K
Kasemsak Uthaichana
Researcher at Chiang Mai University
Publications - 24
Citations - 502
Kasemsak Uthaichana is an academic researcher from Chiang Mai University. The author has contributed to research in topics: Model predictive control & Optimal control. The author has an hindex of 8, co-authored 24 publications receiving 445 citations. Previous affiliations of Kasemsak Uthaichana include Purdue University & Caterpillar Inc..
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Journal ArticleDOI
Development and Validation of a Battery Model Useful for Discharging and Charging Power Control and Lifetime Estimation
TL;DR: In this paper, a partially linearized (in battery power) input-output battery model was developed for lead-acid batteries in a hybrid electric vehicle, which can be extended to different battery types, such as lithium-ion, nickel-metal hydride, and alkaline.
Journal ArticleDOI
MPC of Switching in a Boost Converter Using a Hybrid State Model With a Sliding Mode Observer
TL;DR: The results of time-domain simulation and hardware experiments are used to validate and compare the response of the hybrid optimal control-sliding mode observer to that of a traditional current-mode control strategy.
Journal ArticleDOI
Applications of Numerical Optimal Control to Nonlinear Hybrid Systems
TL;DR: In this article, the authors developed a technique for numerically solving hybrid optimal control problems for unicycle models with both autonomous and controlled switches, which can be used to reduce the computational complexity over existing approaches, making optimal control highly appealing for hybrid systems.
Journal ArticleDOI
Hybrid Model Predictive Control for the Stabilization of Wheeled Mobile Robots Subject to Wheel Slippage
TL;DR: It is shown that recently developed embedding techniques can be used to formulate numerical algorithms for the hybrid model predictive control (MPC) problem that have the same complexity as the MPC for smooth systems.
Proceedings ArticleDOI
Hybrid Model Predictive Control for Stabilization of Wheeled Mobile Robots Subject to Wheel Slippage
TL;DR: It is shown that recently developed embedding techniques can be used to formulate numerical algorithms for the hybrid MPC problem that have the same complexity as MPC for smooth systems.