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Danwei Wang
Researcher at Nanyang Technological University
Publications - 519
Citations - 14382
Danwei Wang is an academic researcher from Nanyang Technological University. The author has contributed to research in topics: Iterative learning control & Control theory. The author has an hindex of 57, co-authored 480 publications receiving 12155 citations. Previous affiliations of Danwei Wang include University of Michigan & University of California, Berkeley.
Papers
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Relationship between space-vector modulation and three-phase carrier-based PWM: a comprehensive analysis [three-phase inverters]
Keliang Zhou,Danwei Wang +1 more
TL;DR: This paper comprehensively analyzes the relationship between space-vector modulation and three-phase carrier-based pulse width modulation and shows that all the drawn conclusions are independent of the load type.
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Feedback stabilization and tracking of constrained robots
N.H. McClamroch,Danwei Wang +1 more
TL;DR: In this paper, the effects of constraint force required to maintain satisfaction of the constraints are considered, and conditions for stabilization and tracking using feedback are developed using mathematical models for constrained robot dynamics.
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Linear Phase Lead Compensation Repetitive Control of a CVCF PWM Inverter
TL;DR: A simple and efficient linear phase lead compensation repetitive control scheme for engineers to develop high-performance power converter systems that helps a repetitive controller to achieve faster convergence rate, higher tracking accuracy, and wider stability region.
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Dynamic surface control of constrained hypersonic flight models with parameter estimation and actuator compensation
TL;DR: In this article, the robust adaptive controller for the longitudinal dynamics of a generic hypersonic flight vehicle is investigated for the control-oriented model, where the velocity and attitude subsystems are transformed into the linearly parameterized form.
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Brief Iterative learning control with initial rectifying action
Mingxuan Sun,Danwei Wang +1 more
TL;DR: Finite initial rectifying action is introduced in the learning algorithm and is shown effective in the improvement of tracking performance, in particular robustness with respect to variable initial shifts.