Modeling and adaptive control of nonlinear dynamical systems using radial basis function network
作者: Rajesh KumarSmriti SrivastavaJ. R. P. Gupta
作者单位: 1Netaji Subhas Institute of Technology
刊名: Soft Computing, 2017, Vol.21 (15), pp.4447-4463
来源数据库: Springer Nature Journal
DOI: 10.1007/s00500-016-2447-9
关键词: Radial basis function networkNonlinear system identification and controlGradient descent principleMulti-layer feed-forward neural networkRobustness
英文摘要: In this paper, the use of radial basis function network (RBFN) for simultaneous online identification and indirect adaptive control of nonlinear dynamical systems is demonstrated. The motivation of using RBFN comes from the simplicity of its structure and simpler mathematical formulation, which gives it an advantage over multi-layer feed-forward neural network (MLFFNN). Since most processes are nonlinear, the use of conventional proportional-integral-derivative controller is not useful. Most of the time plant’s dynamics information is not available. This creates another limitation on the use of conventional control techniques, which works only if plant’s dynamics information is available. The proposed controller is tested for parameter variations and disturbance effects. Simulation...
原始语种摘要: In this paper, the use of radial basis function network (RBFN) for simultaneous online identification and indirect adaptive control of nonlinear dynamical systems is demonstrated. The motivation of using RBFN comes from the simplicity of its structure and simpler mathematical formulation, which gives it an advantage over multi-layer feed-forward neural network (MLFFNN). Since most processes are nonlinear, the use of conventional proportional-integral-derivative controller is not useful. Most of the time plant’s dynamics information is not available. This creates another limitation on the use of conventional control techniques, which works only if plant’s dynamics information is available. The proposed controller is tested for parameter variations and disturbance effects. Simulation...
全文获取路径: Springer Nature  (合作)
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来源刊物:
影响因子:1.124 (2012)

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关键词翻译
关键词翻译
  • control 控制
  • function 函数
  • basis 
  • network 网络
  • systems 系统科学与软件
  • information 报告
  • nonlinear 非线性的
  • dynamical 动力学的
  • radial 放射状的
  • proposed 建议的