Adaptive Sliding Mode Neural Network Control for Nonlinear Systems introduces nonlinear systems basic knowledge, analysis and control methods, and applications in various fields. It offers instructive examples and simulations, along with the source codes, and provides the basic architecture of control science and engineering. - Introduces nonlinear systems' basic knowledge, analysis and control methods, along with applications in various fields - Offers instructive examples and simulations, including source codes - Provides the basic architecture of control science and engineering
Autorentext
Yang Li. Lecturer at the Institute of Information Science and Engineering, Hebei University of Science and Technology, Shijiazhuang, China. She obtained her PhD from Yanshan University, China in 2014. She was a visiting student of the University of the West of England in 2012. Her main research interests are in the areas of sliding mode control, neural network control, time delay system and control applications.
Klappentext
Adaptive Sliding Mode Neural Network Control for Nonlinear Systems introduces nonlinear systems basic knowledge, analysis and control methods, and applications in various fields. It offers instructive examples and simulations, along with the source codes, and provides the basic architecture of control science and engineering.
- Introduces nonlinear systems' basic knowledge, analysis and control methods, along with applications in various fields
- Offers instructive examples and simulations, including source codes
- Provides the basic architecture of control science and engineering
Inhalt
1. Basic Concepts 2. Nonlinear Systems Analysis Approach 3. Classical Nonlinear Systems Control 4. Advanced Nonlinear Systems Controller Design 5. Intelligent Methodology 6. Applications