2015
DOI: 10.1016/j.neucom.2015.05.055
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Learning from adaptive neural network control of an underactuated rigid spacecraft

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Cited by 21 publications
(11 citation statements)
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“…Theorem 1. The underactuated system is considered as (2) and the switching surfaces are defined as ( 14) and (15). Using the TSMC disturbance observers ( 18) and ( 19), the error trajectories of disturbance approximation converge to the origin in the finite time.…”
Section: Resultsmentioning
confidence: 99%
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“…Theorem 1. The underactuated system is considered as (2) and the switching surfaces are defined as ( 14) and (15). Using the TSMC disturbance observers ( 18) and ( 19), the error trajectories of disturbance approximation converge to the origin in the finite time.…”
Section: Resultsmentioning
confidence: 99%
“…Theorem 2. Consider the underactuated plant (2), sliding surfaces ( 14) and (15), and FTSMC manifolds (26) and (27). Using the control law as…”
Section: Resultsmentioning
confidence: 99%
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“…Underactuated systems are categories of dynamical robotic systems (with nonlinear structures) which have less number of actuators than degrees of freedom (Lee et al., 2011). The stabilization and tracking control of such systems requires extensive study because of their high applicability in rigid spacecraft (Zeng and Wang, 2015), aircraft (Han, 2005), robotics (Birglen and Gosselin, 2006; Viswanathan et al., 2018), underwater vehicles (Bi et al., 2010; Caharija et al., 2012), flexible manipulators (Xin and Liu, 2013), surface vessels (Yoo and Park, 2017), quadrotor (Zemalache et al., 2005; Yih, 2017), etc. Owing to lower number of actuators in underactuated systems, the consumed energy, final cost, and complexity of this class of systems are low.…”
Section: Introductionmentioning
confidence: 99%
“…Finally, non-linear model predictive control (MPC) was applied to realize attitude control. Based on the learning theory, Zeng and Wang (2015) studied the attitude stabilization problem of an underactuated spacecraft considering the dynamics of unknown systems. The ( w , z ) parameter was used to describe the attitude kinematics of the system and the adaptive neural network (NN) algorithm based on a radial basis function (RBF) was used to achieve stability.…”
Section: Introductionmentioning
confidence: 99%