Titlebook: Decentralized Neural Control: Application to Robotics; Ramon Garcia-Hernandez,Michel Lopez-Franco,Jose A. Book 2017 Springer International

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Decentralized Inverse Optimal Control for Stabilization: A CLF Approach,ng a suitable controller for each subsystem. Accordingly, each subsystem is approximated by an identifier using a discrete-time recurrent high order neural network (RHONN), trained with an extended Kalman filter (EKF) algorithm. The neural identifier scheme is used to model the uncertain nonlinear s

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Decentralized Inverse Optimal Control for Trajectory Tracking,o design a suitable controller for each subsystem. Accordingly, each subsystem is approximated by an identifier using a discrete-time recurrent high order neural network (RHONN), trained with an extended Kalman filter (EKF) algorithm. The neural identifier scheme is used to model the uncertain nonli

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Schule und Rassismus in den USAr the two DOF robot manipulator, five DOF redundant robot and Shrimp mobile robot. The chapter also includes simulation results for a seven DOF Mitsubishi PA10-7CE robot arm and KUKA youBot mobile robot.

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Robotics Application,r the two DOF robot manipulator, five DOF redundant robot and Shrimp mobile robot. The chapter also includes simulation results for a seven DOF Mitsubishi PA10-7CE robot arm and KUKA youBot mobile robot.

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2198-4182 udes supplementary material: .This book provides a decentralized approach for the identification and control of robotics systems. It also presents recent research in decentralized neural control and includes applications to robotics. Decentralized control is free from difficulties due to complexity

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Schule und Rassismus in den USAeural network (RHONN), trained with an extended Kalman filter (EKF) algorithm. The neural identifier scheme is used to model the uncertain nonlinear subsystem, and based on this neural model and the knowledge of a control Lyapunov function, then an inverse optimal controller is synthesized in order to achieve stability.