Abstract
This chapter proposes a decentralized control for trajectory tracking of a nonlinear system using a neural inverse optimal control approach in order to 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 nonlinear system, 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 trajectory tracking. Applicability of the proposed approach is illustrated via real-time control of a Shrimp robot.
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Garcia-Hernandez, R., Lopez-Franco, M., Sanchez, E.N., Alanis, A.Y., Ruz-Hernandez, J.A. (2017). Decentralized Inverse Optimal Control for Trajectory Tracking. In: Decentralized Neural Control: Application to Robotics. Studies in Systems, Decision and Control, vol 96. Springer, Cham. https://doi.org/10.1007/978-3-319-53312-4_6
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DOI: https://doi.org/10.1007/978-3-319-53312-4_6
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