IC2021 OPTIMAL CONTROL SYLLABUS | ANNA UNIVERSITY BE I&C 8TH SEMESTER SYLLABUS REGULATION 2008 2011 2012-2013 BELOW IS THE ANNA UNIVERSITY 8TH SEMESTER B.E INSTRUMENTATION AND CONTROL ENGINEERING DEPARTMENT SYLLABUS, TEXTBOOKS, REFERENCE BOOKS,EXAM PORTIONS,QUESTION BANK,PREVIOUS YEAR QUESTION PAPERS,MODEL QUESTION PAPERS, CLASS NOTES, IMPORTANT 2 MARKS, 8 MARKS, 16 MARKS TOPICS. IT IS APPLICABLE FOR ALL STUDENTS ADMITTED IN THE YEAR 2011 2012-2013 (ANNA UNIVERSITY CHENNAI,TRICHY,MADURAI, TIRUNELVELI,COIMBATORE), 2008 REGULATION OF ANNA UNIVERSITY CHENNAI AND STUDENTS ADMITTED IN ANNA UNIVERSITY CHENNAI DURING 2009
IC2021 OPTIMAL CONTROL L T P C
3 0 0 3
UNIT I INTRODUCTION 9
Statement of optimal control problem – Problem formulation and forms of optimal control –
Selection of performance measures- Necessary conditions for optimal control – ontryagin’s
minimum principle – State inequality constraints – Minimum time problem.
UNIT II NUMERICAL TECHNIQUES FOR OPTIMAL CONTROL 9
Numerical solution of 2-point boundary value problem by steepest descent and Fletcher
Powell method solution of Ricatti equation by negative exponential and interactive
methods
UNIT III LQ CONTROL PROBLEMS AND DYNAMIC PROGRAMMING 9
Linear optimal regulator problem – Matrix Riccatti equation and solution method – Choice
of weighting matrices – Steady state properties of optimal regulator – Linear tracking
problem – LQG problem – Computational procedure for solving optimal control problems
– Characteristics of dynamic programming solution – Dynamic programming application
to discrete and continuous systems – Hamilton Jacobi Bellman equation.
107
UNIT IV FILTERING AND ESTIMATION 9
Filtering – Linear system and estimation – System noise smoothing and prediction –
Gauss Markov discrete time model – Estimation criteria – Minimum variance estimation –
Least square estimation – Recursive estimation.
UNIT V KALMAN FILTER AND PROPERTIES 9
Filter problem and properties – Linear estimator property of Kalman Filter – Time
invariance and asymptotic stability of filters – Time filtered estimates and signal to noise
ratio improvement – Extended Kalman filter – Case study: Boiler optimization and control.
TOTAL : 45 PERIODS
TEXT BOOKS
1. Kirk D.E., ‘Optimal Control Theory – An introduction’, Prentice hall, N.J., 1970
2. Sage, A.P., ‘Optimum System Control’, Prentice Hall N.H., 1968.
REFERENCES
1. Anderson, BD.O. and Moore J.B., ‘Optimal Filtering’, Prentice hall Inc., N.J., 1979.
2. S.M. Bozic, “Digital and Kalman Filtering”, Edward Arnould, London, 1979.
3. Astrom, K.J., “Introduction to Stochastic Control Theory”, Academic Press, Inc, N.Y.,
1970.
IC2021 OPTIMAL CONTROL L T P C
3 0 0 3
UNIT I INTRODUCTION 9
Statement of optimal control problem – Problem formulation and forms of optimal control –
Selection of performance measures- Necessary conditions for optimal control – ontryagin’s
minimum principle – State inequality constraints – Minimum time problem.
UNIT II NUMERICAL TECHNIQUES FOR OPTIMAL CONTROL 9
Numerical solution of 2-point boundary value problem by steepest descent and Fletcher
Powell method solution of Ricatti equation by negative exponential and interactive
methods
UNIT III LQ CONTROL PROBLEMS AND DYNAMIC PROGRAMMING 9
Linear optimal regulator problem – Matrix Riccatti equation and solution method – Choice
of weighting matrices – Steady state properties of optimal regulator – Linear tracking
problem – LQG problem – Computational procedure for solving optimal control problems
– Characteristics of dynamic programming solution – Dynamic programming application
to discrete and continuous systems – Hamilton Jacobi Bellman equation.
107
UNIT IV FILTERING AND ESTIMATION 9
Filtering – Linear system and estimation – System noise smoothing and prediction –
Gauss Markov discrete time model – Estimation criteria – Minimum variance estimation –
Least square estimation – Recursive estimation.
UNIT V KALMAN FILTER AND PROPERTIES 9
Filter problem and properties – Linear estimator property of Kalman Filter – Time
invariance and asymptotic stability of filters – Time filtered estimates and signal to noise
ratio improvement – Extended Kalman filter – Case study: Boiler optimization and control.
TOTAL : 45 PERIODS
TEXT BOOKS
1. Kirk D.E., ‘Optimal Control Theory – An introduction’, Prentice hall, N.J., 1970
2. Sage, A.P., ‘Optimum System Control’, Prentice Hall N.H., 1968.
REFERENCES
1. Anderson, BD.O. and Moore J.B., ‘Optimal Filtering’, Prentice hall Inc., N.J., 1979.
2. S.M. Bozic, “Digital and Kalman Filtering”, Edward Arnould, London, 1979.
3. Astrom, K.J., “Introduction to Stochastic Control Theory”, Academic Press, Inc, N.Y.,
1970.
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