Intelligent and Embedded Systems
This area of research focuses on the development of systematic design methods for the design of intelligent embedded systems.
Relevant application areas include:
Relevant Control Challenges
- Consumer electronics; e.g. PCs
- Automotive electronics
- Aerospace electronics
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
Objectives and Goals
- 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
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.
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).