The American Control Conference (ACC) is an annual conference that showcases the latest in control systems theory. The conference is one of the leading conferences for controls systems academics and practitioners.
ACC 2023 will showcase the latest technological advancements in algorithm development, robotics, mechatronics, control systems, predictive control, system identification, and more. Collimator is honored to be selected as one of the presenters in the plenary sessions.
The 2023 American Control Conference (ACC) will be held Wednesday through Friday, May 31 - June 2, 2023, in San Diego, California, USA at the Hilton San Diego Bayfront Hotel. Several speakers will discuss topics related to control systems, algorithm development, modeling and simulation, machine learning and more.
ACC is the annual conference of the American Automatic Control Council (AACC), the U.S. national member organization of the International Federation for Automatic Control (IFAC). National and international society co-sponsors of ACC include the American Institute of Aeronautics and Astronautics (AIAA), American Institute of Chemical Engineers(AIChE), American Society of Civil Engineers (ASCE), American Society of Mechanical Engineers (ASME), IEEE Control Systems Society (IEEE-CSS), Institute for Operations Research and the Management Sciences (INFORMS), International Society of Automation (ISA), Society for Modeling & Simulation International (SCS), and Society for Industrial & Applied Mathematics(SIAM).
Collimator's Chief Scientist, Steve Brunton ("eigensteve"), will he hosting a semi-plenary session titled "Machine Learning for Sparse Nonlinear Modeling and Control" on Wednesday May 31st. Join us to learn about how machine learning may be used to develop accurate and efficient nonlinear dynamical systems models for complex natural and engineered systems.
We will explore the sparse identification of nonlinear dynamics (SINDy) algorithm, which identifies a minimal dynamical system model that balances model complexity with accuracy, avoiding overfitting. This approach tends to promote models that are interpretable and generalizable, capturing the essential “physics” of the system. We will also discuss the importance of learning effective coordinate systems in which the dynamics may be expected to be sparse. This sparse modeling approach will be demonstrated on a range of challenging modeling problems, for example in fluid dynamics, and we will discuss how to incorporate these models into existing model-based control efforts.
Collimator is a modeling and simulation platform for engineers to design and test complex, mission critical systems in a way that is reliable, secure, fast and intuitive. Collimator provides a unified environment to design, simulate, test, and continuously upgrade autonomous systems. This is important for the world today where big data is used to train neural networks and engineering design continues through the lifecycle of a system. Reduce your risk and bring products to market faster by using Collimator to: