Research on Nonlinear Automation for First Order ...

ISSN 1738-9968

IJHIT International Journal of Hybrid Information Technology

Vol. 8, No. 9, September 2015

SCIENCE & ENGINEERING RESEARCH SUPPORT SOCIETY

International Journal of Hybrid Information Technology Vol.8, No.9 (2015)

Editor-in-Chief of the IJHIT Journal Ha Jin Hwang, Kazakhstan Institute of Management, Economics and Strategic Research (KIMEP), Kazakhstan

Byeong-ho Kang, University of Tasmania, Australia

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Andrew Kusiak, The University of Iowa, USA

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Bogdan Gabrys, University of Bournemouth, UK

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C.H. Lee, University of Maryland, USA

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Debnath Bhattacharyya, Heritage Inst. of Technology, India

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Dongyu Liu, Cavium Networks, USA

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Elena Zudilova-Seinstra, University of Amsterdam, The Netherlands


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Frank Klawonn, Fachhochschule Braunschweig, Germany

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Kousalya G, Lealta Media, India

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R. Amutha, SSN College of Engineering, India

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Rainer Unland, University of Duisburg-Essen, German

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Randy S. Tolentino, Hannam University, Korea

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Rosslin John Robles, University of San Agustin, Philippines

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S. A. Channiwala, V. Regional College of Engg. and Tech., India

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T. Venkat Narayana Rao, Hyderabad Institute of Technology and Management, India

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Xue-wen Chen, The University of Kansas, USA

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Ying Xie, kennesaw State University, USA

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Yusuke Nojima, Osaka Prefecture University, Japan

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Yuyu Yin, Hangzhou Dianzi University, China

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Ronnie D. Caytiles

International Journal of Hybrid Information Technology Vol. 8, No.9 (2015)

Table of Contents Multiple Evaluation Models for Education Based on Artificial Neural Networks 1 Xiao Qianyin and Liu Bo

Determination of the Optimum Berth Number of the Izmir Seaport by Deterministic and Probabilistic Methods Based on Artificial Neural Networks 11 Ümit Gokkus and Mehmet Sinan Yildirim

Design and Implementation of Embedded Serial Communication Based On Finite State Machine 25 Jian Feng, Yuanyuan Ding

An Adaptive Cellular Genetic Algorithm Based Strategy for Test Sheet Generation

on Selection 33

Ankun Huang, Dongmei Li, Jiajia Hou, Tao Bi

Marker Selection using Skeletonization for very Low Training Sample Analysis of Hyperspectral Image Classification 43 Farid Muhammad Imran and Mingyi He

Study of Proportional Guidance Trajectory based on Guiding Principle 53 Wang Xin, Guo Xin and Yao Jun

The Study of New Virtual Currency-Bitcoin

63

Kai Chain and Yao-Ren Wen

xiii


Design and Research on the LQR Controller for the Magnet Power Supply of the Accelerator based on Particle Swarm Optimization 71 Xin Zhang, Jianwu Dang and Yangping Wang

Optimized Architecture Design of Shared MDS Storage Systems with Hotspot Buffer 81 Peng Hai-Yun and Niu Ling

Harmonic Extension On An LB-Fractal

89

Bhagwati Prasad and Kunti Mishra

Software Testing Case Generation of Ant Colony Optimization based on Quantum Dynamic Self-Adaptation 95 Chen Ping and Xia Min

Wireless Body Sensor Networks: A Review

105

Aarti Sangwan and Partha Pratim Bhattacharya

Combinatorial Test Generation using Improved Harmony Search Algorithm 121 Xiaoan Bao, Shuhan Liu, Na Zhang and Meng Dong

Dielectric Loss Factor Measurement of High Voltage Capacitive Apparatus based on Harmonic Analysis Method 131 Qiang Gao, Baohong Geng, Guangming Zhang, Yadong Liu, Feng Yuan, Gehao Sheng And Xiuchen Jiang

Enriched the Spectrum Sensing Performance of Estimated SNR based Detector in Cognitive Radio Networks 143 Ashish Bagwari, Geetam Singh Tomar

xiv


A New Kmeans Clustering Algorithm For Point Cloud

157

Kun Zhang, Weihong Bi, Xiaoming Zhang, Xinghu Fu, Kunpeng Zhou and Li Zhu

Neuro Inspired Genetic Hybrid Algorithm for Dispatch Planning Problem in Small Scale System

Active Power 171

Navpreet Singh Tung and Prof. (Dr). Sandeep Chakravorty

An Online Learning Model of Mobile User Preference Based on Context Quantification 185 Yancui Shi, Congcong Xiong, Jucheng Yang, Yarui Chen and Jianhua Cao

Design of A National Student Financial Aid System based on Information Integration Technology and Fuzzy Comprehensive Evaluation 203 Hua Ding, Zhongliang Guan and Zhihong Tian

A Fuzzy Logic System to Estimate ARP Poisoning Susceptibility of a Client in Wireless Networks 215 Jaideep Singh and Vinit Grewal

Integrate Metadata by Semantic Recommendation: A Psychology Inspired Method 225 Xixu Fu, Yuan Ren

An Advanced Parallel FPGA Architecture for Bi-Directional Motion Estimation 235 Yangfan Huang, Minjun Deng, Donglian Li, Zhenzhen Li, Mingyan Yu, Cailan Zeng, Yu Zhang, Zhuo Chen, Pengcheng Cao And Ran Liu

xv


A Statistical Analysis Conditioning Application

of

Desiccant

Dehumidifier

for

Air 245

M. Mujahid Rafique

Text Sentiment Classification Based On Mixed Cloud Vector Model Clustering And Kernel Fisher Discriminant 257 Yujuan Xing, Ping Tan

A Credibility-Based Defense SSDF Attacks Scheme Expulsion of Malicious Users in Cognitive Radio

for

the 269

Hong Du, Shuang Fu and Hongna Chu

GA Based Optimal Placement Of SVC For Minimizing Installation Cost And Voltage Deviations 281 Ankita Singh, Shishir Dixit

A Distributed Clustering Algorithm with Optimal Search Model for Wireless Sensor Networks 289 Tao Lü, Zhongbin Cai and Yuyu Zhu

Innovation Mechanism Study Based on Convergence: Implications for IT Enterprises

the

Technological 303 Ye Xu

Research System

on

Nonlinear

Automation

for

First

Order

Delays 313

Ali Roshanzamir, Farzin Piltan, Arman Jahed, Saman Namvarchi, Iman Nazari, Nasri B. Sulaiman

A Curve-Skeleton Extraction Algorithm based on Vector Fields for 3D Model Retrieval 329 Sun Ting, Zhou Wen-Gang and Geng Guo-Hua

xvi


A Dual-Reduct Approach to Generate Core Rules

339

Lv Hanfei and Jiang Congfeng

Study on Mutual Information Medical Image Registration based on Ant Algorithm 353 Luo Hong-Yan

Word Multi-Domain Semantic Polarity Algorithm based on Domain Standard Word Set 361 Yun Sha and Shibo Zhang

Construction Network

and

Application

Of

Effective

Path

Statistics 369

Gang Zhang, Qing-Kui Chen

Support Vector Machine Optimization Based On Magnetic Bacteria Optimization Algorithm 381 Ce Yang, Zhaofeng Chen

Enterprise Internal Training and Performance Evaluation of Network Learning based on Online System 389 Shuxia Wang

xvii


International Journal of Hybrid Information Technology

September 2015 Printed September 2015 Published

Publisher:

SERSC

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Science and Engineering Research Support soCiety 20 Virginia Court, Sandy Bay, Tasmania, Australia E-mail : [email protected]; [email protected] Tel: +61-3-9016-9027

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International Journal of Hybrid Information Technology Vol.8, No.9 (2015), pp.313-328 http://dx.doi.org/10.14257/ijhit.2015.8.9.29

Research on Nonlinear Automation for First Order Delays System 1

1

Ali Roshanzamir, 1Farzin Piltan, 1Arman Jahed, 1Saman Namvarchi, 1Iman Nazari, 1,2Nasri B. Sulaiman

Intelligent Systems and Robotics Lab, Iranian Institute of Advanced Science and Technology (IRAN SSP), Shiraz/Iran 2 Department of Electrical and Electronic Engineering, Faculty of Engineering, University Putra Malaysia, Malaysia Email: [email protected], WWW.IRANSSP.ORG Abstract

First order delay system (FODS) is in class of nonlinear systems. In these systems design control algorithms are very important. In this research nonlinear terms of incremental Proportional Integral Derivative (PID) algorithm is used to nonlinear modelfree integrate large amounts of control methodology in a single methodology. This work, proposes a developed method to design nonlinear based PID controller. In this methodology nonlinear model-free sliding mode algorithm help incremental PID to estimate and linearization of first order delay system. According to this research, the controller robustness improved based on nonlinear term of sliding mode algorithm and the chattering is reduced/eliminate based on PID incremental method. Keywords— First order delays system, PID incremental controller, nonlinear function, sliding mode controller, and model free robust sliding mode algorithm

1. Introduction and Background Design a nonlinear incremental PID controller for first order delay system is the main challenging work in this research. First order delay systems are nonlinear, time variant and uncertain dynamic systems. Complexities of the tasks caused to design and modeling the first order delay architectures with nonlinear behavior. These factors are:  

Time-varying parameters based on type of applications. Simplifying suppositions in system modeling caused to have un-modeled dynamic parameters.  External disturbance and noise measurement which it is caused to generate uncertainties. According to above discussion, first order delay systems are nonlinear uncertain systems, control of these systems are complicated. To control of first order delay systems, two purposes are very important: 

Stability: Stability is due to the proper functioning of the system. A system is called stable if for any bounded input signal the system‟s output will stay bounded. Therefore limitation of output deviation is very important for any design.

ISSN: 1738-9968 IJHIT Copyright ⓒ 2015 SERSC




Robust: Robust method is caused to achieve robust and stable performance in the presence of uncertainty and external disturbance. A system is robust when it would also work well under different conditions and external disturbance.

As a result, design a controller based on these two factors are the main challenge in this work. Based on control theory; controllers for first order delay systems (FODS) are divided into two main collections: Conventional control theory and intelligent control theory where, conventional control theories are work based on nonlinear dynamic parameters of FODS and these are divided into two main categories: Linear control method and nonlinear control method. Intelligent control theory is worked based on intelligent control theory and it is free of nonlinear dynamic parameters of FODS. According to the formulation of FODS, they are uncertain and nonlinear. In linear controller these challenges can be reduced, with the following two methods:  Limiting the performance of the system.  System linearization. Therefore linear types of controller, such as PD, PI or PID controllers have challenge to control of nonlinear systems. Conventional nonlinear control theories are highly sensitive to system‟s behavior and work based on cancelling decoupling and nonlinear terms of dynamic parameters. Sliding Mode Control (SMC) is one of the known nonlinear conventional controller which introduced by many researchers to control of nonlinear systems [1-3]. This controller works very well in certain and partly uncertain condition [1, 14-19]. The main challenge of this controller is equivalent part. Sliding mod controller is a nonlinear model based controller and equivalent part is a dynamic formulation of nonlinear system which is used to control formulation of FODS. However this part is very essential to reliability but in uncertain condition or highly nonlinear dynamic systems it can cause many challenges. However conventional sliding mode controller is a robust, stable and reliable controller but there are three main issues limiting;   

equivalent part related to dynamic equation of FODS computation of the bounds of uncertainties chattering phenomenon [4-7].

The problem in equivalent nonlinear dynamic formulation of FODS is not a simple task and in this research model-free theory is used to reduce this challenge. Due to literature to reduce or eliminate the chattering two main methodologies are introduced [8-11]:  Linear (saturation) boundary layer method  Nonlinear artificial intelligence based method However eliminating the function in sliding mode controllers are used in many research but it can causes to lost the robustness of control and accuracy. In this research incremental PID controller is used to improve the chattering phenomenon. Uncertainties are very important challenges and caused to overestimation of the bounds. As this point if the sliding surface is equal to zero and it is chosen as desired sliding surface, the dynamic of FODS is derived to sliding surface and if switching function is used to reduce the challenge of uncertainty then the linearization and decoupling through the use of feedback, can be realized [12-16]. This paper is organized as follows; second part focuses on the system modeling dynamic formulation. Third part is focused on the methodology. Simulation result and

314

Copyright ⓒ 2015 SERSC


discussion is illustrated in forth part. The last part focuses on the conclusion and compare between this method and the other ones.

2. Theory Delay First Order Plant: Many industrial processes can be represented by a first order model; equation (1) shows the mathematical plant model (in s-plane). Discrete transfer function of this model has obtained using ZOH method, and the selected sampling period (T) is 0.1, equation (2) shows the discrete transfer functions, (in z-plane). (1)

and; (2)

The time delay occurs when a sensor or an actuator are used with a physical separation. Equation (3) shows the mathematical plant model (in s-plane). Discrete transfer functions of this model has been obtained using ZOH method, and the selected sampling period (T) is 0.1, equation (4 and 5) show the discrete transfer functions, (in z-plane). (3)

(4)

(5)

Conventional Sliding Mode Controller: According to above the main four objectives to design controllers are: stability, robust, minimum error and reliability. Linear PID controller is model-free controller and this controller is not reliable. One of the robust nonlinear controllers which have been analyzed by many researchers especially in recent years to control of nonlinear systems is sliding mode controller (SMC). Sliding mode controller (SMC) is robust conventional nonlinear controller in a partly uncertain dynamic system‟s parameters. This conventional nonlinear controller is used in several applications such as in robotics, process control, aerospace and power electronics. This controller can solve two most important challenging topics in control theory, stability and robustness [ 12-19]. The main idea to design sliding mode control is based on the following formulation; (6)

{ where

is sliding surface (switching surface), for MIMO system, is the torque. According to above formulation the main part of this control theory is switching part this idea is caused to increase the speed of response. Sliding mode controller is divided into two main sub parts:


315


 

Discontinues controller Equivalent controller

Figure 1 shows the main part of sliding mode controller with application to FODS.

Figure 1. Block diagram of Conventional Sliding Mode Controller Discontinues controller is used to design suitable tracking performance based on very fast switching. This part of controller is work based on the linear type methodology; therefore it can be PD, PI and PID. Fast switching or discontinuous part have essential role to achieve to good trajectory following, but it is caused system instability and chattering phenomenon. Chattering phenomenon is one of the main challenges in conventional sliding mode controller and it can causes some important mechanical problems such as saturation and heats the mechanical parts of FODS or drivers. Figure 2 shows the sliding surface and chattering phenomenon [10-13].

Figure 2. Sliding Surface and Chattering Phenomenon [3] Equivalent part of robust nonlinear sliding mode controller is the impact of nonlinear term of FODS. It is caused to the control reliability and used to fine tuning the sliding surface slope [3, 7]. The equivalent part of sliding mode controller is the second challenge in uncertain systems especially in FODS because in condition of uncertainty calculate the nonlinear term of FODS‟s dynamic is very difficult or unfeasible. Based on the literature to reduce or eliminate the chattering, various papers have been reported by many researchers which classified into two main methods:  boundary layer saturation method  artificial intelligence based method The boundary layer saturation method is used to reduce or eliminate the high frequency oscillation in conventional sliding mode controller. However this method can solve the challenge of chattering phenomenon but it has two main challenges; increase the error and

316



reduces the speed of response. Figure 3 shows the linear saturation boundary layer function.

Figure 3. Linear Saturation Boundary Layer Functions [3]

3. Methodology In a P controller the control deviation is produced by forming the difference between the process variable and the desired output ; this is then amplified to give the manipulating variable, which operates a suitable actuator. The P controller simply responds to the magnitude of the deviation and amplifies it. As far as the controller is concerned, it is unimportant whether the deviation occurs very quickly or is present over a long period. Beside P component, there are other control components that behave in the same way mentioned above: “D” component responds to changes in the process variable, and “I” component responds to the duration of the deviation. It sums the deviation applied to its input over a period of time. The D and I components, are often combined with a P component to give PI, PD or PID controllers. The PID control law could be represented in two forms, positional form and incremental form. Assume: (7) (8) (9) (10) The continuous-time linear PID controller in position form is described by the following equation:


317


(11)

∫

With time , being continuous instead of discrete, K is a gain, is integration time, and is derivative time. The corresponding discrete-time position form is: ∑

{

}

∑

(12)

∑ Where T is the sampling period, are the proportional gain, integral gain and derivative gain of the PID controller, respectively. The above PID control algorithms are in position form because they directly compute the controller output itself. The PID controller is often used in the incremental form, in which the controller calculates change of the controller output. Note that at sampling time n-1, ∑

(13)

Hence, the incremental form of the PID controller corresponding to Equation (12) is: (14) On the other hand, the integral term can cause slower system response and larger system overshoot; it should not be included in certain applications of PID control. When is set to zero in Equation (12), the PID controller becomes a PI controller in incremental form: (15) Whereas when in incremental form:

in Equation (14), the PID controller reduces to a PD controller

(16) A PI controller in incremental form is related to a PD controller in position form. Letting in Equation (14), a PD controller is obtained in position form: (17) Now by comparing Equation (17) with Equation (15), it could see that the PD controller in position form becomes the PI controller in incremental form if (1) exchange positions, (2) is replaced by , and (3) is replaced by as shown in Figure(4).

318



Figure 4. Steps Required Making the PD in Position Form Works as PI in Incremental Form In PD controller the control law is given by the following equation; ̇

(18)

̇ ̇ . Where and ̇ In this theory and are positive constant. To show this controller is stable and achieves zero steady state error, the Lyapunov function is introduced; [ ̇

̇

[ ̇

(19)

]

̇] ̇

If the conversation energy is written by the following form: [ ̇ Where ̇ energy.

̇]

(20) ̇

̇

shows the power inputs and

̇ [

If ̇

̇ ] is the derivative of the kinetic

]

(21) ̇ , we can write:

Based on ̇

[ ̇

̇

̇

(22)

, we have ̇

̈

̈

(23)

In this state, the actual trajectories converge to the desired state. The dynamic formulation of nonlinear single input system is defined by [7]: ⃗

⃗


(24)

319


is the vector of control input, is the derivation of , [ ̇ ̈ ] is the state vector, is unknown or uncertainty, and is known switching (SIGN) function. The main target to design sliding mode controller is high speed train and high ̇ ̈ tracking accuracy to the desired joint variables; [ ] , according to actual and desired joint variables, the trucking error vector is defined by: ̃

[̃

̃

]

(25)

According to the sliding mode controller theory, the main important part to design this controller is sliding surface, a time-varying sliding surface in the state space is given by the following formulation: (26)

̃

λ is the sliding surface slope coefficient and it is positive constant. The sliding surface can be defined as Proportional-Derivative (PD), Proportional-Integral (PI) and the Proportional-Integral-Derivative (PID). The following formulations represented the three groups are: ̇

(27) (∫ ̃

(28)

)

(29)

∑ ̇

(30)

∑

Integral part of sliding surface is used to decrease the steady state error in sliding mode controller. To have the stability and minimum error in sliding mode controller, the main objective is kept the sliding surface slope near to the zero. Therefore, one of the common strategies is to find input outside of . |

(31)

|

ζ is positive constant. If S(0)>0

̇

(32)

Derivative term of ∫

̇

is eliminated by limited integral from t=0 to t=

is the time that trajectories reach to the sliding surface. If formulation of calculated by;

320

(33)

∫ the



(34)

If |

|

(35)

Above formulation guarantees time to reach the sliding surface is smaller than the trajectories are outside of

|

|

since

. (36)

According to above discussion the formulation of sliding surface ̃ ̇

̈

̈

̇

(37) ̇

̇ is;

The change of sliding surface ̇

is defined as

̇

(38)

According to the formulation of the second order system, a simple solution to get the sliding condition when the dynamic parameters have uncertainty in parameters or external disturbance is the switching control law: ⃗

(39)

The switching function

is defined as (40)

{

⃗ The is the positive constant and the sliding surface can be PD, PI and PID. According to this research, the formulation of robust partly incremental PID sliding mode controller for FODS is; (41)

In PD sliding surface, the change of sliding surface calculated as; ̇

̇ ̇

̈

The discontinuous switching term


(42)

is computed as;

321


(43) ̇

(44)

(45)

∑ )

(

The discontinuous switching part is; (

̇

(46)

∑ )

4. Result and Discussion Figure 5 shows the trajectory tracking and disturbance rejection for PID incremental model free sliding mode controller. According to Figure 5, in certain condition proposed method has about 0.002 errors. In presence of uncertainty the controller‟s error is about 0.091. According to the following Figure, proposed method eliminates the chattering as well have an acceptable rise time. The rise time in certain and uncertain test is the same and equal to 1.1 second.

5

X: 27.2 Y: 5.091

X: 29.9 Y: 5.002

4 Reference Proposed-SMC(0% Disturbance) Proposed-SMC(40% Disturbance)

3

2

1

0

-1

-2

0

5

10

15

20

25

30

Figure 5. Incremental PID model-free Sliding Mode Control Algorithm: Certain and Uncertain Conditions

322



Figure 6 shows the energy outputs in certain and uncertain conditions. According to level of energy it can be seen that, the absolute level of energy in both certain and uncertain conditions are the same but in uncertain condition proposed method has challenge in transient time. 6

5

4 Proposed-SMC (0% Disturbance) Proposed-SMC (40% Disturbance) 3

2

1

0

-1

-2

-3

-4

-5

0

5

10

15

20

25

Figure 6. Incremental PID model-free Sliding Mode Control Algorithm for Energy level: Certain and Uncertain Conditions In Figure 7, root means square error for proposed method in certain and uncertain conditions are compared. According to the following Figure, the rate of RMS error in certain condition is less than uncertain condition. Regarding to Figures 5 to 7 clear that, proposed method eliminate the chattering in certain and uncertain condition and this method have acceptable rise time.


323


1.5

Proposed-SMC(40% Disturbance) Proposed-SMC(0% Disturbance)

RMS error

1

0.5

0 5

10

15 Time

20

25

30

Figure 7. Incremental PID model-free Sliding Mode Control Algorithm for RMS error: Certain and Uncertain Conditions

5. Conclusion In this research, PID incremental model-free sliding mode algorithm is recommended for first order delay system to reduce the rise time as well the other performance. To design robust and stable controller PID incremental conventional model free sliding mode controller is recommended. Conventional sliding mode controller (SMC) is a nonlinear, stable, robust and reliable controller. This type of controller has two main important challenges: chattering phenomenon and equivalent part related to dynamic equation of FODS. Conventional sliding mode controller is worked based on FODS‟s dynamic model. Based on equivalent part in conventional nonlinear controllers, in complex and highly nonlinear systems these controllers have many problems for accurate responses because these type of controllers need to have accurate knowledge of dynamic formulation of system. The nonlinear dynamic formulation problem in highly nonlinear system can be solved by model-free sliding mode controller. In sliding mode controller select the desired sliding surface and function play a vital role to system performance and if the dynamic of FODS is derived to sliding surface then the linearization and decoupling through the use of feedback, not gears, can be realized. As a result, uncertainties are estimated by discontinuous feedback control but it can cause to chattering. To reduce the chattering in presence of switching functions; linear controller is added to discontinuous part of sliding mode controller. Linear controller is type of stable controller as well as conventional sliding mode controller. In proposed methodology PID incremental linear controller is used in parallel with discontinuous part to reduce the role of sliding surface slope as a main coefficient. The simulation results show that the proposed controller

324



works well in various situations. This type of method is the best robust method to eliminate the chattering in presence of uncertainty with switching function. Based on result and discussion, proposed method can eliminate chattering in certain and uncertain condition. This controller reduces the level of energy due to the output energy performance. This type of controller remove the noise oscillation in presence of uncertainties and 40% external disturbance.

Acknowledgment The authors would like to thank the anonymous reviewers for their careful reading of this paper and for their helpful comments. This work was supported by the Iranian Institute of Advance Science and Technology Program of Iran under grant no. 2015Persian Gulf-1. Project Title:” Design a Micro-electronic Based Nonlinear Controller for First Order Delay System” Iranian center of Advance Science and Technology (IRAN SSP) is one of the independent research centers specializing in research and training across of Control and Automation, Electrical and Electronic Engineering, and Mechatronics & Robotics in Iran. At IRAN SSP research center, we are united and energized by one mission to discover and develop innovative engineering methodology that solve the most important challenges in field of advance science and technology. The IRAN SSP Center is instead to fill a long standing void in applied engineering by linking the training a development function one side and policy research on the other. This center divided into two main units:  

Education unit Research and Development unit

References: [1] [2] [3] [4]

[5] [6]

[7] [8]

[9]

[10] [11]

Vachtsevanos, G. I., Davey, K. and Lee, K. M., "Development of a Novel Intelligent Robotic Manipulator," IEEE Control System Magazine, 1987, pp.9-15. Davey, K., Vachtsevanos, G. I., and Powers, R., "An analysis of Fields and Torques in Spherical Induction Motors," lEE Transactions on Magnetics, Vol. MAG-23, 1987, pp. 273-282. Foggia, A., Oliver, E., Chappuis, F., "New Three Degrees of Freedom Electromagnetic Actuator," Conference Record -lAS Annual Meeting, Vol. 35, New York, 1988. Lee, K. M., Vachtsevanos, G. and Kwan, C-K., "Development of a Spherical Wrist Stepper Motor," Proceedings of the 1988 IEEE lntemational Conference on Robotics and Automation, Philadelphia, PA. April 26-29. Lee, K. M., Pei. I., "Kinematic Analysis of a Three Degree-of-Freedom Spherical Wrist Actuator," The Fifth International Conference on Advanced Robotics, Italy, 1991. Wang, I., Jewel, G., Howe, D., "Modeling of a Novel Spherical Pennanent Magnet Actuator," Proceedings of IEEE International Conference on Robotics and Automation, Albuquerque, New Mexico, pp 1190-1195, 1997. Wang, I., Jewel, G., Howe, D., "Analysis, Design and Control of a Novel Spherical Pennanent Magnet Actuator," lEE Proceedings on Electrical Power Applications., vol. 154, no. 1, 1998. Chirikjian, G. S., and Stein, D., "Kinematic Design and Commutation of a Spherical Stepper Motor," IEEEIASME Transactions on Mechatronics, vol. 4, n 4, Piscataway, New Jersey, pp. 342-353, Dec. 1999. Kahlen, K., and De Doncker, R. W., "CW'l'ent Regulators for Multi-phase Pennanent Magnet Spherical Machines." Industry Applications Conference Record of the 2000 IEEE, vol. 3, 2000, pp. 2011-2016. Lee, K. M., Pei, I., and Gilboa, U., "On the Development of a Spherical Wrist Actuator," Proceedings of the 16th NSF Conference on Manufacturing Systems Research, Tempe AZ, January 8-12, 1990. Z. Liu, H. Su, S. Pan, A new adaptive sliding mode control of uncertain nonlinear systems, Asian Journal of Control, 2014, vol. 16, no. 1, 198—208


325


[12] [13]

[14]

[15]

[16]

[17]

[18]

[19]

Y. Shang, Consensus recovery from intentional attacks in directed nonlinear multi-agent systems, International Journal of Nonlinear Sciences and Numerical Simulation, 2013, vol. 14, no. 6, 355—361 Farzin Piltan, A. R. Salehi & Nasri B Sulaiman,“Design Artificial Robust Control of Second Order System Based on Adaptive Fuzzy Gain Scheduling”, World Applied Science Journal (WASJ), 13 (5): 1085-1092, 2011, (ISI, Scopus, SJR=0.22, Q2) M. N. Kamarudin, A. R. Husain, M. N. Ahmad, Control of uncertain nonlinear systems using mixed nonlinear damping function and backstepping techniques, 2012 IEEE International Conference on Control Systems, Computing and Engineering, Malaysia, 2012 Farzin Piltan, N. Sulaiman, S.Soltani, M. H. Marhaban & R. Ramli, “An Adaptive Sliding Surface Slope Adjustment in PD Sliding Mode Fuzzy Control For Robot Manipulator”, International Journal of Control and Automation, 4 (3): 65-76, 2011, (Scopus, SJR=0.25., Q3) Alireza Siahbazi, Ali Barzegar, Mahmood Vosoogh, Abdol Majid Mirshekaran, Samira Soltani,"Design Modified Sliding Mode Controller with Parallel Fuzzy Inference System Compensator to Control of Spherical Motor", IJISA, vol.6, no.3, pp.12-25, 2014. DOI: 10.5815/ijisa.2014.03.02 Mojtaba Yaghoot, Farzin Piltan, Meysam Esmaeili, Mohammad Ali Tayebi, Mahsa Piltan,"Design Intelligent Robust Model-base Sliding Guidance Controller for Spherical Motor", IJMECS, vol.6, no.3, pp.61-72, 2014.DOI: 10.5815/ijmecs.2014.03.08 Farzin Matin, Farzin Piltan, Hamid Cheraghi, Nasim Sobhani, Maryam Rahmani,"Design Intelligent PID like Fuzzy Sliding Mode Controller for Spherical Motor", IJIEEB, vol.6, no.2, pp.53-63, 2014. DOI: 10.5815/ijieeb.2014.02.07. Mohammad Reza Avazpour, Farzin Piltan, Mohammad Hadi Mazloom, Amirzubir Sahamijoo, Hootan Ghiasi and Nasri B. Sulaiman,"Research on First Order Delays System Automation", International Journal of Grid Distribution Computing, vol.8, no.4, pp.211-224, 2015. http://dx. doi.org/10.14257/ijgdc.2015.8.4.20

Authors Ali Roshanzamir is currently Research Assistant at Institute of Advanced Science and Technology, Research and Training Center, IRAN SSP. He is research assistant of team (8 researchers) to design a Micro-electronic Based nonlinear controller for first order delay system since March, 2015, research student (45 researchers) to Nonlinear control of Industrial Robot Manipulator for Experimental Research and Education from June 2010 to June 2011, and published 5 journal papers since 2011 to date. His current research interests are nonlinear control, artificial control system, Microelectronic Device, and HDL design.

Farzin Piltan was born on 1975, Shiraz, Iran. In 2004 he is jointed Institute of Advance Science and Technology, Research and Development Center, IRAN SSP. Now he is a dean of Intelligent Control and Robotics Lab. He is led of team (47 researchers) to design and build of nonlinear control of industrial robot manipulator for experimental research and education and published about 54 Papers in this field since 2010 to 2012, team supervisor and leader (9 researchers) to design and implement intelligent tuning the rate of fuel ratio in internal combustion engine for experimental research and education and published about 17 Journal papers since 2011 to 2013, team leader and advisor (34 researchers) of filtering the hand tremors in flexible surgical robot for experimental research and education and published about 31 journal papers in this field since 2012 to date, led of team (21 researchers) to design high precision and fast dynamic controller for multi-degrees of freedom actuator for experimental research and education and published about 7 journal papers in this field since 2013 to date, led of team (22 researchers) to research of

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full digital control for nonlinear systems (e.g., Industrial Robot Manipulator, IC Engine, Continuum Robot, and Spherical Motor) for experimental research and education and published about 4 journal papers in this field since 2010 to date, team team supervisor and leader to design Intelligent FPGA-Based Control Unit to Control of 4-DOF Medical Robot Manipulator since July, 2015 to now, team supervisor and leader of team (8 researchers) to design a Microelectronic Based nonlinear controller for first order delay system since March, 2015 to now, team supervisor and leader (4 researchers) to design Intelligent Vibration Robot control for Dental Automation since Jun, 2014 to now and finally led of team (more than 130 researchers) to implementation of Project Based-Learning project at IRAN SSP research center for experimental research and education, and published more than 115 journal papers since 2010 to date. In addition to 7 textbooks, Farzin Piltan is the main author of more than 115 scientific papers in refereed journals. He is editorial review board member for „international journal of control and automation (IJCA), Australia, ISSN: 2005-4297; „International Journal of Intelligent System and Applications (IJISA)‟, Hong Kong, ISSN:2074-9058; „IAES international journal of robotics and automation, Malaysia, ISSN:2089-4856; „International Journal of Reconfigurable and Embedded Systems‟, Malaysia, ISSN:2089-4864. His current research interests are nonlinear control, artificial control system and applied to FPGA, robotics and artificial nonlinear control and IC engine modeling and control.

Arman Jahed is currently Research Assistant at Institute of Advanced Science and Technology, Research and Training Center, IRAN SSP. He is research assistant of team (8 researchers) to design a Micro-electronic Based nonlinear controller for first order delay system since March, 2015, research student (45 researchers) to Nonlinear control of Industrial Robot Manipulator for Experimental Research and Education from February 2012 to February 2013, and published 5 journal papers since 2012 to date. His current research interests are nonlinear control, artificial control system, Microelectronic Device, and HDL design.

Saman Namvarchi, is currently Research Student at Institute of Advanced Science and Technology, Research and Training Center, IRAN SSP. He is research student of team (8 researchers) to design a Micro-electronic Based nonlinear controller for first order delay system since March, 2015. His current research interests are nonlinear control, artificial control system, Microelectronic Device, and HDL design. ..


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Iman nazari is currently Research Assistant at Institute of Advanced Science and Technology, Research and Training Center, IRAN SSP. He is research assistant of team (8 researchers) to design a Micro-electronic Based nonlinear controller for first order delay system since March, 2015, research student (45 researchers) to Nonlinear control of Industrial Robot Manipulator for Experimental Research and Education from October 2010 to October 2011, and published 7 journal papers since 2011 to date. His current research interests are nonlinear control, artificial control system, Microelectronic Device, and HDL design

Dr. Nasri Sulaiman, is an expert advisor and supervisor in some projects at Iranian institute of Advanced Science and Technology. He received The B.Eng From University Of Putra Malaysia In 1994, M.Sc., From University Of Southampton, Uk In1999, And Phd Degrees From University Of Edinburgh, Uk In 2007, Respectively. He has more than 65 journal and conference papers. He is Currently A Senior Lecturer In The Department Of Electrical Engineering At University Putra Malaysia Of The Program For Signal Processing, And Evolvable Hardware (EHW) and also is head of control and automation laboratory, Iranian Institute Of Advanced Science And Technology, Shiraz, Iran.

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