Symposium on Optimal Guidance and Control for Autonomous Systems IUTAM on March 19-21, 2020 in Hawaiian Ocean View, United States

Symposium on Optimal Guidance and Control for Autonomous Systems (IUTAM) March 19, 2020 - Hawaiian Ocean View, United States

The IUTAM Symposium on Optimal Guidance and Control for Autonomous Systems 2020 invites papers representing the results of research and studies in optimal guidance and control, and the utilities of these theoretical developments in the performance of autonomous systems.

                Over the last two decades, the theories, concepts and methods of optimal control, calculus of variations, dynamic systems, motion stability, sensor fusion, optimal guidance and machine learning have contributed to paving the way to or to leveraging the autonomy of controllable systems, including space, air, surface and underwater vehicles, and swarm networks. Design, analysis and development of such systems have been associated with safety, stability, accuracy, autonomy, maneuverability, navigation and guidance problems, and their real-time or near real-time solutions, especially for performance in uncertain environments. These problems have been investigated via such conventional and non-conventional solutions methods and approaches as Lagrange and Hamilton-Jacobi- Bellmann’s formalisms, direct and indirect methods, and formalisms based on the sufficient conditions of absolute optimality. Analytical integrability of canonical systems, solvability of Hamilton-Jacobi equation, completeness of necessary and sufficient conditions of optimality, existence of Pontryagin minimum, synthesis of control regimes, real-time integration of optimal control, guidance, navigation and targeting are only some of the still remaining and underlying fundamental issues and challenges of leveraging the autonomy and optimization of the controllable systems. These solutions are critical to not only mission design and analysis, but also to the systems’ satisfactory performance. Utilities of these solutions have led to increased and strengthened requirements for motion planning, dynamics and performance of the onboard control systems or their components.

                The symposium intends to bring together leading scientists, experts and engineers engaged in the development and application of novel concepts, theories and methods of optimal guidance and control, nonlinear dynamic systems and motion stability, sensor fusion and machine learning to discuss and address the issues relevant to theory and performance autonomous and other controllable systems. As such, this meeting represents a wide range of research and studies involving the theories and methods in the areas mentioned above, fundamental issues of autonomy and approaches to their solutions, and utility and implementation of these solutions in leveraging the autonomy of the controllable systems.


Scientific Commitee

Chair:

Full name: Dilmurat Azimov

Affiliation: University of Hawai’i at Manoa

City: Honolulu

Country: USA

Email: [email protected];

Member:

Full name: Corey Ippolito

Affiliation: NASA Ames Research Center

City: Moffett Field, CA

Country: USA

Member:

Full name: Peter Hagedorn

Affiliation: Technische Universität Darmstadt

City: Darmstadt

Country: Germany

Member:

Full name: Markus Landgraf

Affiliation: European Space Agency

City: Noordwijk

Country: The Netherlands

Member:

Full name: Yukihiro Kubo

Affiliation: Ritsumeikan University

City: Kyoto

Member:

Full name: Frederic Bonnans

Affiliation: Ecole Polytechnique

City: Palaiseau

Country: France

Member:

Full name: Andrei Dmitruk

Affiliation: Central Economics & Mathematics Institute, Russian Academy of Sciences

City: Moscow

Country: Russia

IUTAM Representative:

Full name: Peter Eberhard

Affiliation: IUTAM

City:

Country: Germany

Organizing Commitee (all at UH-Manoa, HI, USA):

Dilmurat Azimov

Zhuoyuan Song

Yingfei Dong

Evan Tengan

Evan Kawamura

Dylan Morrison-Fogel

Melissa Onishi

Minji Jo

Gang Yuan

Motion planning

Optimal Control

Optimal Guidance

Feedback control

Machine learning

Swarm network control

Centralized or decentralized control

Guidance, navigation and control (GNC)

Stability

Targeting

Data processing

Sensor fusion

On-board control systems and capabilities

Autonomous GNC

Name: University of Hawaii
Website: http://manoa.hawaii.edu