Robust trajectory tracking control of an underactuated control moment gyroscope via neural network?based feedback linearization ?

被引:27
作者
Moreno-Valenzuela, Javier [1 ]
Montoya-Chairez, Jorge [1 ]
Santibanez, Victor [2 ]
机构
[1] Inst Politecn Nacl CITEDI, Ave Inst Politecn Nacl 1310, Tijuana 22435, Baja California, Mexico
[2] Inst Tecnol La Laguna, Tecnol Nacl Mexico, Blvd Revoluc & Cuauhtemoc SN, Torreon 27000, Mexico
关键词
INERTIA WHEEL PENDULUM; SLIDING MODE CONTROL; ATTITUDE-CONTROL; ADAPTIVE-CONTROL; STEERING LAW; VEHICLE; SYSTEM;
D O I
10.1016/j.neucom.2020.04.019
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
In this document, an underactuated two degrees–of–freedom control moment gyroscope (CMG) is studied. Specifically, the problem of trajectory tracking in the non-actuated joint is addressed. The feedback linearization technique is used to design a model-based controller. Then, an adaptive neural network–based scheme is designed to add robustness with respect to model uncertainties. Studies on the internal and output dynamics are presented. The introduced theory is validated by means of real–time experiments. The comparisons among a linear controller, a cascaded PID–PID scheme, and a known adaptive neural network controller are presented to assess the performance of the novel robust controller given in this work. Better tracking accuracy is obtained with the introduced approach. © 2020 Elsevier B.V.
引用
收藏
页码:314 / 324
页数:11
相关论文
共 70 条
[31]  
Lewis F., 1998, NEURAL NETWORK CONTR
[32]   Nonlinear Decoupling Control of Two-Terminal MMC-HVDC Based on Feedback Linearization [J].
Li, Zheng ;
Hao, Quanrui ;
Gao, Feng ;
Wu, Linlin ;
Guan, Minyuan .
IEEE TRANSACTIONS ON POWER DELIVERY, 2019, 34 (01) :376-386
[33]   Neural network adaptive sliding mode control for omnidirectional vehicle with uncertainties [J].
Lu, Xingyang ;
Zhang, Xiangyin ;
Zhang, Guoliang ;
Fan, Jinhui ;
Jia, Songmin .
ISA TRANSACTIONS, 2019, 86 :201-214
[34]   Attitude control of spacecraft simulator without angular velocity measurement [J].
Malekzadeh, Maryam ;
Sadeghian, Hamid .
CONTROL ENGINEERING PRACTICE, 2019, 84 :72-81
[35]  
Meriam J. L., 2010, ENG MECH DYNAMICS
[36]   Adaptive sliding mode control for finite-time stability of quad-rotor UAVs with parametric uncertainties [J].
Mofid, Omid ;
Mobayen, Saleh .
ISA TRANSACTIONS, 2018, 72 :1-14
[37]   Adaptive control schemes applied to a control moment gyroscope of 2 degrees of freedom [J].
Montoya-Chairez, Jorge ;
Santibanez, Victor ;
Moreno-Valenzuela, Javier .
MECHATRONICS, 2019, 57 :73-85
[38]   A Feedback Linearization-Based Motion Controller for a UWMR with Experimental Evaluations [J].
Montoya-Villegas, Luis ;
Moreno-Valenzuela, Javier ;
Perez-Alcocert, Ricardo .
ROBOTICA, 2019, 37 (06) :1073-1089
[39]   Two adaptive control strategies for trajectory tracking of the inertia wheel pendulum: neural networks vis a vis model regressor [J].
Moreno-Valenzuela, Javier ;
Aguilar-Avelar, Carlos ;
Puga-Guzman, Sergio ;
Santibanez, Victor .
INTELLIGENT AUTOMATION AND SOFT COMPUTING, 2017, 23 (01) :63-73
[40]   Adaptive Neural Network Control for the Trajectory Tracking of the Furuta Pendulum [J].
Moreno-Valenzuela, Javier ;
Aguilar-Avelar, Carlos ;
Puga-Guzman, Sergio A. ;
Santibanez, Victor .
IEEE TRANSACTIONS ON CYBERNETICS, 2016, 46 (12) :3439-3452