Yurong Nan
Zhejiang University of Technology
43 Papers
114 Citations
Yurong Nan is an academic researcher from Zhejiang University of Technology. The author has contributed to research in topics: Control theory & Sliding mode control. The author has an hindex of 11, co-authored 43 publications.
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Papers
Adaptive echo state network control for a class of pure-feedback systems with input and output constraints
TL;DR: An improved dynamic surface sliding mode approach is developed by incorporating high-order sliding mode (HOSM) differentiators into each step of controllers design, such that the sluggish effect of the filter performance in conventional dynamic surface control (DSC) can be eliminated.
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Disturbance-observer Based Adaptive Control for Second-order Nonlinear Systems Using Chattering-free Reaching Law
Liang Tao,Qiang Chen,Yurong Nan +2 more
TL;DR: An adaptive sliding mode control incorporating with a nonlinear disturbance observer is proposed for a class of second-order nonlinear systems with unknown parameters and matched lumped disturbance to improve the tracking performance and compensate for the lumping disturbance including perturbations and uncertainties.
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Double Hyperbolic Reaching Law With Chattering-Free and Fast Convergence
TL;DR: A novel continuous reaching law for chattering-free sliding mode control is proposed by using two hyperbolic functions with similar changing rate and opposite amplitude characteristics, which can guarantee the fast convergence as the initial value of the sliding mode variable is far away from the equilibrium.
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Finite-time tracking control for motor servo systems with unknown dead-zones
Qiang Chen,Li Yu,Yurong Nan +2 more
TL;DR: In the proposed controller design, the unknown nonlinearity of the system is approximated by a simple sigmoid neural network, and the approximation error is diminished by employing a robust term.
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Delay-dependent H∞ Control for Uncertain 2-D Discrete Systems with State Delay in the Roesser Model
TL;DR: A delay-dependent optimal state feedback H∞ controller is obtained by solving an LMI optimization problem and a simulation example of thermal processes is given to illustrate the effectiveness of the proposed results.
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