Learning Objective:This objective of this course is to familiarize students with the cutting edge of practice in control systems design and analysis, almost all of which involves digital implementation. The students will gain familiarity with sampling and quantization, z-transform, and other analysis tools that are used to analyze and design digital control systems; familiarity with the state space and input/output representation, modeling and analysis of digital control systems; familiarity with modern control design methodologies for continuous-time and discrete-time systems that may include but not limited to: state feedback control, state observer design, observer based compensator design, LQ optimal control, Kalman filtering, LQG design, internal model based design, Linear Matrix Inequality based designs, nonlinear observers, feedback linearization, model predictive control; understanding the issues regarding digital controller implementation.
This course is the second in a two course series, ME575 and ME578. It is intended to facilitate the students to gain understanding in: sample theory, z-transform, and other analysis tools that are used to analyze and design digital control systems; Analysis: state space and input/output representation, modeling and analysis of digital control systems; Synthesis: emulation, I/O mapping design, state feedback control, state observer design, observer based compensator design, LQ optimal control, Kalman filtering, LQG design; Implementation: quantization, sampling and noise; of linear time-invariant (LTI) control system design and its extensions. It is intended to bridge between theory and application by bringing implementation issues into the consideration of controller design.
Spring 2019 Syllabus
Topics Covered:Introduction: Issues relating to digital control; Design process. Sample Theory: Sampling Theory; Aliasing; Zero-Order Hold (ZOH); z-Transform and Difference Equations; Properties; Difference Equation. Representation of Sample Data Systems: Pulse Transfer Function Representation; State Space Representation. Analysis of Sampled Data Systems: Stability; Sensitivity and Robustness; Controllability/ Observability; Pole/Zero Cancellation. Design of Discrete-Time Controller, Input/Output Approach: Emulating Continuous-Time Controller; Invariant Methods; Direct Design. Design of Discrete-Time Controller, Polynomial Approach: Problem Formulation; Pole Placement Design; Model Matching Problem. Design of Discrete-Time Controller, State Space Approach: State Feedback; State Estimation (Observer); Observer Based Compensator. LQ Optimal Control. LQG Control. Special Topics: LMI formulations of control, feedback linearization, nonlinear observers, and model predictive control will be shown toward the end of the course. Implementation Issues will be dealt with throughout the course.
Prerequisites:Modeling of (low-order, linear) continuous time physical systems. Laplace transform and related properties (IVT, FVT), transfer function representation. Block diagram and its algebra. Definition of poles/zeros and I/O Stability. Routh-Hurwitz criterion. Analysis and synthesis of continuous-time control systems using Root locus, Bode diagram, and Nyquist plot techniques. Design of classical control algorithms such as PID and lead-lag compensators. State space models and the definitions of controllability and observability.
Applied / Theory:50 / 50
Web Content:Syllabus, grades, lecture notes, solved problems, solutions, quizzes, message board, Course Blog, asynchronous or adaptive release of materials, measurement/tracking of assignment work times
Homework:There will be a weekly quiz and a weekly or biweekly project update for the duration of the course, with grading as given in the syllabus and schedule.
Projects:The course project will involve the modeling, analysis, design, realistic simulation or experimental verification (optional) of a physical system under digital control. There is no project presentation required. The student will need to submit an in-depth project updates and supporting data electronically. Will not visit off campus locations. Project updates (50% of overall grade) will be weekly or biweekly with ten updates over the semester due on the dates listed in the syllabus.
Must get passing project grade to pass course.