Course Details

Subject {L-T-P / C} : EE6304 : System Identification and Adaptive Control { 3-0-0 / 3}

Subject Nature : Theory

Coordinator : Dr. Arijit Guha

Syllabus

Introduction and overview of Systems Identification, Adaptive Control and applications. Parameter Estimation: Least Square, Generalized and Recursive Least Square, Estimator properties including error bounds and convergence, MES, ML and MAP estimators, Nonlinear Least Squares. Model Structures and Predictors. Recursive Identification of Linear dynamic systems: RLS, ELS, IV, RML, Stochastic Approximation, Extended Kalman Filter, generalized prediction error framework and its application to ARMA and state models, convergence analysis, Time varying parameters. Nonlinear System Identification. Adaptive schemes. Adaptive control theory. Applications. Situations when constant Gain feedback is insufficient. Robust control. The adaptive control problem. The model following problem. MRAS based on stability theory. Model following when the full state is measurable. Direct MRAS for general linear systems. Prior knowledge in MRAS. MRAS for partially known systems. Use of robust estimation methods in MRAS. The basic idea. Indirect self-tuning regulators. Direct Self-tuning regulators. Linear Quadratic STR. Adaptive Predictive control. Prior knowledge in STR. Linear-in-the-parameters model. Least squares estimation. Experimental conditions. Recursive estimators. Extended least squares. Robust estimation methods (dead zone, projection).Implementation issues. Nonlinear System Identification Techniques

Course Objectives

Course Outcomes

1. Design and implement system identification experiments. <br />2. Use input-output experimental data for identification of mathematical dynamical models. <br />3. Use system identification methods to design adaptive controllers. <br />4. Explain the advantages and disadvantages of adaptive control relative to other control approaches

Essential Reading

Supplementary Reading