Neural networks have become a well-established methodology as exempli?ed by their applications to identi?cation and control of general nonlinear and complex systems; the use of high order neural networks for modeling and learning has recently increased. Usingneuralnetworks, controlalgorithmscanbedevelopedtoberobustto uncertainties and modeling errors. The most used NN structures are Feedf- ward networks and Recurrent networks. The latter type o?ers a better suited tool to model and control of nonlinear systems. There exist di?erent training algorithms for neural networks, which, h- ever,...
Neural networks have become a well-established methodology as exempli?ed by their applications to identi?cation and control of general nonlinear and c...
This book constitutes the proceedings of the second International Workshop on Advanced Computational Intelligence (IWACI2009), with a sequel of IWACI 2008 successfully held in Macao, China. IWACI2009 provided a high-level international forum for scientists, engineers, and educators to present state-of-the-art research in computational intelligence and related fields.
This book constitutes the proceedings of the second International Workshop on Advanced Computational Intelligence (IWACI2009), with a sequel of IWACI ...
Neural networks have become a well-established methodology as exempli?ed by their applications to identi?cation and control of general nonlinear and complex systems; the use of high order neural networks for modeling and learning has recently increased. Usingneuralnetworks, controlalgorithmscanbedevelopedtoberobustto uncertainties and modeling errors. The most used NN structures are Feedf- ward networks and Recurrent networks. The latter type o?ers a better suited tool to model and control of nonlinear systems. There exist di?erent training algorithms for neural networks, which, h- ever,...
Neural networks have become a well-established methodology as exempli?ed by their applications to identi?cation and control of general nonlinear and c...
This volume 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...
This volume deals with continuous time dynamic neural networks theory applied to the solution of basic problems in robust control theory, including id...
Discrete-Time Inverse Optimal Control for Nonlinear Systems proposes a novel inverse optimal control scheme for stabilization and trajectory tracking of discrete-time nonlinear systems. This avoids the need to solve the associated Hamilton-Jacobi-Bellman equation and minimizes a cost functional, resulting in a more efficient controller.
Design More Efficient Controllers for Stabilization and Trajectory Tracking of Discrete-Time Nonlinear Systems
The book presents two approaches for controller synthesis: the first based on passivity...
Discrete-Time Inverse Optimal Control for Nonlinear Systems proposes a novel inverse optimal control scheme for stabilization and ...
Doubly Fed Induction Generators: Control for Wind Energy provides a detailed source of information on the modeling and design of controllers for the doubly fed induction generator (DFIG) used in wind energy applications. Focusing on the use of nonlinear control techniques, this book:
Discusses the main features and advantages of the DFIG
Describes key theoretical fundamentals and the DFIG mathematical model
Develops controllers using inverse optimal control, sliding modes, and neural networks
Devises an improvement to add...
Doubly Fed Induction Generators: Control for Wind Energy provides a detailed source of information on the modeling and design of c...
Ramon Garcia-Hernandez Michel Lopez-Franco Edgar N. Sanchez
This book provides a decentralized approach for the identification and control of robotics systems. It also presents recent research in decentralized neural control and includes applications to robotics. Decentralized control is free from difficulties due to complexity in design, debugging, data gathering and storage requirements, making it preferable for interconnected systems. Furthermore, as opposed to the centralized approach, it can be implemented with parallel processors.
This approach deals with four decentralized control schemes, which are able to identify the robot dynamics....
This book provides a decentralized approach for the identification and control of robotics systems. It also presents recent research in decentraliz...
