SBIR Phase I: MatLab Based Toolbox for Promoting Engineering Education of L1 Adaptive Control Theory

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This Small Business Innovation Research (SBIR) Phase I project will develop a MATLAB based toolbox that can be used in educational laboratory environments for the training of the next generation of engineers with modern adaptive control theory, and will be performed by CU Aerospace (CUA) and team partner the University of Illinois at Urbana-Champaign (UIUC). The existing MATLAB toolboxes for control system design that use the dated theoretical advances from 1980?s and 90?s are suitable for applications in linear systems. However, the existing toolboxes cannot effectively serve the purpose of control design for complex systems in the presence of uncertainties. The key feature of this breakthrough L1 adaptive control theory is the separation between identification and control. It leads to decoupling of adaptation from robustness and ensures uniform and predictable response in the presence of unpredictable failures and disturbances. The CUA-UIUC team will build a commercial toolbox that captures the recent advances in control theory having routines for implementation of L1 adaptive controllers, and apply the new toolbox in laboratory classroom environments to train our future generation of scientist-engineers.The broader impact/commercial potential of this project is that the development of the L1 Adaptive Control Toolbox will serve as the ideal tool to transition this modern control theory to a much wider technical audience both through continuing education of practicing professionals and engineering students. Within a very short time span, the architectures of this powerful theory for robust adaptive control of complex systems has supported a large number of very challenging and ambitious experiments including: (i) flight tests of a subscale commercial jet (GTM-AirSTAR) at NASA, including post-stall flight regimes; (ii) control of nuclear plants; (iii) coordination of multiple unmanned autonomous vehicles in time-critical missions within given spatial constraints using vision-based sensors; (iv) pressure control in drilling applications of oil production; control of fiber optics; (v) networked control systems in the presence of various failures; (vi) control of anesthesia; (vii) iterative learning control in the presence of unpredictable disturbances; (viii) aerobiological sampling; control of smart materials in the presence of hysterisis; (ix) aerial refueling of multiple UAVs in turbulence; (x) control of flexible aircraft; and (xi) control of rotorcraft. The development of this toolbox into a commercial product will serve as a springboard for CUA to develop systematic design methods for broader applications of this theory to industry and many governmental departments including DOE, DOD, DOT, and NASA.