Stable Adaptive Fuzzy Control with TSK Fuzzy Friction Estimation for Linear Drive Systems
Journal of Intelligent and Robotic Systems
An improved robust adaptive fuzzy controller for MIMO systems
Control and Intelligent Systems
Direct adaptive fuzzy control with a self-structuring algorithm
Fuzzy Sets and Systems
A new model-free adaptive sliding controller for active suspension system
International Journal of Systems Science
Preventing bursting in approximate-adaptive control when using local basis functions
Fuzzy Sets and Systems
Robust and adaptive design of numerical optimization-based extremum seeking control
Automatica (Journal of IFAC)
On-line robust trajectory generation on approach and landing for reusable launch vehicles
Automatica (Journal of IFAC)
Short-term prediction models for server management in Internet-based contexts
Decision Support Systems
Sampled-data adaptive NN tracking control of uncertain nonlinear systems
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Projection-based adaptive neurocontrol with switching logic deadzone tuning
IEEE Transactions on Neural Networks
ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part I
An Adaptive NN Controller with Second Order SMC-Based NN Weight Update Law for Asymptotic Tracking
ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part II
Power control for cellular communications with channel uncertainties
ACC'09 Proceedings of the 2009 conference on American Control Conference
FAT-based adaptive visual servoing for robots with time varying uncertainties
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
Direct adaptive self-structuring fuzzy controller for nonaffine nonlinear system
Fuzzy Sets and Systems
IEEE Transactions on Neural Networks
A new robust weight update for multilayer-perceptron adaptive control
Control and Intelligent Systems
Resource Management Strategies for the Mobile Web
Mobile Networks and Applications
A Q-modification neuroadaptive control architecture for discrete-time systems
IEEE Transactions on Neural Networks
Fire-rule-based direct adaptive type-2 fuzzy H∞ tracking control
Engineering Applications of Artificial Intelligence
Adaptive control for nonlinear systems with time-varying control gain
Journal of Control Science and Engineering - Special issue on Adaptive Control Theory and Applications
International Journal of Artificial Life Research
International Journal of Fuzzy System Applications
Adaptive high gain observer based output feedback predictive controller for induction motors
Computers and Electrical Engineering
Adaptive type-2 fuzzy sliding mode controller for SISO nonlinear systems subject to actuator faults
International Journal of Automation and Computing
Generalized dynamical fuzzy model for identification and prediction
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
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From the Publisher:A powerful, yet easy-to-use design methodology for the control of nonlinear dynamic systemsA key issue in the design of control systems is proving that the resulting closed-loop system is stable, especially in cases of high consequence applications, where process variations or failure could result in unacceptable risk. Adaptive control techniques provide a proven methodology for designing stable controllers for systems that may possess a large amount of uncertainty. At the same time, the benefits of neural networks and fuzzy systems are generating much excitement-and impressive innovations-in almost every engineering discipline.Stable Adaptive Control and Estimation for Nonlinear Systems: Neural and Fuzzy Approximator Techniques brings together these two different but equally useful approaches to the control of nonlinear systems in order to provide students and practitioners with the background necessary to understand and contribute to this emerging field.The text presents a control methodology that may be verified with mathematical rigor while possessing the flexibility and ease of implementation associated with "intelligent control" approaches. The authors show how these methodologies may be applied to many real-world systems including motor control, aircraft control, industrial automation, and many other challenging nonlinear systems. They provide explicit guidelines to make the design and application of the various techniques a practical and painless process.Design techniques are presented for nonlinear multi-input multi-output (MIMO) systems in state-feedback, output-feedback, continuous or discrete-time, or even decentralized form. To help students and practitioners new to the field grasp and sustain mastery of the material, the book features:Background material on fuzzy systems and neural networksStep-by-step controller designNumerous examplesCase studies using "real world" applicationsHomework problems and design projects