Edgar N. Sanchez

Guanrong Chen

Carlos J. Vega

Oscar J. Suarez

This book presents two nonlinear control strategies for complex dynamical networks. First, sliding-mode control is used, and then the inverse optimal control approach is employed. For both cases, model-based is considered in Chapter 3 and Chapter 5; then, Chapter 4 and Chapter 6 are based on determining a model for the unknow system using a recurrent neural network, using on-line extended Kalman filtering for learning. The book is organized in four sections. The first one covers mathematical preliminaries, with a brief review for complex networks, and the pinning methodology. Additionally, sliding-mode control and inverse optimal control are introduced. Neural network structures are also discussed along with a description of the high-order ones. The second section presents the analysis and simulation results for sliding-mode control for identical as well as non-identical nodes. The third section describes analysis and simulation results for inverse optimal control considering identical or non-identical nodes. Finally, the last section presents applications of these schemes, using gene regulatory networks and microgrids as examples.

Analysis and Applications

Nonlinear Pinning Control of Complex Dynamical Networks

Riccardo S. Sanchez-Pena

Daniel R. Charnavvsky

The Artificial Pancreas presents research on the top issues related to the artificial pancreas (AP) and its application to diabetes. AP is a newer form of treatment to inject insulin accurately and efficiently, thereby significantly improving the patient's quality of life. By connecting a continuous glucose monitor (CGM) to a continuous subcutaneous insulin infusion using a control algorithm, AP delivers and regulates the most accurate amount of insulin to maintain normal glycemic values. Featuring chapters written by the world's leaders in AP research, this book provides readers with the latest studies and results to assist and improve the lives of patients living with diabetes.

Current Situation and Future Directions

The Artificial Pancreas

Wen Yu

This book constitutes the proceedings of the second International Workshop on Advanced Computational Intelligence (IWACI 2009), with a sequel of IWACI 2008 successfully held in Macao, China. IWACI 2009 provided a high-level international forum for scientists, engineers, and educators to present state-of-the-art research in computational intelligence and related fields. Over the past decades, computational intelligence community has witnessed t- mendous efforts and developments in all aspects of theoretical foundations, archit- tures and network organizations, modelling and simulation, empirical study, as well as a wide range of applications across different domains. IWACI 2009 provided a great platform for the community to share their latest research results, discuss critical future research directions, stimulate innovative research ideas, as well as facilitate inter- tional multidisciplinary collaborations. IWACI 2009 received 146 submissions from about 373 authors in 26 countries and regions (Australia, Brazil, Canada, China, Chile, Hong Kong, India, Islamic Republic of Iran, Japan, Jordan, Macao, Malaysia, Mexico, Pakistan, Philippines, Qatar, Republic of Korea, Singapore, South Africa, Sri Lanka, Spain, Taiwan, Thailand, UK, USA, Ve- zuela, Vietnam, and Yemen) across six continents (Asia, Europe, North America, South America, Africa, and Oceania). Based on the rigorous peer reviews by the Program Committee members, 52 high-quality papers were selected for publication in this book, with an acceptance rate of 36.3%. These papers cover major topics of the theoretical research, empirical study, and applications of computational intelligence.

Advances in Computational Intelligence

Alexander S. Poznyak

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.).

Identification, State Estimation and Trajectory Tracking

Differential Neural Networks for Robust Nonlinear Control

Alma Y. Alanís

Alexander G. Loukianov

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, normally encounter some technical problems such as local minima, slow learning, and high sensitivity to initial conditions, among others. As a viable alternative, new training algorithms, for example, those based on Kalman ?ltering, have been proposed. There already exists publications about trajectory tracking using neural networks; however, most of those works were developed for continuous-time systems. On the other hand, while extensive literature is available for linear discrete-timecontrolsystem,nonlineardiscrete-timecontroldesigntechniques have not been discussed to the same degree. Besides, discrete-time neural networks are better ?tted for real-time implementations.

Trained with Kalman Filtering

Discrete-Time High Order Neural Control

Ramon Garcia-Hernandez

Alma Y. Alanis

Michel Lopez-Franco

Jose A. Ruz-Hernandez

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. The training of each neural network is performed on-line using an extended Kalman filter (EKF). The first indirect decentralized control scheme applies the discrete-time block control approach, to formulate a nonlinear sliding manifold. The second direct decentralized neural control scheme is based on the backstepping technique, approximated by a high order neural network. The third control scheme applies a decentralized neural inverse optimal control for stabilization. The fourth decentralized neural inverse optimal control is designed for trajectory tracking. This comprehensive work on decentralized control of robot manipulators and mobile robots is intended for professors, students and professionals wanting to understand and apply advanced knowledge in their field of work.

Decentralized Neural Control: Application to Robotics

Edgar N. Sanchez

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