M
Masayoshi Tomizuka
Researcher at University of California, Berkeley
Publications - 1178
Citations - 35429
Masayoshi Tomizuka is an academic researcher from University of California, Berkeley. The author has contributed to research in topics: Control theory & Control system. The author has an hindex of 80, co-authored 1111 publications receiving 30069 citations. Previous affiliations of Masayoshi Tomizuka include University of California & Western Digital.
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Journal Article
Vehicle Lateral Control Under Fault in Front and/or Rear Sensors: Final Report
TL;DR: In this article, the authors present the research results of Task Order 4204(TO4204), Vehicle Lateral Control under Fault in Front and/or Rear Sensors. This project is a continuing effort of the Partners for Advanced Transit and Highways (PATH) on the research of passenger vehicles for Automated Highway Systems (AHS).
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Optimal Control Parameterization for ActivelPassive EXoskeleton with Variable Impedance Actuator
TL;DR: A novel computational method is introduced for APEX to search suitable dynamic properties to assist a given task and results indicate that the method can search the global optimum of the problem to provide users with suitable passive assistance.
Proceedings ArticleDOI
Learning Differentiable Safety-Critical Control using Control Barrier Functions for Generalization to Novel Environments
TL;DR: This work embeds the optimization procedure of the exponential control barrier function based quadratic program (ECBF-QP) as a differentiable layer within a deep learning architecture that enables generalization to new environments for high relative-degree systems with forward invariance guarantees.
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Mode Switching and Smooth Motion Generation for Adaptive Cruise Control Systems by a Virtual Lead Vehicle
TL;DR: The virtual lead vehicle scheme generates smooth reaction for the host vehicle when the lead vehicle cut out or a vehicle cuts in from a side lane and the speed and the location of the virtual vehicle are given by the control system according to the current situation.
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Distributed and cooperative optimization-based iterative learning control for large-scale building temperature regulation
TL;DR: With the algorithm, large-scale building temperature control problems are solvable with reasonable computational load and guaranteed control performance under nearly repetitive disturbances and the convergence property of the algorithm is proved.