Arizona State University Armando A. Rodriguez
ASU Professor 




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Intelligent and Embedded Systems

 

This area of research focuses on the development of systematic design methods for the design of intelligent embedded systems.

Application Areas
Relevant application areas include:

  • Consumer electronics; e.g. PCs
  • Automotive electronics
  • Aerospace electronics
Relevant Control Challenges
Relevant control challenges include:
  • modeling and nominal uncertainty characterization
  • time delay (lag) associated with A/D
  • A/D resolution and quantization effects
  • time delay and lag associated with D/A /PWM
  • D/A/PWM resolution effects
  • lag due to anti-aliasing filter (AAF)
  • discrete-time (sample-data) controller design methodology
  • inner-outer hierarchical control system architecture
  • constraint enforcement issues
  • command following, disturbance attenuation, sensor noise attenuation
  • performance robustness
  • bandwidth management
  • controller complexity and implementation issues associated with embedded processor implementation
  • controller coefficient sensitivity issues
  • finite precision arithmetic (word length) effects within embedded processor; e.g. PC-104, field programmable gate array (FPGA)
  • controller-filter power consumption
  • power efficiency
  • parallel and hierarchical implementations
  • use of high-level rapid protoyping tools to generate executable stand alone code (turn-key solution)
  • controller maintenance and rapid update
  • user interface
  • controller adaptation; e.g. model- and signal-based adaptation
  • real-time modeling and uncertainty estimation via system identification and first principles  
  • mode selection; e.g. power up
  • health monitoring, fault detection, and fault tolerance issues
  • hardware-in-the-loop testing, validation, verification, and deployment
Objectives and Goals
The main objective of this research is to develop systematic methods for the design of flexible low cost embedded control systems with nominal performance/robustness guarantees that address each of the above challenges.

Approaches
Modern model-based robustness, system identification,  uncertainty estimation, and robust sample-data control system design theory. Developing a methodology which works for all aerospace systems is not realistic. Given this, we take a system-class-specific approach.
That is, we focus on specific classes of systems; e.g. specific types of aircraft or specific types of rotary aircraft.

Collaborators and Sponsors
Collaborators include: Professors Kostas Tsakalis, David Allee, Jennie Si (ASU, Electrical Engineering). This work has been supported by the National Science Foundation (NSF) and by ASU's Intelligent embedded Systems Laboratory (IeSL).

 

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