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

这个小型企业创新研究(SBIR)第一阶段项目将开发一个基于MATLAB的工具箱，可用于教育实验室环境中，用于培训具有现代自适应控制理论的下一代工程师，并将由加州大学航空航天(CUA)及其团队合作伙伴伊利诺伊大学香槟分校(UIUC)执行。现有的MatLab控制系统设计工具箱采用了1980-S和90-S的最新理论成果，适合于线性系统的应用。然而，现有的工具箱不能有效地服务于存在不确定性的复杂系统的控制设计。这一突破性的L1自适应控制理论的主要特点是辨识与控制分离。它使适应性与稳健性脱钩，并确保在出现不可预测的故障和干扰时作出一致和可预测的反应。CUA-UIUC团队将构建一个商业工具箱，该工具箱捕捉控制理论的最新进展，具有实施L1自适应控制器的例程，并在实验室课堂环境中应用新工具箱来培训我们的未来一代科学家-工程师。该项目的更广泛影响/商业潜力是，L1自适应控制工具箱的开发将作为理想的工具，通过对实践专业人员和工程专业学生的继续教育，将现代控制理论转移到更广泛的技术受众。在极短的时间跨度内，这一强大的复杂系统自适应控制理论的体系结构已经支持了大量极具挑战性和雄心勃勃的实验，包括：(1)在美国国家航空航天局(NASA)对小型商用喷气式飞机(GTM-AirStar)的飞行测试，包括失速飞行制度；(2)核电厂的控制；(3)使用基于视觉的传感器在给定空间约束下协调多个无人驾驶自主飞行器；(4)石油开采钻井应用中的压力控制；光纤控制；(5)出现各种故障时的网络控制系统；(6)麻醉控制；(7)在出现不可预测的干扰时的迭代学习控制；(8)空气生物采样；(8)在存在滞后的情况下控制智能材料；(9)在湍流中对多架无人机进行空中加油；(10)对柔性飞机的控制；(11)对旋翼机的控制。将这个工具箱开发成商业产品将成为CUA开发系统设计方法的跳板，以便将这一理论更广泛地应用于工业和许多政府部门，包括美国能源部、国防部、能源部和美国宇航局。

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.