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  • 7:51 PM, Monday, 18 Oct 2021

Course Postgraduate
Semester Electives
Subject Code AVC865
Subject Title Machine learning and Control


Machine learning fundamentals: supervised learning – artificial neural networks, support vector machines, kernel methods, statistical techniques, recurrent (or feedback) neural networks; unsupervised learning – clustering, self organizing map, competitive learning, pre‐processing techniques (principal component analysis, singular value decomposition, independent component analysis); semisupervised learning – reinforcement learning

Applications to Control Problems: State estimation using neuro observer (single layer and multi layer), kalman filter and reinforcement learning;Identification of non‐linear dynamical systems using neural networks (state space models and input‐output models), support vector machines and reinforcement learning Modelling and (approximate solutions to) Optimal control problems using support vector machines, regression methods, monte‐carlo method, model predictive control and adaptive reinforcement learning

Robust control using differential neural networks, support vector machines and reinforcement learning Path planning using dynamic neural networks, density based machine learning techinques, support vector machines

Adaptive control using self organizing map or RBF networks, Trajectory tracking using dynamic (recurrent) neural networks.

Text Books

Same as Reference

  1. Frank Leroy Lewis, Suresh Jagannathan, A. Yeşildirek, Neural Network Control of Robot Manipulators And Non‐Linear Systems, Taylor and Francis group, 1999.
  2. Frank L. Lewis, Derong Liu, Reinforcement Learning and Approximate Dynamic Programming for Feedback Control, Wiley and IEEE press, 2013
  3. Zi‐Xing Cai, Intelligent Control: Principles, Techniques and Applications World Scientific, 1997.
  4. Bishop, C. M., Pattern Recognition and Machine Learning, Springer, 2006.
  5. Alexander S. Poznyak, Edgar N. Sanchez, Wen Yu, Differential Neural Networks for Robust Nonlinear Control Identification, State Estimation and Trajectory tracking, World Scientific, 2001.
  6. Alex Smola, S.V.N. Vishwanathan, Introduction to Machine Learning, Cambridge University Press, 2010.
  7. Simon Haykins, Neural Networks and Learning Machines, Prentice Hall, 2009.
  8. Related Research Articles from Journals and Conferences.