
Discrete-Time Recurrent Neural Control by Edgar N Sanchez
The book presents recent advances in the theory of neural control for discrete-time nonlinear systems with multiple inputs and multiple outputs. It provides solutions for the output trajectory tracking problem of unknown nonlinear systems based on sliding modes and inverse optimal control scheme.-
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Advances in Missile Guidance, Control, and Estimation
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Robust Formation Control for Multiple Unmanned Aerial Vehicles
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Analysis and Synthesis of Fuzzy Control Systems
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Control of Nonlinear Systems via PI, PD and PID
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Wireless Ad hoc and Sensor Networks
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Modeling and Control of Complex Systems
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Distributed Adaptive Consensus Control of Uncertain Multi-Agent Systems
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Optimal Event-Triggered Control Using Adaptive Dynamic Programming
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Control of Nonlinear Systems
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Maneuverable Formation Control in Constrained Space
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Reinforcement Learning and Dynamic Programming Using Function Approximators
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Quantitative Process Control Theory
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Modeling and Control for Micro/Nano Devices and Systems
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Sliding Mode Control in Electro-Mechanical Systems
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System Modeling and Control with Resource-Oriented Petri Nets
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Variable Gain Control and Its Applications in Energy Conversion
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Optimal Control
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Cooperative Control of Multi-Agent Systems
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Multi-Agent Systems
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Deterministic Learning Theory for Identification, Recognition, and Control
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Synchronization and Control of Multiagent Systems
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Anti-Disturbance Control for Systems with Multiple Disturbances
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Tensor Product Model Transformation in Polytopic Model-Based Control
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Fundamentals in Modeling and Control of Mobile Manipulators
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Intelligent Diagnosis and Prognosis of Industrial Networked Systems
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Nonlinear Control of Dynamic Networks
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End-to-End Adaptive Congestion Control in TCP/IP Networks
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Electric and Plug-in Hybrid Vehicle Networks
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Fuzzy Controller Design
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Modern Control Engineering
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Nonlinear Control of Electric Machinery
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Nonlinear Pinning Control of Complex Dynamical Networks
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Classical Feedback Control with Nonlinear Multi-Loop Systems
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Adaptive Control of Dynamic Systems with Uncertainty and Quantization
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Optimal Networked Control Systems with MATLAB
"This book on Discrete-time Recurrent Neural Control is unique in the literature, with new knowledge and information about the new technique of recurrent neural control especially for discrete-time systems
The book is well organized and clearly presented. It will be welcome by a wide range of researchers in science and engineering, especially graduate students and junior researchers who want to learn the new notion of recurrent neural control. I believe it will have a good market.
It is an excellent book after all."
— Guanrong Chen, City University of Hong Kong
"This book includes very relevant topics, about neural control. In these days, Artificial Neural Networks have been recovering their relevance and well-stablished importance, this due to its great capacity to process big amounts of data. Artificial Neural Networks development always is related to technological advancements; therefore, it is not a surprise that now we are being witnesses of this new era in Artificial Neural Networks, however most of the developments in this research area only focuses on applicability of the proposed schemes. However, Edgar N. Sanchez author of this book does not lose focus and include both important applications as well as a deep theoretical analysis of Artificial Neural Networks to control discrete-time nonlinear systems. It is important to remark that first, the considered Artificial Neural Networks are development in discrete-time this simplify its implementation in real-time; secondly, the proposed applications ranging from modelling of unknown discrete-time on linear systems to control electrical machines with an emphasize to renewable energy systems. However, its applications are not limited to these kind of systems, due to their theoretical foundation it can be applicable to a large class of nonlinear systems. All of these is supported by the solid research done by the author."
— Alma Y. Alanis, University of Guadalajara, Mexico
"This book discusses in detail; how neural networks can be used for optimal as well as robust control design. Design of neural network controllers for real time applications such as induction motors, boost converters, inverted pendulum and doubly fed induction generators has also been carried out which gives the book an edge over other similar titles. This book will be an asset for the novice to the experienced ones."
— Rajesh Joseph Abraham, Indian Institute of Space Science & Technology, Thiruvananthapuram, India
| SKU | Unavailable |
| ISBN 13 | 9781138550209 |
| ISBN 10 | 1138550205 |
| Title | Discrete-Time Recurrent Neural Control |
| Author | Edgar N Sanchez |
| Series | Automation And Control Engineering |
| Condition | Unavailable |
| Binding Type | Hardback |
| Publisher | CRC Press |
| Year published | 2018-09-04 |
| Number of pages | 271 |
| Cover note | Book picture is for illustrative purposes only, actual binding, cover or edition may vary. |
| Note | Unavailable |





































