Intelligent Control (Key Technology of AI) and Its Application
Artificial intelligence (AI) is a branch of computer science that has been known as one of the world's three cutting-edge technologies (space technology, energy technology and artificial intelligence) since the 1970s. It is also considered one of the three cutting-edge technologies of the 21st century (genetic engineering, nanoscience and artificial intelligence). Intelligent control is the key technology of artificial intelligent.
Sample Chapter(s)
Preface (97 KB)
Components of the Book:
  • Preface
  • Chapter 1 Introduction
    • 1.1 Overview of intelligent control
    • 1.2 The main types of intelligent control
    • Reference
  • Chapter 2. Recognition of artificial ripening tomato and nature mature tomato based on the double parallel genetic neural network
    • 2.1 Hardware of recognition system
    • 2.2 Extraction of color feature
    • 2.3 Double parallel neural network and genetic algorithm
    • 2.4 Weight adjustment of hidden layer to output layer
    • 2.5 System test
    • Reference
  • Chapter 3. Egg freshness recognition based on a fuzzy radial-basis-function neural network technology
    • 3.1 Material and methods
    • 3.2 Construction of fuzzy RBF neural network
    • 3.3 System design
    • 3.4 Results
    • Reference
  • Chapter 4. Backstepping adaptive fuzzy control of servo system with LuGre friction
    • 4.1 System modeling
    • 4.2 Backstepping adaptive fuzzy control
    • 4.3 Simulation experiment
    • References
  • Chapter 5. RBFNNBA control of electromechanical servo system based on the LuGre model
    • 5.1 System model
    • 5.2 Controller design
    • 5.3 Simulation analysis
    • Reference
  • Chapter 6. RBF neural network-based output constrained control of servo system with LuGre friction
    • 6.1 System model
    • 6.2 Controller design
    • 6.3 Simulation results
    • Reference
  • Chapter 7. Overall design of dual-motor drive servo system
    • 7.1 Composition of servo system
    • 7.2 Design of driving scheme
    • 7.3 Design of control computer system
    • 7.4 Position detection component
    • Reference
  • Chapter 8. Modeling of dual-motor driving servo system
    • 8.1 Linear part modeling
    • 8.2 Nonlinear part modeling
    • 8.3 System model
    • Reference
  • Chapter 9. Intelligent PID control
    • 9.1 Intelligent PID controller
    • 9.2 Simulation results
    • Reference
  • Chapter 10. Fuzzy PI control
    • 10.1 System control principle
    • 10.2 Theoretical analysis of synchronous linkage control
    • 10.3 The design of fuzzy PI controller
    • 10.4 Fuzzification of accurate quantity
    • 10.5 Fuzzy control Rules
    • 10.6 Fuzzy Decision
    • 10.7 Simulation results
    • References
  • Chapter 11. Fuzzy RBF NN control
  • Chapter 12. Genetic RBF NN control
    • 12.1 System model
    • 12.2 RBF NN adaptive control
    • 12.3 Optimization of the hidden layer center value and width by genetic algorithm
    • 12.4 Adjustment of weights from hidden layer to output layer
    • 12.5 Simulation results
    • References
  • Chapter 13. Backstepping adaptive control
    • 13.1 System model 1
    • 13.2 Controller design 1
    • 13.3 Simulation results 1
    • 13.4 Experiment results 1
    • 13.5 System model 2
    • 13.6 Controller design 2
    • 13.7 Simulation results 2
    • 13.8 System model 3
    • 13.9 Controller design 3
    • 13.10 Simulation results 3
    • References
  • Chapter 14. Backstepping integral adaptive control
    • 14.1 System model
    • 14.2 Controller design
    • 14.3 Simulation results
    • References
  • Chapter 15. All-coefficient adaptive control
    • 15.1 Characteristic model
    • 15.2 Controller design
    • 15.3 Simulation results
    • References
  • Chapter 16. Fuzzy parameter approximation-based improved backstepping adaptive control
    • 16.1 System model
    • 16.2 Backstepping adaptive control based on fuzzy parameter approximation
    • 16.3 Simulation results
    • References
  • Chapter 17. RBF NN backstepping adaptive control
    • 17.1 System model
    • 17.2 Controller design
    • 17.3 Simulation and experiment analysis
    • References
  • Chapter 18. Projection algorithm-based dynamic surface control
    • 18.1 System model
    • 18.2 Controller design
    • 18.3 Stability analysis
    • 18.4 Simulation results
    • 18.5 Experiment results
    • Reference
  • Chapter 19. Fuzzy sliding mode adaptive control
    • 19.1 System model
    • 19.2 Controller design
    • 19.3 Simulation results
    • Reference
  • Chapter 20. Adaptive robust control of a class of uncertain nonlinear systems based on backstepping
    • 20.1 Problem description
    • 20.2 Controller design
    • 20.3 Simulation study
    • Reference
  • Chapter 21. RBF NN-based backstepping adaptive control for a class of nonlinear systems
    • 21.1 Problem description
    • 21.2 Controller design and stability analysis
    • 21.3 Simulation analysis
    • Reference
Readership: Students, academics, teachers and other people attending or interested in Artificial intelligence.
1
Preface
Haibo Zhao
PDF (97 KB)

Chapter 1 Introduction
Haibo Zhao
PDF (149 KB)

Chapter 2. Recognition of artificial ripening tomato and nature mature tomato based on the double parallel genetic neural network
Haibo Zhao
PDF (417 KB)

Chapter 3. Egg freshness recognition based on a fuzzy radial-basis-function neural network technology
Haibo Zhao
PDF (546 KB)

Chapter 4. Backstepping adaptive fuzzy control of servo system with LuGre friction
Haibo Zhao
PDF (864 KB)

Chapter 5. RBFNNBA control of electromechanical servo system based on the LuGre model
Haibo Zhao
PDF (1536 KB)

Chapter 6. RBF neural network-based output constrained control of servo system with LuGre friction
Haibo Zhao
PDF (233 KB)

Chapter 7. Overall design of dual-motor drive servo system
Haibo Zhao
PDF (149 KB)

Chapter 8. Modeling of dual-motor driving servo system
Haibo Zhao
PDF (255 KB)

Chapter 9. Intelligent PID control
Haibo Zhao
PDF (252 KB)

Chapter 10. Fuzzy PI control
Haibo Zhao
PDF (1726 KB)

Chapter 11. Fuzzy RBF NN control
Haibo Zhao
PDF (528 KB)

Chapter 12. Genetic RBF NN control
Haibo Zhao
PDF (309 KB)

Chapter 13. Backstepping adaptive control
Haibo Zhao
PDF (407 KB)

Chapter 14. Backstepping integral adaptive control
Haibo Zhao
PDF (289 KB)

Chapter 15. All-coefficient adaptive control
Haibo Zhao
PDF (266 KB)

Chapter 16. Fuzzy parameter approximation-based improved backstepping adaptive control
Haibo Zhao
PDF (1174 KB)

Chapter 17. RBF NN backstepping adaptive control
Haibo Zhao
PDF (2442 KB)

Chapter 18. Projection algorithm-based dynamic surface control
Haibo Zhao
PDF (1470 KB)

Chapter 19. Fuzzy sliding mode adaptive control
Haibo Zhao
PDF (259 KB)

Chapter 20. Adaptive robust control of a class of uncertain nonlinear systems based on backstepping
Haibo Zhao
PDF (259 KB)

Chapter 21. RBF NN-based backstepping adaptive control for a class of nonlinear systems
Haibo Zhao
PDF (226 KB)
Haibo Zhao
Tongling University. A scientific researcher in China. His research focuses on intelligent control and intelligent systems. He holds an M.Eng in control theory and control engineering from Nanjing University of Science and Technology, China, and a B.Eng in automation from Anhui Polytechnic University, China. Now he is a professor working in Tongling University, China.
This Book

250pp. Published November 2024

Scientific Research Publishing

Category:Computer Science & Communications

ISBN: 979-8-89507-952-2

(Hardcover) USD 319.00

ISBN: 979-8-89507-953-9

(E-Book) USD 63.00

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