Course Description: This course will offer the students an overview of the technology of autonomous mobile robotic systems and the mechanisms that allow a mobile robot to move through a real-world environment to perform its tasks. Since the design of any successful mobile robot involves the integration of many different disciplines -- among them kinematics, signal analysis, information theory, artificial intelligence, and probability theory -- the course will discuss all facets of mobile robotic system, including hardware design, wheel design, kinematics analysis, sensors and perception, localization, mapping, motion planning, navigation, and robot control architectures. Multi-robot systems will also be introduced due to their broader applications, such as search and rescue tasks, and exploring tasks.

Course Description: Advanced topics in autonomous and intelligent mobile robots, with emphasis on planning algorithms and cooperative control. Robot kinematics, path and motion planning, formation strategies, cooperative rules and behaviors. The application of cooperative control spans from natural phenomena of groupings such as fish schools, bird flocks, deer herds, to engineering systems such as mobile sensing networks, vehicle platoon.

Course Description: Methods for analysis and design of nonlinear control systems emphasizing Lyapunov theory. Second order systems, phase plane descriptions of nonlinerar phenomena, limit cycles, stability, direct and indirect method of Lyapunov, linearization, feedback linearization, Lyapunov-based design, and backstepping.

Course Description: An introduction to classic and modern feedback control that does not presume an undergraduate background in control. Transfer function and state space modeling of linear dynamic systems, closed-loop response, root locus, proportional, integral, and derivative control, compensators, controllability, observability, pole placement, linear quadratic cost controllers, and Lyapunov stability. MATLAB simulations in control system design.

Course Description: The theory of linear algebra with application to state space analysis. Topics include Cauchy-Binet and Laplace determinant theorems, and system of linear equations; linear transformations, basis, and rank; Gaussian elimination; LU and congruent transformations; Gramm-Schmidt; eigenvalues, eigenvectors, and similarity transformations; canonical forms; functions of matrices; singular value decomposition; generalized inverses; norm of a matrix; polynomial matrices; matrix differential equations; state space; and controllability and observability.

Course Description: Introduction to the theory and design of linear feedback and control systems in both digital and analog form, review of z-transform and Laplace transforms, time domain performance error of feedback systems, PID controller, frequency domain stability, including Nyquist stability in both analog and digital form, frequency domain performance criteria and design, such as via the gain and phase plots, state variable analysis of linear dynamical systems, elementary concepts of controllability, observability and stability via state space methods, and pole placement and elements of state variable design for single-input single-output systems.

Course Description:Signal acquisition procedures, instrumentation components, electronic amplifiers, signal conditioning, low-pass, high-pass, and band-pass filters, A/D converters and antialiasing filters, embedded control and instrumentation, microcontrollers, digital and analog I/O, instruments for measuring physical quantities such as motion, force, torque, temperature, pressure, etc., FFT and elements of modern spectral analysis, random signals, standard deviation and bias.