Applications of Neural Adaptive Control Technology
This book presents the results of the second workshop on Neural Adaptive Control Technology, NACT II, held on September 9-10, 1996, in Berlin. The workshop was organised in connection with a three-year European-Union-funded Basic Research Project in the ESPRIT framework, called NACT, a collaboration between Daimler-Benz (Germany) and the University of Glasgow (Scotland).The NACT project, which began on 1 April 1994, is a study of the fundamental properties of neural-network-based adaptive control systems. Where possible, links with traditional adaptive control systems are exploited. A major aim is to develop a systematic engineering procedure for designing neural controllers for nonlinear dynamic systems. The techniques developed are being evaluated on concrete industrial problems from within the Daimler-Benz group of companies.The aim of the workshop was to bring together selected invited specialists in the fields of adaptive control, nonlinear systems and neural networks. The first workshop (NACT I) took place in Glasgow in May 1995 and was mainly devoted to theoretical issues of neural adaptive control. Besides monitoring further development of theory, the NACT II workshop was focused on industrial applications and software tools. This context dictated the focus of the book and guided the editors in the choice of the papers and their subsequent reshaping into substantive book chapters. Thus, with the project having progressed into its applications stage, emphasis is put on the transfer of theory of neural adaptive engineering into industrial practice. The contributors are therefore both renowned academics and practitioners from major industrial users of neurocontrol.
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adaptive law algorithm applications approach approximation artificial neural networks asymptotically asymptotically stable basal ganglia bounded buffer closed-loop component computational control input control law control loop control structure control system controller design convergence Daimler-Benz defined differential inequalities DMLP dynamic systems equation estimation feedback control feedforward Fourier Gaussian Gaussian function given global identification IEEE implemented integration internal model inverse-dynamics layer learning line current linear linearisation linearizable Lyapunov Lyapunov function Lyapunov stability method minimum phase model networks modelling error module motion planning NARX neural adaptive control neural control neural model neurons nodes nonlinear control nonlinear systems operating regimes optimization output feedback output weights parameters PD-controller perturbation plant positive definite problem robot arm robotic manipulator robust sampling scheduling Section sensor signal simulation SIMULINK space speed speed-field stability submachine Theorem tion topology torque tracking error trajectory trivial solution values variables vector voltage zero