Hasil dan Analisa Simulasi

(1)

 Hasil dan Analisa Simulasi

23

Dengan Kontroler FSMC dan SMC

 Referensi x =1 dan Tanpa Gangguan

Spesifikasi Respon Waktu

(SMC) (FSMC)

Rise Time 0.285 detik 0.235 detik Setling Time 0.55 detik 0.46 detik

Steady State Error 0 0

Maksimum Overshoot

0 0

(SMC) (FSMC)

Rise Time 0.1 detik 0.1detik

Setling Time 1.051 detik 1.004 detik

Time peak 0.186detik 0.181 detik

Maksimum Overshoot / Undershoot

5.07 x10^-3rad -8.86 x10^-7rad

(2)

 Hasil dan Analisa Simulasi

Dengan Kontroler FSMC dan SMC

 Referensi phi =0.5 dan Tanpa Gangguan

(SMC) (FSMC)

Maksimum Overshoot 0 0

(SMC) (FSMC)

Steady State Error 4 % 0

Maksimum Overshoot 0 0

(3)

 Hasil dan Analisa Simulasi

25

Dengan Kontroler FSMC dan SMC

 Referensi x =1 dan Gangguan impulse

Sliding Mode Control (SMC)

Fuzzy Sliding Mode Control (FSMC)

Rise Time 0.285 detik 0.235 detik

Maksimum Overshoot / undeshoot terhadap gangguan

0.9997 1.001

time peak terhadap gangguan

1.302 detik 1.303 detik

(4)

 Hasil dan Analisa Simulasi

Dengan Kontroler FSMC dan SMC

 Referensi x =1 dan Gangguan impulse

(SMC) (FSMC)

Rise Time 0.1 detik 0.1detik

Time peak 0.186detik 0.181 detik

Maksimum Overshoot / Undershoot

5.07 x10^-3rad -8.86 x10^-7rad

Maksimum Overshoot / Undershoot terhadap gangguan

4.6 x10^-2rad 7.8 x10^-4rad

(5)

 Hasil dan Analisa Simulasi

27

Dengan Kontroler FSMC dan SMC

 Referensi phi =0.5 dan Gangguan impulse

(SMC) (FSMC)

Maksimum Overshoot/undeshoot

terhadap gangguan

4.19x10^-4m 1.55x10^-3m

(SMC) (FSMC)

Steady State Error 4 % 0

Maksimum Overshoot/Undershoot

terhadap gangguan

3.1 x10^-2rad 8x10^-4rad

(6)

 Hasil dan Analisa Simulasi

Dengan Kontroler FSMC dan SMC

 Referensi x =1 dan Gangguan Acak

(SMC) (FSMC)

(7)

 Hasil dan Analisa Simulasi

29

Dengan Kontroler FSMC dan SMC

 Referensi phi = 0.5 dan Gangguan Acak

(SMC) (FSMC)

(8)

 Kesimpulan dan Saran

1. Pemodelan dinamik robot inverted pendulum dikembangkan dari model dinamik motor DC, dinamik roda, dan model matematis chasis robot.

2. Metode FSMC memiliki beberapa kelebihan dibanding metode SMC

diantaranya memiliki rise time, settling time,dan error steady state lebih kecil.

3. Dalam simulasi menggunakan gangguan berupa sinyal impulse dan sinyal acak, FSMC memiliki magnitude osilasi (chatering) yang lebih kecil

dibandingkan dengan SMC.

Kesimpulan

Saran

1. Mengguanakan metode lain dalam pemodelan robot milsalkan metode Lagrange.

2. Menggunakan Fuzzy Adaptive, Sehingga menjadi Adaptive Fuzzy Sliding Mode Control (AFSMC)

3. Dapat mengimplementasikan pada miniatur Robot

(9)

 Referensi

[1]. Palm, R., Driankov, D dan Hellendoorn, H, (1997), Model Based Fuzzy Control: Fuzzy Gain Schedulers and Sliding Mode Fuzzy Controllers, Spinger-Verlag., Berlin.

[2]. Abdollah, M.F. (2006), Proportional Integral Sliding Mode Control of a Wheeled Balancing Robot, Master Engineering Thesis., University Teknologi Malaysia, Malaysia.

[3]. Ooi, R.C. (2003), Balancing a Two-Wheeled Autonomous Robot, B.Sc. Final Year Project, University of Western Australia School of Mechanical Engineering, Australia.

[4]. Grasser, F., D’Arrigo, A., Colombi, S., Rufer, A.C. (2002), “JOE: A Mobile Inverted pendulum”, IEEE Transactions on Industrial Electronics, 49(1): 107-114.

[5]. Perruquetti, W. and Barbot, J.P, (2002), Sliding mode control In Engineering, Marcel Deker, Inc., New York.

[6]. Sung, W.K, dan Ju, J.L. (1995), “Design of a Fuzzy Controller With Fuzzy Sliding Surface”, Elsevier: Fuzzy Sets and Systems, 71(1995): 359-367.

[7]. Bessa, W.M., Dutra, M.S., dan Kreuzer, E. (2008), “Depth Contol of Remotely Operated

Underwater Vehicles Using an Adaptive Fuzzy Sliding Mode Controller”, Elsevier: Robotics and Autonomous Systems, 56(2008): 670-677.

[8]. Palm, R. (1994), “Robust Control by Fuzzy Sliding Mode”. Elsevier: Automatica, Vol.30, N0.9, pp.1429-1437.

[9]. Thongcai, S. (2004), “Fuzzy Sliding Mode Design”, the Jurnal of KNITNB, Vol.14, No.1.

[10]. Utkin, V.I. (1977), “Variable Structure System with Sliding Modes”, IEEE Transactions

on Automatic Control. AC-22(2): 212-222. ₃₁

(10)

[11]. Nawawi S.W. (2006), “Control of Two-wheels Inverted pendulum Mobile Robot Using Full Order Sliding Mode Control”, Proceedings of International Conference on Man-Machine Systems 2006 September 15-16 2006, Langkawi, Malaysia.

[12]. Nawawi S.W. (2006), “Controller Design for Two-Wheels inverted Pendulum Vehicle Using PISMC”. University Technology Malaysia, Johor baru, Malaysia.

[13]. Rizan, R.I.(2008), Analisis dan Perancangan System Pengendali Pada Inverted

Pendulum Menggunakan Metode Fuzzy Sliding Mode Control, Tugas Akhir Sarjana Teknik, Institut Teknologi Sepuluh Nopember, Surabaya.

[14]. Miranda, J.L.C. (2009), Application of Kalman Filtering and PID Control for Direct Inverted pendulum Control, Master of Science Thesis, California State University, USA..

[15]. Miller, P. (2008), Bulding a Two Wheeled Balancing Robot, B.Sc. Final Year Project University of Southern Queensland, England..

[16]. Mokonopi, K. (2006), Balancing A Two Wheeled Robot, B.Sc. Final Year Project, University of Southern Queensland, England.

[17]. Pathak, K., Franch, J. and Agrawal, S.K. (2005), “Velocity and Position Control of a Wheeled Inverted pendulum by Partial Feedback Linearization”, IEEE Transactions on Robotics, 21(3): 505-513.

[18]. Shiroma, N, Matsumoto, O., Kajita, S. & Tani, K. (1996), “Cooperative Behaviour of a Wheeled Inverted pendulum for Object Transportation”, Proceedings of the 1996 IEEE/RSJ International Conference on Intelligent Robots and Systems '96, IROS 96, Volume: 2 , 4-8 Nov.

1996 Page(s): 396 -401.

[19]. Wijaya, P.K.P. (2010), “Disain Kendali Robust Pada Balancing Robot Menggunakan

Fuzzy Sliding Mode Control (FSMC)”, Proceedings Seminar Nasional Program Magister Dan ³²

(11)

33