CAREER: Scalable Adaptive Control with Performance Guarantees for Safe and Efficient Autonomous Systems

About This Grant

This Faculty Early Career Development Program (CAREER) grant funds research on autonomous systems that are poised to transform industries ranging from transportation and manufacturing to environmental monitoring and space exploration. Use of these systems in safety-critical applications faces significant challenges, such as ensuring safety and performance in unpredictable environments and adapting to large and sudden changes, and handling complex dynamics. This project seeks to overcome these barriers by developing advanced control methods that ensure reliable and high-performing operation of autonomous systems, even in dynamic and uncertain conditions. These advancements could improve the safety, reliability, and performance of technologies such as self-driving cars, autonomous air taxis, and spacecraft. In addition to its technical contributions, the project will cultivate the next generation of innovators in control and autonomous systems. Educational and outreach efforts will include developing new course materials, organizing workshops, and engaging K-12 students in Alabama through interactive activities such as drone race challenges and twisty flyer events, sparking interest in science, technology, engineering, and mathematics (STEM) fields. The goal of this project is to develop advanced control methodologies that enable the reliable deployment of autonomous systems in dynamic and uncertain environments, addressing key challenges such as safety, performance, adaptability, and the ability to manage complex, high-dimensional systems. To achieve this, the research focuses on three main thrusts: (i) designing a control architecture that integrates robust adaptive uncertainty compensation and constrained control to ensure safe and efficient operation of nonlinear systems under complex uncertainties, (ii) establishing an adaptive nonlinear parameter-varying control framework to handle large uncertainties, including those arising from control authority constraints and unmatched uncertainties, and (iii) leveraging machine learning techniques to enhance the scalability and performance of the projected robust adaptive control algorithms, enabling their application to high-dimensional systems with stringent performance demands. Together, these efforts aim to advance the state of the art in managing uncertainty, constraints, and nonlinear dynamics, setting a foundation for deploying safety-critical autonomous systems across a wide range of applications. The educational and outreach components will complement the research by equipping students across all levels with the skills and motivation to pursue careers in control and autonomy, thereby contributing to a stronger workforce and regional ecosystem in intelligent systems. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.