Geri Dön

Bulanık adaptif kayan kipli robot kontrolü

Başlık çevirisi mevcut değil.

  1. Tez No: 46227
  2. Yazar: RAGIP MUSTAFA BAŞBUĞ
  3. Danışmanlar: DOÇ.DR. FUAT GÜRLEYEN
  4. Tez Türü: Doktora
  5. Konular: Bilgisayar Mühendisliği Bilimleri-Bilgisayar ve Kontrol, Computer Engineering and Computer Science and Control
  6. Anahtar Kelimeler: Denetim sistemleri, Robot denetim, Robotlar, Control systems, Robot control, Robots
  7. Yıl: 1995
  8. Dil: Türkçe
  9. Üniversite: İstanbul Teknik Üniversitesi
  10. Enstitü: Fen Bilimleri Enstitüsü
  11. Ana Bilim Dalı: Belirtilmemiş.
  12. Bilim Dalı: Belirtilmemiş.
  13. Sayfa Sayısı: Belirtilmemiş.

Özet

ÖZET Bulanık Adaptif Kayan Kipli Robot Kontrolü konusundaki bu tezde, Tübitak MAM Robotik Bölümünde tasarlanan ve imal edilen endüstriyel tipteki MAMROB ER- 15 Robotunun dinamik modeli gözönüne alınarak, dayanıklı, integral etkili dayanıklı, dayanıklı uyarlamalı, kestirilmiş eşdeğer kontrollü çatırtısız kayan kipli ve bulanık adaptif kayan kipli kontrol yöntemleri geliştirilerek robot kontrolü yapılmıştır. Birinci aşamada, MAMROB robotunun L-E ve parametre doğrusallaştırılmış dinamik modelleri elde edilmiştir. İkinci aşamada, dayanıklı, integral etkili dayanıklı ve dayanıklı uyarlamalı kontrol yöntemleri geliştirilerek MAMROB robotuna uygulanmıştır, integral etkili dayanıklı kontrol yöntemi olarak geliştirilen bu yeni yöntem, kontrol kuralındaki dayanıklı doyma ve integral etkilerinin olumlu tarafları birleştirilerek elde edilmiştir. Bu yöntemlere ait simülasyon sonuçlan verilmiştir. Üçüncü aşamada, SABANOVIC 'in kestirilmiş eşdeğer kontrollü çatırtısız kayan kipli kontrol yöntemleri geliştirilerek MAMROB robotuna uygulanmış ve deneysel çalışmalara ait sonuçlar verilmiştir. Son aşamada, Sabanovic'in Kayan Kipli Kontrol yönteminin Adaptif Kayan Kipli Kontrol Yöntemi olabilmesi için, bulanık uyarlama mekanizması geliştirilerek, bulanık adaptif kayan kipli kontrol yöntemi, tezde bir yenilik olarak sunulmuştur. Literatürde teklif edilen bulanık kayan kipli kontrol yöntemlerinden farklı olarak ele alman uyarlama mekanizması 7.Bölümde ayrıntıları ile birlikte sunulmuştur. VII

Özet (Çeviri)

SUMMARY In this thesis, some certain control techniques are developed and applied to a new six degree of freedom ( DOF ) robot manipulator (MAMROB) which is made by Tubitak MAM, CAD/CAM Robotics Department. First of all, we derived the dynamic model of the MAMROB as a new industrial robot. In order to obtain the dynamic model of robot, we prefer the Lagrange-Euler method. We used the true physical parameter values in our model. In addition to classical control techniques, we proposed a novel robust control approach based on parameter linearization method. In this control, we proposed a globally asymptotically stable robust control scheme by combining integral control with a robust saturation control law. This method takes advantage of both saturation control and integral control techniques. During the derivation of these control scheme, we realized the uncertainty bounds that are required in the synthesis of robust control laws which are difficult to estimate precisely because they depend not only on various physical parameters of robot, but also on the commanded trajectory and the manipulator motion states. In this thesis, we also used a new chattering free sliding mode control technique. In classical sliding mode control the most important problem is the chattering which is high frequency oscillations of the control signal. A chattering signal may cause fatigue in harmonic drives in robot arms. In our method, chattering is avoided by selecting a different Lyapunov function. This novel approach can be used directly, if the controller parameters set property. ' Due to nonproper selection of control parameters and load variations, chattering may arise and cause some problems even when this algorithm is used. In order to overcome this problem we proposed a novel adaptation technique based on fuzzy logic. Fuzzy adaptive sliding mode control has both robust control properties and adaptive capability. IXIn the firs chapter, the general procedures are introduced and some technical information is given about MAMROB. The general kinematics and dynamics derivation methods for robot manipulators are introduced in the second and third chapters. In the fourth chapter, the dynamics and kinematics equations of MAMROB are derived for its real time controls and their simulation studies. Five different control approaches are simulated in the fifth chapter to test their control performances. These control approaches are PID, MRAC, Robust Control, Modified Robust Control and Robust Adaptive Control. L-E dynamic model of robot is as follows.... M(q)q+ C(q,q)q+ g(q) = x (1) Parameter linearized dynamic model of robot is as follows M(q)q+C(q,q)q+g(q) = Y(q,q,q)p = r (2) where, Y (nxm) is a known matrix called regression matrix of robot, p (mxl) is an unknown parameter vector. Let p0 e9?m and /?e$R+ respectively be nominal parameter vector and a positive number that defines a bound of parametric uncertainty. The parameter error and its norm can be defined as given below; \\p-Po\\*P 0) Let to be nominal torque vector corresponding to nominal parameter vector p0 and it can be defined asr0 = M0 (q)a + C0 (q, q)v + g0 (q) - Kr (3) *0=Y(q,q,v,a)p0-Kr (4) In Eq. (4), K is a positive definite diagonal matrix and vector valued functions v, a, r are defined as a = v = <ld-k<l (5) r-q+Aq where qt, q = q-qd and A respectively are desired position vector, position error vector and a positive definite matrix of appropriate dimension. After some arrangements, the control rule is taken as follows t = t0 +Y(q,q,v,a)u = Y(q,q,v,a)(p0 +u)-Kr (6) *.. - M(q)r+C(q,q)r + Kr = Y(q,q,v,a)(p+u) (7) where, u is an additional control rule that can be chosen in order to provide robustness against the parametric uncertainties [86]. For this purpose, we have a = i YTr YTr if,\YTr\>6 -YTr if,\YTr\<e P i. e (8) Finally, control and adaptation rules can be written as follows A. A x = M(q)a + C{q, q)v + g(q) -Kr = Y(q, q, v,a) p- Kr p = -TY(q,q,v,a)Tr (9) XISome results of computer simulations of MAMROB controlled by above mentioned control laws are illustrated in the following figures. 1.5 position of link- 1 (rad) position of link-2 (rad) 0.5 0.5 1.5 1 0.5 0. time (sec) position of link-3. (rad) ^ ! ! / : i I j j ? 2 4 time (sec) 1 0.5 0.0.5 time (sec) position error of link- 1 (rad) time (sec) Figure 1. Position responses of links of MAMROB with adaptive control. position of link- 1. (rad) 0.5 1 A time (sec) position of link-2. (rad) 0.5 1.5 1 0.5 0 r 1.5 position of link-2. (rad) 0 9 A time (sec) positions of links (1,2,3) 0.5 2 4 time (sec) 2 4 time (sec) Figure 2. Positions responses of links of MAMROB with robust control. XIIIn the sixth chapter, first the conventional sliding mode control is shortly introduced, then a novel control approach called chattering free sliding mode control is derived that it can be used safely for robotic manipulators. The control approach developed in this thesis not only has been used in simulation but also has actually been applied to MAMROB. The various results that we have obtained in both simulations and experiments are presented in detail in chapter 6. Selected control rule is as follows; u = uea + Ko (10) where K is a gain matrix, a is a vector valued function called sliding function and u ^ is a control termed as estimated equivalent control of which the defining equation is tieq - 1 rs+l' (11) where, ris the cut-off frequency of low pass filter. Some results obtained by computer simulations in case of chattering free sliding mode control of MAMROB are shown in the following figures. ?02...op position of link- 1 (rad) 13 3 4 time (sec) vdocity etrcr phasepianetflink-l. artrolsfendtflHc-l. ? 005 001 001S 002 0025 043 00£ tUM 0045 pcaticnenxr 0*9 Figure 3. Position response, phase plane trajectory and time evolution of control signal of link-1 in case of chattering free sliding mode control of MAMROB. In the final chapter, fuzzy logic adaptation techniques are developed for chattering free sliding mode control. In this novel control approach, we have proposed a new fuzzy logic adaptation mechanism in order to adjust the sliding mode controller adaptively thus we have a better performance in different working conditions of robots. XIIIBlock diagram of this new method is shown in figure 4. SLIDING MODE CONTROLLER Xr t / / ?M(q) r Au O FUZZY ADAPTATION MECHANISM delay * ROBOT Figure 4. Fuzzy Adaptive Sliding Mode Control Block Diagram of MAMROB. Some simulation results with fuzzy adaptive sliding mode control are illustrated in the following figures.. 1 0.5 0 -0.5 I 1.5 position and step ref. of link-1 (rad) time (sec) position error of link-1 (rad) 0.5 time (sec) velocity of link-1. (rad/sec) 5 time (sec) control signal of link-1. (Nm) Figure 5. Position, velocity, position error and control signal of link-1 of MAMROB with a fuzzy adaptive sliding mode control scheme. XIV

Benzer Tezler

  1. Tez No

    105304

    İmalat sektöründe transport amacına yönelik robot araçların tasarımı

    Design of robot vehicles for transportation in manufacturing sector

    ZEKERİYA GÜNEŞ

    Yüksek Lisans

    Türkçe

    Türkçe

    2001

    Makine MühendisliğiAtatürk Üniversitesi

    Makine Mühendisliği Ana Bilim Dalı

    DOÇ. DR. BÜNYAMİN AKSAKAL

  2. Tez No

    34697

    Yüksek hızlı fuzzy lojik kontrolcü kullanarak bir servo motorun adaptif kontrolü

    Başlık çevirisi yok

    ŞENOL ALKAN

    Yüksek Lisans

    Türkçe

    Türkçe

    1994

    Elektrik ve Elektronik MühendisliğiYıldız Teknik Üniversitesi

    Elektrik Ana Bilim Dalı

    DOÇ. DR. GALİP CANSEVER

  3. Tez No

    78133

    Klasik kontrol yöntemleri ve bulanık denetimin bir PMDC motorda denenerek sonuçların karşılaştırılması

    Experimental evaluation of the classical and fuzzy control method on PMDC motor

    CELAL SAMYELİ

    Yüksek Lisans

    Türkçe

    Türkçe

    1998

    Makine MühendisliğiKaradeniz Teknik Üniversitesi

    Makine Mühendisliği Ana Bilim Dalı

    DOÇ. DR. LEVENT GÜMÜŞEL

  4. Tez No

    104608

    Adaptive data compression in networks: A learning automaton approach

    Ağlarda adaptif veri sıkıştırma: Öğrenebilen durum makinesi yaklaşımı

    EMRE BAYDOĞAN

    Yüksek Lisans

    İngilizce

    İngilizce

    2001

    Bilgisayar Mühendisliği Bilimleri-Bilgisayar ve KontrolMarmara Üniversitesi

    Bilgisayar Mühendisliği Ana Bilim Dalı

    YRD. DOÇ. DR. M. BORAHAN TÜMER

  5. Tez No

    100815

    Neuro-Fuzzy variable structure control of robotic manipulators

    Robot kollarının bulanık yapay sinir ağları ile değişken yapılı kontrolu

    HASAN PALAZ

    Doktora

    İngilizce

    İngilizce

    2000

    Bilgisayar Mühendisliği Bilimleri-Bilgisayar ve Kontrolİstanbul Teknik Üniversitesi

    PROF.DR. M. KEMAL SARIOĞLU

Geri Dön