State space Approach: State space modeling of physical systems – diagonal and Jordan canonical forms – Solution of Linear Time Invariant (LTI) state equation – Cayley Hamilton theorem – Controllability and Observability Tests – Kalman decomposition technique – Controller design by state feedback – Full order/reduced order observer design – observer-based state feedback control – stability definitions in state space domain.
Adaptive control theory: System Identification – Frequency – Impulse – Step Response methods – Off-line – on line methods – Least square – Recursive least square – fixed memory – stochastic approximate method. MRAS & STC: The gradient approach – MIT rule Liapunov Functions – Pole placement control – minimum variance control – Predictive control.
Karl.J.Astrom, Bjorn Witten Mark, Adaptive Control, 2nd Ed., Pearson Education Pvt. Ltd.
M.Gopal, ‘Digital Control Systems and State Space Method’, 3rd Ed., TMH, 2008.
Katsuhiko Ogata, ‘Modern Control Engineering’, PHI ‐India, New Delhi 1989.
Fairman, ‘Linear Control Theory: State Space Approach’, John Wiley, 1998.
John S. Bay, ‘Fundamentals of Linear State Space Systems’, McGraw Hill, 1998.
Isermann R, ‘Digital Control System vol. I & II’, Narosa Publishing House, Reprint 1993.
Mendal JM, ‘Discrete Technique of Parameter Estimate’, Marcel Dekkas, New York, 1973.
Course Outcomes (COs):
CO1: Ability to Understanding the basics of state-space modelling which includes representation and solution to state space, physical significance of Eigenvalues and Eigenvectors
CO2: Analyze the controllability and observability with applications
CO3: Understanding the basics of adaptive Control and Various identification algorithms
CO4: Develop skills to design and analyse the Model Reference Adaptive Control and Self tuned Regulator