Unit 1 Transfer Function Approach to Controller Design Structures and

10 lecture hours

• Specifications Control Structures
• Feed Forward Controller
• One Degree of Freedom Feedback Controller
• Two Degrees of Freedom Feedback Controller
• Proportional Control
• Nyquist Plot for Control Design
• Stability Margins InternalStabilityandRealizability
• Internal Stability
• Internal Stability Ensures Controller Realizability
• Closed Loop Delay Specification and Realizability
• Internal Model Principle and System Type
• Internal Model Principle
• System Type
• Introduction to Limits of Performance
• Time Domain Limits
• Sensitivity Functions
• Frequency Domain Limits
• Well Behaved Signals
• Small Rise Time in Response
• Small Overshoot in Response
• Large Decay Ratio
• Solving Aryabhatta’s Identity
• Euclid’s Algorithm for GCD of Two Polynomials
• Aryabhatta’s Identity
• Algorithm to Solve Aryabhatta’s Identity

Unit 2 Proportional, Integral, Derivative Controllers

6 lecture hours

• Sampling Revisited
• Discretization Techniques
• Area Based Approximation
• Step Response Equivalence Approximation
• Discretizationof PID Controllers
• Basic Design
• Ziegler–Nichols Method of Tuning
• 2-DOF Controller with Integral Actionat Steady State
• Bumpless PID Controller with Tc=Sc
• PID Controller with Filtering and Tc = Sc, 2-DOF PID Controller with Tc = Sc(1)
• 2-DOF PID Controller with Tc(1) = Sc(1)

Unit 3 Pole Placement Controllers

5 lecture hours

• Dead-Beat and Dahlin Control
• Pole Placement Controller with Performance Specifications
• Implementation of Unstable Controllers
• Internal Model Principle for Robustness
• Redefining Good and Bad Polynomials
• AntiWindup Controller
• PID Tuning Through Pole Placement Control

Unit 4 Special Cases of Pole Placement Control

4 lecture hours

• Smith Predictor
• Internal Model Control
• IMC Design for Stable Plants
• IMC in Conventional Form for Stable Plants
• PID Tuning Through IMC

Unit 5 Minimum Variance Control 08 18

• Program Name: Master of Engineering Level: PG Branch: Applied Instrumentation Subject Code : ME02067011 Subject Name : Digital Control j-Step Ahead Prediction Error Model
• Control Objective for ARMAX Systems
• Prediction Error Model Through Noise Splitting
• Interpretation of the Prediction Error Model
• Splitting Noise into Past and Future Terms
• ARIX Prediction Error Model
• ARIMAX Prediction Error Model
• Minimum Variance Controller
• Minimum Variance Controller for ARMAX Systems
• Expression for Sum of Squares
• Control Law for Nonminimum PhaseSystems
• Gene ralized Minimum Variance Controller
• GMVC for ARMAX Model
• GMVC for ARIMAX Model
• PID Tuning Through GMVC

Unit 6 Model Predictive Control

6 lecture hours

• Generalized Predictive Control
• GPC for ARIX Model
• ARIMAX Model
• Steady State Weighted Generalized Predictive Control (GPC)
• Model Derivation
• Tunedwith - GPC, Dynamic Matrix Control

Unit 7 State Space Techniques in Controller Design

6 lecture hours

• Pole Placement
• Ackermann’s Formula
• Control Law when System is not in Canonical Form
• Prediction Estimators
• Current Estimators
• Regulator Design – Combined Control Law and Estimator
• Linear Quadratic Regulator
• Formulation of Optimal Control Problem
• Solution to Optimal Control Problem
• Infinite Horizon Solution to LQR Design
• Kalman Filter