Dynamic nonlinear inverse-model based control of a twin rotor system using adaptive neuro-fuzzy inference system

被引：17
作者：
Toha, S. F. [1 ]
Tokhi, M. O. [1 ]
机构：
[1] Univ Sheffield, Dept Automat Control & Syst Engn, Sheffield S10 2TN, S Yorkshire, England
来源：
2009 THIRD UKSIM EUROPEAN SYMPOSIUM ON COMPUTER MODELING AND SIMULATION (EMS 2009) | 2009年
关键词：
Inverse-model; twin rotor multi-input multi-output system (TRMS); adaptive neuro fuzzy system (ANFIS);
D O I：
10.1109/EMS.2009.106
中图分类号：
TP301 [理论、方法];
学科分类号：
081202 ;
摘要：
A dynamic control system design has been a great demand in the control engineering community, with many applications particularly in the field of flight control. This paper presents investigations into the development of a dynamic nonlinear inverse-model based control of a twin rotor multi-input multi-output system (TRMS). The TRMS is an aerodynamic test rig representing the control challenges of modern air vehicle. A model inversion control with the developed adaptive model is applied to the system. An adaptive neuro-fuzzy inference system (ANFIS) is augmented with the control system to improve the control response. To demonstrate the applicability of the methods, a simulated hovering motion of the TRMS, derived from experimental data is considered in order to evaluate the tracking properties and robustness capacities of the inverse- model control technique.
引用
收藏
页码：107 / 111
页数：5
相关论文
共 14 条
[1]  
[Anonymous], VIBRATION SHOCK HDB
[2]   Nonlinear inverse dynamics control of the aircraft in the presence of windshear [J].
Cao, YH ;
Chen, Y ;
Yuan, KG ;
Jin, CJ .
AIRCRAFT ENGINEERING AND AEROSPACE TECHNOLOGY, 2004, 76 (06) :592-599
[3]  
*FEEDB INSTR LTD, 1996, TWIN ROT MIMO SYST M
[4]   A model-based inverse method for positioning scatterers in a cladded component inspected by ultrasonic waves [J].
Haïat, G ;
Lhémery, A ;
Calmon, P ;
Lasserre, F .
ULTRASONICS, 2005, 43 (08) :619-628
[5]   ANFIS - ADAPTIVE-NETWORK-BASED FUZZY INFERENCE SYSTEM [J].
JANG, JSR .
IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS, 1993, 23 (03) :665-685
[6]   Inverse model control using recurrent networks [J].
Kambhampati, C ;
Craddock, RJ ;
Tham, M ;
Warwick, K .
MATHEMATICS AND COMPUTERS IN SIMULATION, 2000, 51 (3-4) :181-199
[7]   Adaptive nonlinear control system design for helicopter robust command augmentation [J].
Lee, S ;
Ha, C ;
Kim, BS .
AEROSPACE SCIENCE AND TECHNOLOGY, 2005, 9 (03) :241-251
[8]  
Luo F.-L., 1997, Applied Neural Networks for Signal Processing
[9]   SYNTHESIS OF A HELICOPTER NONLINEAR FLIGHT CONTROLLER USING APPROXIMATE MODEL INVERSION [J].
PRASAD, JVR ;
LIPP, AM .
MATHEMATICAL AND COMPUTER MODELLING, 1993, 18 (3-4) :89-100
[10]   Adaptive non-linear model inversion control of a twin rotor multi-input multi-output system using artificial intelligence [J].
Rahideh, A. ;
Shaheed, H. M. ;
Bajodah, A. H. .
PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART G-JOURNAL OF AEROSPACE ENGINEERING, 2007, 221 (G3) :343-351
12