Arizona State University Armando A. Rodriguez
ASU Professor 




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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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