2006 - IEEE Fellow For contributions to theory and analysis of stable adaptive neural networks for intelligent control systems.
Shuzhi Sam Ge focuses on Control theory, Adaptive control, Lyapunov function, Nonlinear system and Backstepping. His Control theory study combines topics in areas such as Artificial neural network and Control engineering. His Control engineering research incorporates elements of Stability, Robot and Trajectory.
His Adaptive control research is multidisciplinary, relying on both Control system, Robust control, Variable structure control, Bounded function and Adaptive system. His Lyapunov function research focuses on subjects like Boundary, which are linked to Distributed parameter system and Partial differential equation. His studies in Nonlinear system integrate themes in fields like Discrete time and continuous time, Control, MIMO, Mathematical optimization and Bounding overwatch.
Shuzhi Sam Ge mostly deals with Control theory, Adaptive control, Nonlinear system, Artificial neural network and Control engineering. Shuzhi Sam Ge has researched Control theory in several fields, including Robot and Bounded function. His Adaptive control research incorporates elements of Control system, Robust control, Nonlinear control, Tracking error and Adaptive system.
His studies deal with areas such as MIMO, Discrete time and continuous time and Mathematical optimization as well as Nonlinear system. His study in Artificial neural network is interdisciplinary in nature, drawing from both Tracking and Trajectory. He combines subjects such as Stability, Control, Actuator and System dynamics with his study of Control engineering.
Shuzhi Sam Ge mainly focuses on Control theory, Artificial intelligence, Control theory, Control and Nonlinear system. Shuzhi Sam Ge works mostly in the field of Control theory, limiting it down to topics relating to Artificial neural network and, in certain cases, Lyapunov stability, as a part of the same area of interest. His Artificial intelligence research is multidisciplinary, incorporating perspectives in Computer vision and Pattern recognition.
His study in Control focuses on Adaptive control in particular. His work is dedicated to discovering how Nonlinear system, Reinforcement learning are connected with Optimal control and other disciplines. Shuzhi Sam Ge has included themes like Exponential stability and Linear system in his Lyapunov function study.
Shuzhi Sam Ge mainly focuses on Control theory, Nonlinear system, Artificial neural network, Control and Trajectory. His Control theory study integrates concerns from other disciplines, such as Robot and Tracking. His Nonlinear system study incorporates themes from Mathematical optimization, Applied mathematics, Mixture model, Collision and Conditional probability distribution.
The Artificial neural network study combines topics in areas such as Salient, System model, Lyapunov stability, Focus and Reinforcement learning. His research investigates the link between Control and topics such as Control system that cross with problems in Visualization, Protocol and Strict-feedback form. His research integrates issues of Exponential stability and Aerodynamics in his study of Lyapunov function.
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Switched Linear Systems: Control and Design
Zhendong Sun;Shuzhi S. Ge.
Barrier Lyapunov Functions for the control of output-constrained nonlinear systems
Keng Peng Tee;Shuzhi Sam Ge;Eng Hock Tay.
New potential functions for mobile robot path planning
S.S. Ge;Y.J. Cui.
international conference on robotics and automation (2000)
Stable Adaptive Neural Network Control
S. S. Ge;C. C. Hang;T. H. Lee;Tao Zhang.
Dynamic Motion Planning for Mobile Robots Using Potential Field Method
S. S. Ge;Y. J. Cui.
Autonomous Robots (2002)
Brief paper: Adaptive dynamic surface control of nonlinear systems with unknown dead zone in pure feedback form
T. P. Zhang;S. S. Ge.
Analysis and synthesis of switched linear control systems
Zhendong Sun;S. S. Ge.
Switched Linear Systems
Zhendong Sun;Shuzhi Sam Ge.
Adaptive Neural Network Control of Robotic Manipulators
Shuzhi S. Ge;Christopher J. Harris.
Adaptive neural control of uncertain MIMO nonlinear systems
S.S. Ge;Cong Wang.
IEEE Transactions on Neural Networks (2004)
Profile was last updated on December 6th, 2021.
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