Author
Naiyao Zhang
Bio: Naiyao Zhang is an academic researcher from Tsinghua University. The author has contributed to research in topics: Fuzzy control system & Fuzzy logic. The author has an hindex of 4, co-authored 8 publications receiving 210 citations.
Papers
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TL;DR: Simulation results have shown that the hierarchical fuzzy control scheme can control the ball from a point to another without hitting the obstacles and in the least time.
113 citations
TL;DR: It is proved that the closed-loop system is asymptotically stable based on Lyapunov synthesis approach while the identification of the controlled plant is unnecessary, and these schemes are applied to control a chaotic system.
72 citations
TL;DR: Digital simulation with NETOMAC software has verified that the fuzzy control scheme can improve power system transient stability and damp power swings very quickly.
14 citations
28 Oct 1997
TL;DR: Digital simulations with NOTOMAC software have demonstrated that the fuzzy control scheme is superior to other conventional control methods and effectively improves power systems transient stability and very quickly damp power swings.
Abstract: The Takagi-Sugeno model (T-S model) based fuzzy control scheme for nonlinear systems is presented in this paper. In the T-S fuzzy model, a nonlinear system is represented with a set of fuzzy rules which describe the local linear dynamics. Then the parallel distributed compensation (PDC) design is employed in the fuzzy controller design. Linear optimal control is used in the derivation of each control rule. The overall fuzzy controller is a fuzzy blending of each individual linear controller. Quadratic stability of the overall nonlinear control system can be checked and ensured with H/sup /spl infin// control theory. The fuzzy control scheme is applied to thyristor-controlled series compensator control in power system transients. Digital simulations with NOTOMAC software have demonstrated that the fuzzy control scheme is superior to other conventional control methods. The fuzzy controller effectively improves power systems transient stability and very quickly damp power swings.
11 citations
18 Aug 1998
TL;DR: A T-S model based fuzzy control scheme for thyristor controlled series compensation (TCSC) in power system transients is presented and a practical approach requiring only local signals is described.
Abstract: A desirable transient stability control scheme should be able to ensure the first-swing stability as well as provide effective damping. This paper presents a T-S model based fuzzy control scheme for thyristor controlled series compensation (TCSC) in power system transients. The nonlinear power system containing a TCSC is modelled as a fuzzy "blending" of a set of locally linearized models. A linear optimal control is designed for each local linear model. Different control requirements at different stages during power system transients can be considered in deriving the linear control rules. The resulting fuzzy controller is then a fuzzy "blending" of these linear controllers. Furthermore, a gradient descent algorithm is put forward to modify the rule truth factors for optimization. For convenient industrial realization of the fuzzy control scheme, a practical approach requiring only local signals is also described. Simulation with NETOMAC software has verified the effectiveness of the fuzzy control scheme.
4 citations
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TL;DR: A survey of the emerging field termed “control of chaos” is given, which includes traditional control engineering methods including linear, nonlinear and adaptive control, neural networks and fuzzy control, and applications in various fields of engineering.
364 citations
TL;DR: The problems and methods of control of chaos, which in the last decade was the subject of intensive studies, were reviewed and the basic results obtained within the framework of the traditional linear, nonlinear, and adaptive control, as well as the neural network systems and fuzzy systems were presented.
Abstract: The problems and methods of control of chaos, which in the last decade was the subject of intensive studies, were reviewed. The three historically earliest and most actively developing directions of research such as the open-loop control based on periodic system excitation, the method of Poincare map linearization (OGY method), and the method of time-delayed feedback (Pyragas method) were discussed in detail. The basic results obtained within the framework of the traditional linear, nonlinear, and adaptive control, as well as the neural network systems and fuzzy systems were presented. The open problems concerned mostly with support of the methods were formulated. The second part of the review will be devoted to the most interesting applications.
261 citations
TL;DR: This paper presents two indirect adaptive fuzzy control schemes for a class of uncertain continuous-time multi-input multi-output nonlinear dynamic systems and derived based on Lyapunov stability analysis so that, under appropriate assumptions, semi-global stability and asymptotic convergence to zero of tracking errors can be guaranteed.
251 citations
TL;DR: This paper investigates fuzzy adaptive control schemes for a class of multi-input multi-output (MIMO) unknown nonlinear systems with known and unknown sign of the control gain matrix and proposes an adaptation proportional-integral (PI) law.
243 citations
TL;DR: A direct adaptive fuzzy control scheme for a class of uncertain continuous-time single-input single-output (SISO) nonaffine nonlinear dynamic systems and it is shown that the tracking error converges to a neighborhood of zero.
217 citations