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.

Syllabus

Lecture Materials:

• Lecture 1 slides

Homework Solutions:

• Homework #1 solutions can be found here and here
• Homework #2 solutions can be found here
• Homework #3 solutions
• Homework #4 solutions: Section 6.1, Section 6.2, Section 6.6,
• Homework #5 solutions
• Homework #7 solutions
• Homework #8 solutions
• Homework #9 solutions

Final Exam Solutions:

• Final Exam Solutions

Sample Tests:

• Sample MidTerm Exam
• Sample MidTerm Exam
• Sample Final Exam
• Sample Final Exam
• Sample Final Exam

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.

Syllabus

Lecture slides:

• Lecture 1: Introduction to Autonomous Mobile Robots
• Lecture 2: Introduction to Multi-Robot Systems
• Lecture 3: Path Planning Algorithms
• Lecture 4: Multi-Robot Motion Planning
• Lecture 5: Collision Avoidance in Dynamic Environments
• Lecture 6: Cooperative Control of Large Systems
• Lecture: Cooperative Formation Tracking of Multi-Vehicle Systems

Reading List:

• Paper 1: Guest Editorial: Advances in MultiRobot Systems, by T. Arai, E. Pagello, and L. E. Parker, IEEE Transactions on Robotics and Automation 18(5): 655-661, 2002.
• Paper 2: Cooperative Mobile Robotics: Antecedents and Directions, by Cao, Fukunaga, and Kahng, Autonomous Robots 4(1): 7-27, 1997.
• Paper 3: Optimal and efficient path planning for partially-known environments, by A. Stentz, Proceedings of 1994 IEEE International Conference on Robotics and Automation, Page(s):3310 - 3317 vol.4, 8-13 May 1994.
• Paper 4: The Focussed D* Algorithm for Real-Time Replanning, by A. Stentz, Proceedings of the International Joint Conference on Artificial Intelligence, August, 1995.
• Paper 5: A distributed and optimal motion planning approach for multiple mobile robots, by Y. Guo and L. Parker, Proceedings of the 2002 IEEE International Conference on Robotics and Automation, pp. 2612-2619, Washington D.C., May, 2002.
• Paper 6: A new analytical solution to mobile robot trajectory generation in the presence of moving obstacles, by Zhihua Qu, Jing Wang, Plaisted, C.E., IEEE Transactions on Robotics, Volume 20, Issue 6, Dec. 2004 Page(s):978 - 993 .
• Paper 7: Cooperative Control of Large Systems, by M. E. Khatir and E. J. Davison, IN Cooperative Control, A Post-Workshop Volume 2003 Block Island Workshop on Cooperative Control, Edited by V. Kumar, N. Leonard, A. S. Morse, page 119-136.
• Paper 8: Exploring Complex Networks, by S. H. Strogatz, Nature 410, 268-276 (8 March 2001).
• Paper 9: Consensus Problems in Networks of Agents With Switching Topology and Time-Delays, by R. Olfati-Saber and R. M. Murray, IEEE Transactions on Automatic Control, Vol. 49, No. 9, pp. 1520-1533, 2004.
• Paper 10: Decentralized control of vehicle formations, by G. Lafferriere.,1,A.Williams1, J. Caughman, J.J.P. Veerman, Systems and Control Letters, Vol. 54, pp. 899-910, 2005.

Source codes:

• Formation control:The main file, and the def function.

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

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: 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: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.