ET4005 Intelligent Control and Automation Syllabus:
ET4005 Intelligent Control and Automation Syllabus – Anna University PG Syllabus Regulation 2021
COURSE OBJECTIVES
To Impart the knowledge of various optimization techniques and hybrid schemes.
To introduce the concept, Analysis and implementation of ANN and Fuzzy logic controllers.
To Emphasis the need for Genetic algorithm and its role for automation.
To provide the basics of automation and its requirements
To demonstrate the role of Intelligent controller in automation applications.
UNIT I ARTIFICIAL NEURAL NETWORK & FUZZY LOGIC
ARTIFICIAL NEURAL NETWORK: Learning with ANNs, single-layer networks, multi-layer perceptrons, Back propagation algorithm (BPA) ANNs for identification, ANNs for control, Adaptive neuro controller. Fuzzy Logic Control: Introduction, fuzzy sets, fuzzy logic, fuzzy logic controller design, Fuzzy Modelling & identification, Adaptive Fuzzy Control Design.
UNIT II GENETIC ALGORITHM
Basic concept of Genetic algorithm and detail algorithmic steps- Hybrid genetic algorithm – Solution for typical control problems using genetic algorithm. Concept on some other search techniques like Tabu search, Ant-colony search and Particle Swarm Optimization
UNIT III HYBRID CONTROL SCHEMES
Fuzzification and rule base using ANN–Neuro fuzzy systems-ANFIS–Optimization of membership function and rule base using Genetic Algorithm and Particle Swarm Optimization.
UNIT IV AUTOMATION
Introduction to Automation – Automation in Production System, Principles and Strategies of Automation, Basic Elements of an Automated System, Advanced Automation Functions, Levels of Automations- Industrial Automation -computer vision for automation- PLC and SCADA based Automation- IoT for automation- Industry 4.0.
UNIT V INTELLIGENT CONTROLLER FOR AUTOMATION APPLICATION
Applications of Intelligent controllers in Industrial Monitoring, optimization and control- Smart Appliances- Automation concept for Electrical vehicle- Intelligent controller and Automation for Power System.
TOTAL: 45 PERIODS
COURSE OUTCOMES:
At the end of this course, the students will have the ability in
CO1: Demonstrate the basic architectures of NN and Fuzzy logics
CO2: Design and implement GA algorithms and know their limitations.
CO3: Explain and evaluate hybrid control schemes and PSO
CO4: Interpret the significance of Automation concepts.
CO5: Develop the intelligent controller for automation applications.
REFERENCES:
1. Laurene V.Fausett, “Fundamentals of Neural Networks, Architecture, Algorithms, and Applications”, Pearson Education, 2008.
2. Timothy J.Ross, “Fuzzy Logic with Engineering Applications”, Wiley, Third Edition, 2010.
3. David E.Goldberg, “Genetic Algorithms in Search, Optimization, and Machine Learning”, Pearson Education, 2009.
4. W.T.Miller, R.S.Sutton and P.J.Webrose, “Neural Networks for Control”, MIT Press, 1996.
5. Srinivas Medida, Pocket Guide on Industrial Automation for Engineers and Technicians, IDC Technologies.
6. ChanchalDey and Sunit Kumar Sen, Industrial Automation Technologies, 1st Edition, CRC Press, 2022.