In 1948, he received a Bachelor of Science from NYU in aeronautical engineering, and began to work for Grumman Aircraft in the same year. His studies continued as a part-time student leading to a Master of Science in mathematics (1951) and to the Sc.D. in aeronautical engineering (1958).[1] In 1963, he left Grumman (as Assistant Chief of the Research Department) and founded Analytical Mechanics Associates with two partners. In 1978, he became a professor in the Aerospace and Ocean Engineering Department at the Virginia Polytechnic Institute.[1]
Kelley received NYU's Founder's Day Award in 1959, the IAS New York Section Award in 1961, the AIAA Guidance and Control of Flight Award in 1973, and the AIAA Pendray Award in 1979. He was a Fellow of the AIAA, and member of the AAS, the IEEE and SIAM, and founder and first chairman of IFAC's Mathematics of Control Committee.[1]
In machine learning, a neural network is a model inspired by the structure and function of biological neural networks in animal brains.
In machine learning, backpropagation is a gradient estimation method commonly used for training a neural network to compute its parameter updates.
A feedforward neural network (FNN) is one of the two broad types of artificial neural network, characterized by direction of the flow of information between its layers. Its flow is uni-directional, meaning that the information in the model flows in only one direction—forward—from the input nodes, through the hidden nodes and to the output nodes, without any cycles or loops. Modern feedforward networks are trained using backpropagation, and are colloquially referred to as "vanilla" neural networks.
Trajectory optimization is the process of designing a trajectory that minimizes some measure of performance while satisfying a set of constraints. Generally speaking, trajectory optimization is a technique for computing an open-loop solution to an optimal control problem. It is often used for systems where computing the full closed-loop solution is not required, impractical or impossible. If a trajectory optimization problem can be solved at a rate given by the inverse of the Lipschitz constant, then it can be used iteratively to generate a closed-loop solution in the sense of Caratheodory. If only the first step of the trajectory is executed for an infinite-horizon problem, then this is known as Model Predictive Control (MPC).
A native of Terre Haute, Indiana, Stuart E. Dreyfus is professor emeritus at University of California, Berkeley in the Industrial Engineering and Operations Research Department. While at the Rand Corporation he was a programmer of the JOHNNIAC computer. While at Rand he coauthored Applied Dynamic Programming with Richard Bellman. Following that work, he was encouraged to pursue a Ph.D. which he completed in applied mathematics at Harvard University in 1964, on the calculus of variations. In 1962, Dreyfus simplified the Dynamic Programming-based derivation of backpropagation using only the chain rule. He also coauthored Mind Over Machine with his brother Hubert Dreyfus in 1986.
The Gauss pseudospectral method (GPM), one of many topics named after Carl Friedrich Gauss, is a direct transcription method for discretizing a continuous optimal control problem into a nonlinear program (NLP). The Gauss pseudospectral method differs from several other pseudospectral methods in that the dynamics are not collocated at either endpoint of the time interval. This collocation, in conjunction with the proper approximation to the costate, leads to a set of KKT conditions that are identical to the discretized form of the first-order optimality conditions. This equivalence between the KKT conditions and the discretized first-order optimality conditions leads to an accurate costate estimate using the KKT multipliers of the NLP.
DIDO is a MATLAB optimal control toolbox for solving general-purpose optimal control problems. It is widely used in academia, industry, and NASA. Hailed as a breakthrough software, DIDO is based on the pseudospectral optimal control theory of Ross and Fahroo. The latest enhancements to DIDO are described in Ross.
Arthur Earl Bryson Jr. is the Paul Pigott Professor of Engineering Emeritus at Stanford University and the "father of modern optimal control theory". With Henry J. Kelley, he also pioneered an early version of the backpropagation procedure, now widely used for machine learning and artificial neural networks.
Yann André LeCun is a French-American computer scientist working primarily in the fields of machine learning, computer vision, mobile robotics and computational neuroscience. He is the Silver Professor of the Courant Institute of Mathematical Sciences at New York University and Vice President, Chief AI Scientist at Meta.
John L. Junkins is an American academic and a distinguished professor of aerospace engineering in the College of Engineering at Texas A&M University specializing in spacecraft navigation, guidance, dynamics, and control. He holds the Royce E. Wisenbaker Endowed Chair at Texas A&M University and also serves as the Founding Director of the Hagler Institute for Advanced Study at Texas A&M University, since its founding in December 2010. On November 24, 2020, Junkins was announced as the interim President of Texas A&M University starting January 2021. He was the interim president until May 31, 2021.
Isaac Michael Ross is a Distinguished Professor and Program Director of Control and Optimization at the Naval Postgraduate School in Monterey, CA. He has published a highly-regarded textbook on optimal control theory and seminal papers in pseudospectral optimal control theory, energy-sink theory, the optimization and deflection of near-Earth asteroids and comets, robotics, attitude dynamics and control, orbital mechanics, real-time optimal control, unscented optimal control and continuous optimization. The Kang–Ross–Gong theorem, Ross' π lemma, Ross' time constant, the Ross–Fahroo lemma, and the Ross–Fahroo pseudospectral method are all named after him. According to a report published by Stanford University, Ross is one of the world's top 2% of scientists.
Peretz P. Friedmann is an American aerospace engineer and an academic. He is the François-Xavier Bagnoud Professor Emeritus of Aerospace Engineering at the University of Michigan, Ann Arbor.
Naira Hovakimyan is an Armenian control theorist who holds the W. Grafton and Lillian B. Wilkins professorship of the Mechanical Science and Engineering at the University of Illinois at Urbana-Champaign. She is the director of AVIATE Center of flying cars at UIUC, funded through a NASA University Leadership Initiative. She was the inaugural director of the Intelligent Robotics Laboratory during 2015–2017, associated with the Coordinated Science Laboratory at the University of Illinois at Urbana-Champaign.
Daniele Mortari is Professor of Aerospace Engineering at Texas A&M University and Chief Scientist for Space for Texas A&M ASTRO Center. Mortari is known for inventing the Flower Constellations, the k-vector range searching technique, and the Theory of functional connections.
Machine learning control (MLC) is a subfield of machine learning, intelligent control, and control theory which aims to solve optimal control problems with machine learning methods. Key applications are complex nonlinear systems for which linear control theory methods are not applicable.
In mathematics, unscented optimal control combines the notion of the unscented transform with deterministic optimal control to address a class of uncertain optimal control problems. It is a specific application of tychastic optimal control theory, which is a generalization of Riemmann-Stieltjes optimal control theory, a concept introduced by Ross and his coworkers.
Silvia Ferrari is an Italian-American aerospace engineer. She is John Brancaccio Professor at the Sibley School of Mechanical and Aerospace Engineering at Cornell University and also the director of the Laboratory for Intelligent Systems and Control (LISC) at the same university.
Frank L. Lewis is an American electrical engineer, academic and researcher. He is a professor of electrical engineering, Moncrief-O’Donnell Endowed Chair, and head of Advanced Controls and Sensors Group at The University of Texas at Arlington (UTA). He is a member of UTA Academy of Distinguished Teachers and a charter member of UTA Academy of Distinguished Scholars.
Lyle Norman Long is an academic, and computational scientist. He is a Professor Emeritus of Computational Science, Mathematics, and Engineering at The Pennsylvania State University, and is most known for developing algorithms and software for mathematical models, including neural networks, and robotics. His research has been focused in the fields of computational science, computational neuroscience, cognitive robotics, parallel computing, and software engineering.
Joaquim R. R. A. Martins is an aerospace engineer, academic, and author. He is the Pauline M. Sherman Collegiate Professor in the Department of Aerospace Engineering at the University of Michigan, where he directs the Multidisciplinary Design Optimization Laboratory. He also has a courtesy appointment in the Department of Naval Architecture and Marine Engineering.
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