Customers who bought this item also bought

• Predictive Control in Process Engineering - From the Basics to the Applications
• Reinforcement Learning and Approximate Dynamic Programming for Feedback Control. IEEE Press Series on Computational Intelligence
• Predictive Control in Process Engineering: From the Basics to the Applications
• Kernel Adaptive Filtering. A Comprehensive Introduction. Adaptive and Learning Systems for Signal Processing, Communications and Control Series
• Optimal Control. 3rd Edition
• Statistical Analysis of Profile Monitoring. Wiley Series in Probability and Statistics
• Adaptive Inverse Control. A Signal Processing Approach Reissue Edition
• Adaptive Signal Processing. Next Generation Solutions. Adaptive and Learning Systems for Signal Processing, Communications and Control Series
• Fuzzy Control and Identification
• Dynamic Modeling and Predictive Control in Solid Oxide Fuel Cells. First Principle and Data-based Approaches

Croatia's FC Rating Affirmed by S&P; Outlook Stable Jun 98. Edition No. 1

VDM Publishing House, January 2010, Pages: 124

Adaptive tracking of nonlinear dynamic plants is currently an important area of research. The main difficulty being felt by the research community is the lack of a general modelling framework that can facilitate synthesis of a simplistic control law, while being capable of providing accurate approximation of nonlinear systems. The aim of this study is to alleviate that problem by introducing a novel technique based on the control-oriented U- Model for the adaptive tracking of a wide range of stable nonlinear dynamic plants using only input- output data. The overall scheme is based on the robust internal model control (IMC) structure wherein different internal models, using nonlinear adaptive filtering and higher-order neural networks, are used. In each case, the U-Model equivalence of the internal model is developed and a simplistic control law based on polynomial root-solving is synthesized. The effectiveness of the proposed adaptive schemes is demonstrated through simulations and real-time applications to a variety of nonlinear plants.

Naveed Razzaq, Butt.
Naveed R. Butt is a doctoral candidate at the department of Mathematical Statistics at Lund University, Sweden. He earned his BS degree in Engineering Sciences from the GIK Institute, Pakistan and a Master's degree in Systems Engineering from KFUPM, Saudi Arabia. His research interests include statistical signal processing and nonlinear control