https://axon.cs.byu.edu/~martinez/classes/678/Presentations/Yao.pdf

Extreme learning machine: Theory and applications

I have developed "tennis elbow" from lugging this book around the past four weeks, but it is worth the pain, the effort, and the aspirin. It is also worth the (relatively speaking) bargain price. Including appendixes, this book contains 894 pages of text. The entire panorama of the neural sciences is surveyed and examined, and it is comprehensive in its scope, from genomes to social behaviors. The editors explicitly state that the book is designed as "an introductory text for students of biology, behavior, and medicine," but it is hard to imagine any audience, interested in any fragment of neuroscience at any level of sophistication, that would not enjoy this book. The editors have done a masterful job of weaving together the biologic, the behavioral, and the clinical sciences into a single tapestry in which everyone from the molecular biologist to the practicing psychiatrist can find and appreciate his or

Principles of Neural Science

This paper surveys the field of reinforcement learning from a computer-science perspective. It is written to be accessible to researchers familiar with machine learning. Both the historical basis of the field and a broad selection of current work are summarized. Reinforcement learning is the problem faced by an agent that learns behavior through trial-and-error interactions with a dynamic environment. The work described here has a resemblance to work in psychology, but differs considerably in the details and in the use of the word "reinforcement." The paper discusses central issues of reinforcement learning, including trading off exploration and exploitation, establishing the foundations of the field via Markov decision theory, learning from delayed reinforcement, constructing empirical models to accelerate learning, making use of generalization and hierarchy, and coping with hidden state. It concludes with a survey of some implemented systems and an assessment of the practical utility of current methods for reinforcement learning.

/pdf/reinforcement-learning-a-survey-38lmyylg2y.pdf

Reinforcement learning: a survey

Planning algorithms are impacting technical disciplines and industries around the world, including robotics, computer-aided design, manufacturing, computer graphics, aerospace applications, drug design, and protein folding. This coherent and comprehensive book unifies material from several sources, including robotics, control theory, artificial intelligence, and algorithms. The treatment is centered on robot motion planning but integrates material on planning in discrete spaces. A major part of the book is devoted to planning under uncertainty, including decision theory, Markov decision processes, and information spaces, which are the “configuration spaces” of all sensor-based planning problems. The last part of the book delves into planning under differential constraints that arise when automating the motions of virtually any mechanical system. Developed from courses taught by the author, the book is intended for students, engineers, and researchers in robotics, artificial intelligence, and control theory as well as computer graphics, algorithms, and computational biology.

Planning Algorithms: Introductory Material

This paper surveys the field of reinforcement learning from a computer-science perspective. It is written to be accessible to researchers familiar with machine learning. Both the historical basis of the field and a broad selection of current work are summarized. Reinforcement learning is the problem faced by an agent that learns behavior through trial-and-error interactions with a dynamic environment. The work described here has a resemblance to work in psychology, but differs considerably in the details and in the use of the word ``reinforcement.'' The paper discusses central issues of reinforcement learning, including trading off exploration and exploitation, establishing the foundations of the field via Markov decision theory, learning from delayed reinforcement, constructing empirical models to accelerate learning, making use of generalization and hierarchy, and coping with hidden state. It concludes with a survey of some implemented systems and an assessment of the practical utility of current methods for reinforcement learning.

Reinforcement Learning: A Survey

The objective of this paper is to interpret existing flying qualities criteria in a multivariable format based upon state vectors and matrices of stability and control derivatives. State-space methods are shown to be entirely compatible with conventional flying qualities analysis. Furthermore, they lead to a definition of the well-known control anticipation parameter that has potential utility in the evaluation of higher-order aircraft/control systems.

A unifying framework for longitudinal flying qualities criteria

Methods and results in the continuing development of a digital flight control system (DFCS) for the CH-47B helicopter are examined. The helicopter is the research vehicle for the NASA VTOL Approach and Landing Technology (VALT) Program. It is equipped with comprehensive equipment for the investigation of navigation, guidance, and control requirements for future VTOL aircraft. Two control modes (attitude-command and velocity-command) are implemented, and each mode provides 'Type 1' response to guidance commands. DFCS design is based upon optimal estimation and control methods, which are found to provide flexible and efficient means for defining practical digital control systems.

Digital flight control design for a tandem-rotor helicopter

The seminar course for first-year undergraduate students, From the Earth to the Moon, deals with both technical and non-technical aspects of space flight, with particular reference to lunar voyages. The goals of the course are to establish a framework for understanding technology and its applications, to present fundamental principles of science and program management, and to motivate students to learn more about the many facets of engineering. As such, the course introduces numerous issues of systems engineering in a broad context, presenting not only science, technology, and mathematics but the reasons that these subjects are important. Typically, all of the students in the course have studied physics and/or calculus in high school, though half plan to major in the humanities. Thus, the course has dual roles in exposing liberal arts students to details of technology and engineering students to social impacts of technology.

From the Earth to the Moon: a freshman seminar

Time to reinvent the general aviation aircraft.

Control-theoretic pilot models can provide important new insights regarding the stability and performance characteristics of the pilot-aircraft system. Optimal-control pilot models can be formed for a wide range of flight conditions, suggesting that the human pilot can maintain stability if he adapts his control strategy to the aircraft's changing dynamics. Of particular concern is the effect of sub-optimal pilot adaptation as an aircraft transitions from low to high angle-of-attack during rapid maneuvering, as the changes in aircraft stability and control response can be extreme. This paper examines the effects of optimal and sub-optimal effort during a typical 'high-g' maneuver, and it introduces the concept of minimum-control effort (MCE) adaptation. Limited experimental results tend to support the MCE adaptation concept.

/pdf/prediction-of-pilot-aircraft-stability-boundaries-and-2keqdfve4l.pdf

Robert F. Stengel

Papers

A unifying framework for longitudinal flying qualities criteria

Digital flight control design for a tandem-rotor helicopter

From the Earth to the Moon: a freshman seminar

Time to reinvent the general aviation aircraft.

Prediction of pilot-aircraft stability boundaries and performance contours