Nonlinear and Adaptive Control of Smart Material-Actuated Systems with Application to Nanopositioning

该奖项的研究目标是为滞后系统开发一种新颖的多时间尺度非线性和自适应控制框架，从而实现对智能材料驱动系统的稳健、精确和高带宽控制。智能材料，如压电材料和形状记忆合金，表现出复杂的迟滞行为与由智能材料执行器驱动的结构和流体的非线性动力学的强烈耦合，特别是在中高驱动水平下。后者，再加上迟滞和动态的不确定性，使得精确控制智能材料驱动系统变得具有挑战性。在这项研究中，将建立滞回系统的多时间尺度平均理论。这将首次提供一个框架，通过时间尺度分离，将自适应滞后补偿与大量的非线性和自适应控制方法相结合，用于无滞后系统。此外，还将开发基于可重构计算硬件的用于滞后反转和自适应的通用并行范例，以使所提出的理论能够有效地实现。所开发的理论和算法将在压电致动器驱动的纳米定位系统的控制中得到验证，所提出的项目将对智能材料的一些应用领域产生积极的影响，如微纳米技术、生物医学设备、机器人以及航空航天和汽车行业。跨学科项目将为有才华的研究生和本科生提供宝贵的培训经验，特别是那些来自代表性不足群体的学生。它还将丰富密歇根州立大学现有和新开发的智能材料和控制课程，如智能材料传感器和执行器，以及自适应控制。私人投资促进机构还将积极寻找机会，将已开发的技术转移到纳米定位和扫描探针显微镜(SPM)行业。

The research objective of this award is to develop a novel multi-time-scale nonlinear and adaptive control framework for hysteretic systems, and thus to enable robust, precision, and high-bandwidth control of smart material-actuated systems. Smart materials, such as piezoelectric materials and shape memory alloys, exhibit strong coupling of complex hysteretic behavior with the nonlinear dynamics of structures and fluids that are driven by smart material actuators, especially at medium-to-high drive levels. The latter, together with the uncertainties in both hysteresis and dynamics, makes it challenging to precisely control smart material-actuated systems. In this research, a multi-time-scale averaging theory for hysteretic systems will be established. This will, for the first time, provide a framework for merging adaptive hysteresis compensation with a plethora of nonlinear and adaptive control methods for hysteresis-free systems through time-scale separation. In addition, a general, parallel paradigm for hysteresis inversion and adaptation will be developed based on reconfigurable computing hardware, to enable efficient implementation of the proposed theory. The developed theory and algorithms will be validated in the control of a piezoelectric actuator-driven nanopositioning system.The proposed project can positively impact a number of application areas of smart materials, such as micro- and nanotechnology, biomedical devices, robotics, and aerospace and automotive industries. The interdisciplinary project will offer valuable training experience for talented graduate and undergraduate students, especially those from underrepresented groups. It will also enrich existing and newly developed courses on smart materials and controls at Michigan State University, such as Smart Material Sensors and Actuators, and Adaptive Control. The PIs will also proactively seek opportunities to transfer the developed technology to the nanopositioning and scanning probe microscopy (SPM) industry.