• AE 6252, Smart Structure Control – Modeling smart sensors and actuators, development of closed loop models, design of controllers, validation of controllers, application to vibration control, noise control, and shape control. Prerequisite: AE 6230. (3 semester hours)
• AE 6504, Modern Methods of Flight Control – Linear quadratic regulator design. Model following control. Stochastic control. Fixed structure controller design. Applications to aircraft flight control. Prerequisite: AE 3521. (3 semester hours)
• AE 6505, Kalman Filtering – Probability and random variables and processes; correlation; shaping filters; simulation of sensor errors; Wiener filter; random vectors; covariance propagation; recursive least-squares; Kalman filter; extensions. Prerequisite: AE 3515. (3 semester hours)
• AE 6506, Guidance and Navigation – Earth's shape and gravity. Introduction to inertial navigation. GPS aiding. Error analysis. Guidance systems. Analysis of the guidance loop. Estimation of guidance variables. Adjoint analysis. Prerequisite: AE 3521. (3 semester hours)
• AE 6511, Optimal Guidance and Control – Euler-Lagrange formulation; Hamilton-Jacobi approach; Pontryagin's minimum principle; Systems with quadratic performance index; Second variation and neighboring extremals; Singular solutions; numerical solution techniques. Prerequisite: AE 3515. (3 semester hours)
• AE 6531, Robust Control I – Robustness issues in controller analysis and design. LQ analysis, H2 norm, LQR, LQG, uncertainty modeling, small gain theorem, H-infinity performance, and the mixed-norm H2/H-infinity problem. Prerequisite: ECE 6550. (3 semester hours)
• AE 6532, Robust Control II – Advanced treatment of robustness issues. Controller analysis and design for linear and nonlinear systems with structured and non-structured uncertainty. Reduced-order control, stability, multipliers, and mixed-mu. Prerequisite: ECE 6531. (3 semester hours)
• AE 6534, Control of AE Structures – Advanced treatment of control of flexible structures. Topics include stability of multi-degree-of-freedom systems, passive and active absorbers and isolation, positive real models, and robust control for flexible structures. Prerequisite: ECE 6230, ECE 6531. (3 semester hours)
• AE 6580, Nonlinear Control – Advanced treatment of nonlinear robust control. Lyapunov stability theory, absolute stability, dissipativity, feedback linearization, Hamilton-Jacobi-Bellman theory, nonlinear H-infinity, backstepping control, and control Lyapunov functions. Prerequisite: ECE 6550. (3 semester hours)
• ECE 6550*, Linear Systems and Controls – Introduction to linear system theory and feedback control. Topics include state space representations, controllability and observability, linear feedback control. Prerequisite: Graduate Standing. (3 semester hours)
• ECE 6551, Digital Controls - Techniques for analysis and synthesis of computer-based control systems. Design projects provide an understanding of the application of digital control to physical systems. Prerequisites: ECE 6550 Minimum Grade of D. (3 semester hours)
• ECE 6552, Nonlinear Systems and Control - Classical analysis techniques and stability theory for nonlinear systems. Control design for nonlinear systems, including robotic systems. Includes design projects. Prerequisites: ECE 6550 Minimum Grade of D. (3 semester hours)
• ECE 6553, Optimal Control and Optimization - Optimal control of dynamic systems, numerical optimization, techniques and their applications in solving optical-trajectory problems. Prerequisites: ECE 6550 Minimum Grade of D. (3 semester hours)
• ECE 6554, Adaptive Control - Methods of parameter estimation and adaptive control for systems with constant or slowly varying unknown parameters. Includes MATLAB design projects emphasizing applications to physical systems. Prerequisites: ECE 6550 Minimum Grade of D. (3 semester hours)
• ECE 6555, Optimal Estimation - Techniques for signal and state estimation in the presence of measurement and process noise with the emphasis on Wiener and Kalman filtering. Prerequisites: ECE 6550 Minimum Grade of D. (3 semester hours)
• ECE 6559, Advanced Linear Systems - Study of multivariable linear system theory and robust control design methodologies. Prerequisites: ECE 6550 Minimum Grade of D. (3 semester hours)
• ME 6401*, Linear Control Systems – Theory and applications of linear systems, state space, stability, feedback controls, observers, LQR, LQG, Kalman Filters. Prerequisite: ME 3015 or equivalent, or with the consent of the instructor. (3 semester hours)
• ME 6402, Nonlinear Control Systems – Analysis of nonlinear systems, geometric control, variable structure control, adaptive control, optimal control, applications. Prerequisite: ME 6401 or equivalent, or with the consent of the instructor. (3 semester hours)
• ME 6403, Digital Control Systems – Comprehensive treatment of the representation, analysis, and design of discrete-time systems. Techniques include Z- and W- transforms, direct method, control design, and digital tracking. Prerequisite: ME 3015 or equivalent, or with the consent of the instructor. (3 semester hours)
• ME 6404, Advanced Control System Design and Implementation – Analysis, synthesis and implementation techniques of continuous-time and real-time control systems using classical and state-space methods. Prerequisite: ME 6403 or equivalent, or with the consent of the instructor. (3 semester hours)

• CS 3600, Introduction to Artificial Intelligence - An introduction to artificial intelligence and machine learning. Topics include intelligent system design methodologies, search and problem solving, supervised and reinforced learning. Prerequisites: CS 1332
• CS 6601*, Artificial Intelligence - Basic concepts and methods of artificial intelligence including both symbolic/conceptual and numerical/probabilistic techniques. Prerequisites: CS 2600
• CS 7612, AI Planning - Symbolic numerical techniques that allow intelligent systems to decide how they should act in order to achieve their goals, including action and plan representation, plan synthesis and reasoning, analysis of planning algorithms, plan execution and monitoring, plan reuse and learning, and applications. Prerequisites: CS 6601
• CS 7640, Learning in Autonomous Agents - An in-depth look at agents that learn, including intelligent systems, robots, and humans. Design and implementation of computer models of learning and adaptation in autonomous intelligent agents. Prerequisites: CS 3600 or CS 4641
• CS 7641 Machine Learning - Machine learning techniques and applications. Topics include foundational issues; inductive, analytical, numerical, and theoretical approaches; and real-world applications. Prerequisites: CS 6601
• ECE 6556, Intelligent Control – Principles of intelligent systems and their utility in modeling, identification, and control of complex systems; neuro-fuzzy tools applied to supervisory control; hands-on laboratory experience. Prerequisites: ECE 6550 Minimum Grade of D. (3 semester hours)

These courses form a two semester sequence with similar “laboratory-rotation” formats. Each course requires the student to complete a semester-long research project under the guidance of at least two faculty members from distinct participating schools (AE, BME, CoC, ECE, or ME). The courses are designed to expose students to the discipline of research in a structured way and to encourage novel ideas in a multidisciplinary context.

The desired learning outcome is to foster a multidisciplinary research approach in the student by:

• Critically assessing the prior art in an area outside his/her own,
• Performing state-of-the-art experimental or simulation work in a multidisciplinary area,
• Coherently reporting, at the level of a conference publication, on the research performed.
  

The evaluation component includes:

• Week 2, a one page proposal outlining the proposed work along with a well-argued motivation;
• Week 5: a detailed written review paper of the state-of-the art in the area;
• Week 10: a written report on the experimental or simulation component in progress;
• Week 14: a final report and presentation that includes all of the above along with a discussion of the work done and opportunities for future work.
  

All deliverables will be graded by both faculty advisors as well as reviewed to comply with the evaluation criteria set by the Robotics Program Committee.