Robust Control of Multiple-Input Multiple-Output (MIMO) Dynamical Systems/Processes
The focus of this research is on the development of control system design methodologies for multiple-input multiple-output (MIMO) systems/processes operatin in the presence of significant uncertain nonlinearities, uncertain dynamics, and parametric uncertainty.
Application Area
Relevant application areas include:
- Aerospace systems; e.g. Interacting aero-thermo-elastic-propulsion dynamics
- Robotic and other electromechanical systems
- Semiconductor and thermal processes; e.g.active cooling of microprocessor systems
Relevant Control Challenges
Relevant control challenges include:
- uncertain nonlinearities (e.g. aero-thermo-elastic-propulsion),
- hard nonlinearities (e.g. control position and rate saturation nonlinearities),
- uncertain (typically high-frequency) dynamics,
- parametric uncertainty,
- uncertain actuator and sensor dynamics,
- MIMO dynamical coupling/interactions (e.g. aero-propulsion),
- satisfying multivariable decoupling specifications,
- satisfying channel-specific bandwidth specifications,
- satisfying MIMO directionality specifications,
- digital, sample-data, and multi-rate embedded system implementation issues,
- controller complexity and implementation issues,
- stabilization,
- following of varying (typically low frequency) reference commands,
- attenuation of (stochastic, typically low frequency) disturbances,
- attenuation of (stochastic, typically high frequency) measurement noise,
- state estimation,
- parameter and uncertainty estimation (system identification).
Objectives and Goals
The main objective of this research is to develop a systematic design methodology which addresses each of the above control system design challenges. A major goal here is the development of tools that can be used by practicing engineers to design "full envelop" MIMO control systems.
Approaches
Quasi-linear parameter varying (LPV) systems, generalized predictive control (GPC) and model predictive control (MPC). Model- and performance-based optimization is the main design approach.
Collaborators and Sponsors
Collaborators include:
- Professor Petros Voulgaris (University of Illinois, Urbana-Champaign; Aerospace Engineering)
- Dr. Brett Ridgely (Raytheon Missile Systems, Sr Department Manager, Autopilot Design Department, GNC Technology Director, Tucson, AZ)
- Professor Jeff Shamma (UCLA; Mechanical Engineering)
This work has been sponsored by the following organizations:
- National Science Foundation (NSF), the Consortium for Embedded and Inter-Networking Technologies (CEINT), AFOSR, Eglin AFB, Honeywell, Boeing, NASA.
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