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Differential Neural Networks for Robust Nonlinear Control
Alexander S. PoznyakEdgar N. SanchezWen Yu (profesor titular.)
其他書名
Identification, State Estimation and Trajectory Tracking
出版World Scientific, 2001
主題Science / Mechanics / DynamicsComputers / Neural Networks
ISBN981281129X9789812811295
URLhttp://books.google.com.hk/books?id=ut4orOH_VPsC&hl=&source=gbs_api
EBookSAMPLE
註釋This book deals with continuous time dynamic neural networks theory applied to the solution of basic problems in robust control theory, including identification, state space estimation (based on neuro-observers) and trajectory tracking. The plants to be identified and controlled are assumed to be a priori unknown but belonging to a given class containing internal unmodelled dynamics and external perturbations as well. The error stability analysis and the corresponding error bounds for different problems are presented. The effectiveness of the suggested approach is illustrated by its application to various controlled physical systems (robotic, chaotic, chemical, etc.). Contents: Theoretical Study: Neural Networks Structures; Nonlinear System Identification: Differential Learning; Sliding Mode Identification: Algebraic Learning; Neural State Estimation; Passivation via Neuro Control; Neuro Trajectory Tracking; Neurocontrol Applications: Neural Control for Chaos; Neuro Control for Robot Manipulators; Identification of Chemical Processes; Neuro Control for Distillation Column; General Conclusions and Future Work; Appendices: Some Useful Mathematical Facts; Elements of Qualitative Theory of ODE; Locally Optimal Control and Optimization. Readership: Graduate students, researchers, academics/lecturers and industrialists in neural networks.