There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

[신간의 별자리x] 우리/미술, 그리고 ‘슬픔의 박물관’

I. the origin of species by means of natural selection

This book, written by two major figures in adaptive control, provides a wealth of material for researchers, practitioners, and students. While some researchers in adaptive control may note the absence of a particular topic, the book‘s scope represents a high-gain instrument. It can be used by designers of control systems to enhance their work through the information on many new theoretical developments, and can be used by mathematical control theory specialists to adapt their research to practical needs. The book is strongly recommended to anyone interested in adaptive control.

Adaptive Control

Online actor-critic algorithm to solve the continuous-time infinite horizon optimal control problem

Multi-agent systems arise in several domains of engineering and they can be used to solve problems which are difficult for an individual agent to solve. Examples of multi-agent systems include disaster response, online trading, and modeling social structures. Strategies for team decision problems, including optimal control, player games (H-infinity control, non zero sum), and so on are normally solved for off-line by solving associated matrix equations such as the coupled Riccati equations or coupled Hamilton-Jacobi equations. However, using that approach, players cannot change their objectives online in real time without calling for a completely new off-line solution for the new strategies. Therefore, in this paper we present an online gaming algorithm based on policy iteration to solve the continuous-time (CT) distributed multi-agent games for cooperative systems with infinite horizon cost with known dynamics. That is, the algorithm learns online in real-time the solution to the game design cooperative coupled HJ equations. This allows for truly dynamical team decisions where objective functions can change in real time and the system dynamics can be timevarying.

/pdf/distributed-games-for-multi-agent-systems-games-on-5bmq4dvvzv.pdf

Distributed Games for Multi-Agent Systems: Games on Communication Graphs

Reinforcement Learning with Applications in Automation Decision and Feedback Control

This chapter contains sections titled: Introduction Optimal Control and the Continuous Time Hamilton-Jacobi-Bellman Equation Online Solution of Nonlinear Two-Player Zero-Sum Games and Hamilton-Jacobi-Isaacs Equation Online Solution of Nonlinear Nonzero-Sum Games and Coupled Hamilton-Jacobi Equations References 
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Online Learning Algorithms for Optimal Control and Dynamic Games

In this paper, an Adaptive Backstepping Neural Network control approach is used for a class of affine nonlinear systems which describe the pump model in the strict feedback form. The close loop signals are semi globally uniformly ultimately bounded and the output of the system is proven to follow a desired trajectory. Simulation results are presented to show the effectiveness of the approach proposed in order to control the pump output.

/pdf/adaptive-backstepping-neural-network-control-for-mechanical-1cfez03hwq.pdf

Adaptive Backstepping Neural Network Control for Mechanical Pumps

In this paper, an Adaptive Backstepping Neural Network control approach is used for a class of affine nonlinear systems which describe the Mitogen Activated Protein Kinase (MAPK) cascade models in the strict feedback form. We consider some of forms of the MAPK cascade [4]. The close loop signals are semiglobally uniformly ultimately bounded and the output of the system is proven to follow a desired trajectory. Simulation results are presented to show the effectiveness of the approach proposed in order to control the MAPK output.

Adaptive Backstepping control for MAPK cascade models using RBF neural networks

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