Liuping Wang
Papers
13
Total Citations
253
H-Index
9
About
Liuping Wang is a leading figure in advanced control systems, with research spanning predictive control, repetitive control, and mobile robot localization. Her major contributions lie in the integration of model predictive control (MPC) with repetitive control, enabling precise tracking of periodic signals and effective rejection of bandlimited disturbances—a critical capability for applications like robot arms and industrial automation. Her 2013 work on predictive-repetitive control with constraints has garnered 49 citations, while her 2011 paper on multivariable repetitive-predictive controllers using frequency decomposition has 41 citations, showcasing her foundational impact. Wang has also made notable strides in robotics, pioneering the use of Doppler–Azimuth radar for cost-effective, lightweight robot navigation and localization, as seen in her 2018 KLD-sampling with Gmapping paper (41 citations). Her 2022 textbook, *State Feedback Control and Kalman Filtering with MATLAB/Simulink Tutorials*, bridges theory and practice, making complex state-space methods accessible to students and engineers. With over 200 total citations across her top works, Wang’s research is distinguished by its experimental validation and practical implementation, cementing her reputation as a scholar who advances both theory and real-world control solutions.
Research Focus
Key Achievements
Top Papers
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- 4Predictive iterative learning control with experimental validation31 citations · 2016
- 5Monte Carlo localisation of a mobile robot using a Doppler–Azimuth radar18 citations · 2018
- 6Disturbance observer-based predictive repetitive control with constraints15 citations · 2020
- 7Feature-based robot navigation using a Doppler-azimuth radar14 citations · 2016
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- 9State Feedback Control and Kalman Filtering with MATLAB/Simulink Tutorials11 citations · 2022
- 10