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Yinggan Tang
Researcher at Yanshan University
Publications - 62
Citations - 1503
Yinggan Tang is an academic researcher from Yanshan University. The author has contributed to research in topics: Nonlinear system & Chaotic. The author has an hindex of 20, co-authored 54 publications receiving 1220 citations. Previous affiliations of Yinggan Tang include Chinese Ministry of Education.
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Optimum design of fractional order PIλDµ controller for AVR system using chaotic ant swarm
TL;DR: In this paper, a chaotic ant swarm (CAS) optimization method was used to optimize the tuning of FOPID controller, in which the objective function is composed of overshoot, steady-state error, raising time and settling time.
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A new particle swarm optimization algorithm with adaptive inertia weight based on Bayesian techniques
TL;DR: A new adaptive inertia weight adjusting approach is proposed based on Bayesian techniques in PSO, which is used to set up a sound tradeoff between the exploration and exploitation characteristics and is compared with other types of improved PSO algorithms, which also performs well.
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Fractional order sliding mode controller design for antilock braking systems
TL;DR: A novel robust controller named fractional order sliding mode controller (FOSMC) is proposed for ABS to regulate the slip to a desired value and can not only deal with the uncertainties in ABS system but also track the desired slip faster than conventional integer order SMC with proportional or proportional-derivative sliding surface.
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Parameter estimation for time-delay chaotic system by particle swarm optimization
Yinggan Tang,Xinping Guan +1 more
TL;DR: In this article, a particle swarm optimization (PSO) is used to optimize the objective function through particles' cooperation and evolution in a time-delay chaotic system, where the time delay is treated as an additional parameter.
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Dynamic multi-swarm particle swarm optimizer with cooperative learning strategy
TL;DR: The dynamic multi-swarm particle swarm optimizer (DMS-PSO) and a new cooperative learning strategy (CLS) are hybridized to obtain DMS- PSO-CLS, which makes information be used more effectively to generate better quality solutions.