77, 1689-1698, 2007.
[15] Lin, C., Hong, S, The design of neuro-fuzzy networks using particle swarm optimisation
and recursive singular value decomposition, Neurocomputing 71, 271-310, 2007.
[16] Esmin, A.A.A., Lambert-Torres, G, Fitting fuzzy membership functions using hybrid
particle swarm optimization, In: Proceedings on 2006 IEEE International Conference on
Fuzzy Systems, Vancouver, BC, Canada, pp. 2112-2119, 2006.
[17] Ali, S.F., Ramaswamy, A, Optimal fuzzy logic controller for MDOF structural systems
using evolutionary algorithms, Engineering Applications of Artificial Intelligence (22),
407-419, 2009.
[18] Rao, A.R.M., Sivasubramanian, K, Multi-objective optimal design of fuzzy logic controller
using a self configurable swarm intelligence algorithm, Computers and Structures (86),
2141-2154, 2008.
[19] Yasser Ali Almatheel Ahmed Abdelrahman, Speed Control of DC Motor Using Fuzzy
Logic Controller, International Conference on Communication, Control, Computing and
Electronics Engineering (ICCCCEE), Khartoum, Sudan , 2017.
[20] Triya Haiyunnisa ; Hilman Syaeful Alam ; Taufik Ibnu Salim, Design and implementation
of fuzzy logic control system for water quality control, Automation, Cognitive Science,
Optics, Micro Electro-Mechanical System, and Information Technology (ICACOMIT),
2nd International Conference on Indonesia, 2017.
[21] Jian Chen ; Chenfeng Xu ; Chengshuai Wu ; Weihua Xu, Adaptive Fuzzy Logic Control
of Fuel-Cell-Battery Hybrid Systems for Electric Vehicles, IEEE Transactions on Industrial
Informatics,14 (1), Jan, 2018.

8. Saffa Jasim Mosa, Eman A.Gani, Ahmed Abdulrudah Abbass and Mohammed Hasan
Abdulameer
http://www.iaeme.com/IJCIET/index.asp 77 editor@iaeme.com
[22] Zhikang Song, Jian Huang, Dongrui Wu: Performance comparison of efficient type-
reduction approaches for interval type-2 fuzzy logic control. Chinese Automation Congress
(CAC), 2017.
[23] Mohammed Hasan Abdulameer, Siti Norul Huda Sheikh Abdullah, Zulaiha Ali Othman,
Support Vector Machine Based on Adaptive Acceleration Particle Swarm Optimization,
The Scientific World Journal, vol. 2014, Article ID 835607, 8 pages, 2014.
[24] Sari, N. R., W. F. Mahmudy, and A. P. Wibawa. Inflation Rate Forecasting through
Adaptive Neuro Fuzzy Inference System, JNIT (Journal of Next Generation Information
Technology), 8(1), 11-19, 2017.