(3 units). Review of State space modeling concepts, controllability, observability, poles and zeros. Minimal realizations. State and output feedback, spectral assignability, pole placement techniques, full and reduced order observers, separation principle, compensator design using observers. Introduction to optimal control, linear quadratic problem, algebraic Riccati equations, Kalman filtering. Introduction to nonlinear control. Applications to fuzzy systems, neural networks, genetic algorithms, and chaotic systems. Course Component: Laboratory, Lecture, Tutorial Prerequisite: ELG 3155.
(3 units). Review of State space modeling concepts, controllability, observability, poles and zeros. Minimal realizations. State and output feedback, spectral assignability, pole placement techniques, full and reduced order observers, separation principle, compensator design using observers. Introduction to optimal control, linear quadratic problem, algebraic Riccati equations, Kalman filtering. Introduction to nonlinear control. Applications to fuzzy systems, neural networks, genetic algorithms, and chaotic systems. Course Component: Laboratory, Lecture, Tutorial Prerequisite: ELG 3155.
