Giving CMAC Basis Functions a Tail in Order to Prevent Bursting in Neural-Adaptive Control

被引:0
|
作者
Macnab, C. J. B. [1 ]
机构
[1] Univ Calgary, Dept Elect & Comp Engn, Calgary, AB, Canada
关键词
NETWORK CONTROL;
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
This work proposes a modification to the Cerebellar Model Articulation Controller (CMAC) for use in a direct adaptive control scheme. With the original CMAC, when an oscillation in the input occurs between two CMAC cells and across the origin, the weights in the adjacent cells drift in opposite directions-resulting in control signal chatter. When the chatter is severe it can cause a sudden increase in error referred to as bursting. Weight update modifications strong enough to prevent weight drift can severely limit performance. In the proposed method each basis function has a tail, remaining non-zero for the duration of the subsequent cells indexing. A small oscillation about the origin now occurs entirely within each basis function, so that each weight is much less likely to drift in one direction. The robust weight modification can then be made much weaker, resulting in significantly better performance. A simulation of a quadrotor helicopter subject to both a sinusoidal disturbance and an unknown payload verifies the stability and performance of the proposed method.
引用
收藏
页码:2182 / 2187
页数:6
相关论文
共 50 条
  • [41] A self-organized multiple model approach for neural-adaptive control of jump nonlinear systems
    Fabri, SG
    Kadirkamanathan, V
    ADAPTIVE SYSTEMS IN CONTROL AND SIGNAL PROCESSING 1998, 2000, : 115 - 120
  • [42] Neural-Adaptive Output Feedback Control of a Class of Transportation Vehicles Based on Wheeled Inverted Pendulum Models
    Li, Zhijun
    Yang, Chenguang
    IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY, 2012, 20 (06) : 1583 - 1591
  • [43] CMAC adaptive control of flexible-joint robots using backstepping with tuning functions
    Macnab, CJB
    D'Eleuterio, GMT
    Meng, M
    2004 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION, VOLS 1- 5, PROCEEDINGS, 2004, : 2679 - 2686
  • [44] Getting weights to behave themselves: Achieving stability and performance in neural-adaptive control when inputs oscillate
    Macnab, CJB
    ACC: Proceedings of the 2005 American Control Conference, Vols 1-7, 2005, : 3192 - 3197
  • [45] NeuRBF: A Neural Fields Representation with Adaptive Radial Basis Functions
    Chen, Zhang
    Li, Zhong
    Song, Liangchen
    Chen, Lele
    Yu, Jingyi
    Yuan, Junsong
    Xu, Yi
    2023 IEEE/CVF INTERNATIONAL CONFERENCE ON COMPUTER VISION, ICCV, 2023, : 4159 - 4171
  • [46] Adaptive CMAC neural control of chaotic systems with a PI-type learning algorithm
    Hsu, Chun-Fei
    Chung, Chao-Ming
    Lin, Chih-Min
    Hsu, Chia-Yu
    EXPERT SYSTEMS WITH APPLICATIONS, 2009, 36 (09) : 11836 - 11843
  • [47] Robust Adaptive Fuzzy Control for Mobile Robot via Fuzzy CMAC Neural Network
    Chen, Jie
    Xi, Wen
    Mo, Wei
    INTERNATIONAL CONFERENCE ON MECHANICS AND CONTROL ENGINEERING (MCE 2015), 2015, : 301 - 307
  • [48] An Introspective Algorithm for Achieving Low-Gain High-Performance Robust Neural-Adaptive Control
    Macnab, C. J. B.
    2014 AMERICAN CONTROL CONFERENCE (ACC), 2014, : 2893 - 2899
  • [49] Local basis functions in adaptive control of elastic systems
    Macnab, C. J. B.
    2005 IEEE INTERNATIONAL CONFERENCE ON MECHATRONICS AND AUTOMATIONS, VOLS 1-4, CONFERENCE PROCEEDINGS, 2005, : 19 - 25
  • [50] High Order Robust Adaptive Control Barrier Functions and Exponentially Stabilizing Adaptive Control Lyapunov Functions
    Cohen, Max H.
    Belta, Calin
    2022 AMERICAN CONTROL CONFERENCE, ACC, 2022, : 2233 - 2238