01-01-2013, 12:16 PM
Biomimicry, Mathematics, and Physics for Control and Automation: Conflict or Harmony?
Biomimicry vs. Math/Physics?
Intelligent control = emulation of biological control processes for solving automation problems
Examples:
Neural networks (e.g., balancing, walking, learning)
Fuzzy systems (e.g., automated driving)
Conventional control = develop mathematical models, synthesize controllers, nonlinear analysis.
Relations between these?
Advantages? Disadvantages?
A framework for discussion
Scientific, pragmatic-engineering, include theory/simulation/experimental views (even if outside your talents)
No “pet-approaches”
No hype and marketing
Problems with these issues on all sides…
We use science, heuristics, and technology (computing, sensing, etc.)
Uncertainty Principle: Coping with Uncertainty is the Essence of the Problem
Many types of uncertainty
Math/physics: Improve models (representing uncertainty), generalize synthesis methods
Biomimicry: “Can we learn something from the most robust autonomous systems?”
Math/physics applies to biomimicry-based methods, and can learn from it. Harmony!
Fuzzy/Neural Vs. Conventional Control
Fuzzy: Heuristic nonlinear controller synthesis. Avoid need for models? Are models used?
Problems: Ignore physics/useful model information, models as a comparative tool, indiscriminate application of the approach, where is success?
Similar in philosophy to PID control in industry!
Good:Acknowledgement of role of heuristics that are used for almost all implementations (e.g., 9/10 exceptions/rules, 1/10 regulator)