Browsing by Author "Narikiyo, Tatsuo"

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Citation - WoS: 17
Citation - Scopus: 20
Adaptive Neural Network-Based Saturated Control of Robotic Exoskeletons
(Springer, 2018) Asl, Hamed Jabbari; Narikiyo, Tatsuo; Kawanishi, Michihiro
In this paper, novel adaptive neural network (NN) controllers with input saturation are presented for n-link robotic exoskeletons. The controllers consist of a state feedback controller and an output feedback controller. Through utilizing auxiliary dynamics, the controllers provide a new framework for input saturated control of these robotic systems which can feature the global stability for state feedback control. To compensate for the unknown dynamics of the system, adaptive schemes based on NNs are exploited. Furthermore, adaptive robust terms are utilized to deal with unknown external disturbances. Stability studies show that the closed-loop system is globally uniformly ultimately bounded (UUB) with the state feedback controller, where the global property of the NN-based controller is achieved exploiting a smooth switching function and a robust control term. Also, the system is semi-globally UUB with the output feedback controller. Effectiveness of the controllers is validated by simulations and experimental tests.
Citation - WoS: 5
Citation - Scopus: 4
Neural Network-Based Asymptotic Tracking Control of Unknown Nonlinear Systems With Continuous Control Command
(Taylor & Francis Ltd, 2020) Babaiasl, Mahdieh; Narikiyo, Tatsuo
This paper proposes a robust tracking controller for a class of nonlinear second-order systems with time-varying uncertainties. The controller is mainly based on the robust integral of the sign of the error (RISE) control approach to achieve an asymptotic stability result with a continuous control command in the presence of additive uncertainties. An adaptive feedforward neural network control term is blended with a new RISE controller to improve the system's transient performance. The proposed RISE controller is a modified version of the existing saturated RISE controller such that only sign of the derivative of the output is needed. The stability of the closed-loop system is well studied, where a local asymptotic stability is proven. The controller performance is validated through simulations on a two-degree-of-freedom lower limb robotic exoskeleton.