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

International Joint Conference on Neural Networks

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L2 Gain And Passivity Techniques In Nonlinear Control

A Glimpse at Set Theory. The Real Numbers. The Topology of Cartesian Spaces. Convergence. Continuous Functions. Functions of One Variable. Infinite Series. Differentiation in RP Integration in RP.

The elements of real analysis (2nd edition), by Robert G. Bartle. Pp xv, 480. £10. 1976. SBM 0 471 05464 X (Wiley)

Open Dynamics Engine を用いたスノーボードロボットシミュレータの開発

Entry guidance is a fundamental element of atmospheric entry missions. However, during the atmospheric entry, there are many uncertainties that cannot be predicted and ultimately lead to trajectory...

Development and Validation of Linear Covariance Analysis Tool for Atmospheric Entry

Differential Evolution (DE) is a basic and robust evolutionary strategy that has been applied to determining the global optimum for complex optimization problems[1–5]. It was introduced in 1995 by Storn and Price [1] and has been successfully applied to optimization problems including nonlinear, non-differentiable, non-convex, and multi-model functions. DE algorithms show good convergence, high-reliability, simplicity, and a reduced number of controllable parameters [2]. Olds and Kluever [3] applied DE to an interplanetary trajectory optimization problem and demonstrated the effectiveness of DE to produce rapid solutions. Madavan [4] discussed various modifications to the DE algorithm, improved its computational efficiency, and applied it to aerodynamic shape optimization problems. DE algorithms are easy to use, as they require only a few robust control variables, which can be drawn from a well-defined numerical interval. However, the existing various DE algorithms also have limitations, being susceptible to instability and getting trapped into local optima[2]. Notable effort has been spent addressing this by coupling DE algorithms with other optimization algorithms (for example, Self Organizing Maps (SOM) [6], Dynamic Hill Climbing (DHC) [7], Neural Networks (NN) [7], Particle Swarm Optimization (PSO) [8]). In these cases, the additional algorithm is used as an additional loop within the optimization process, creating a hybrid system with an inner and outer loop. Such hybrid algorithms are inherently more complex and so the computation cost is increased. Attempting to address this, a self-adaptive DE was designed and applied to the orbit design problem for prioritized multiple targets by Chen[5]. However, the self-adaptive feature is somewhat limited as it relates only to the number of generations within the optimization. A Self-adaptive DE which can automatically adapt its learning strategies and the associated parameters during the evolving procedure was proposed by Qin and Suganthan[9] and 25 test functions were used to verify the algorithm.

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An improved differential evolution algorithm and its applications to orbit design

In the postcapture phase, compliant manipulation can maintain the target undamaged and ensure the safety of the manipulation procedure. In most studies, due to the compliant equation, the c...

Constrained Compliant Control for Space Robot Postcapturing Uncertain Target

Generate optimal grasping trajectories to the end-effector using an improved genetic algorithm

A robust trajectory optimization approach for rendezvous and proximity operations in perturbed elliptical orbits with uncertainties is proposed. For robust trajectory design, a new discrete-time li...

Jianjun Luo

Papers

Development and Validation of Linear Covariance Analysis Tool for Atmospheric Entry

An improved differential evolution algorithm and its applications to orbit design

Constrained Compliant Control for Space Robot Postcapturing Uncertain Target

Generate optimal grasping trajectories to the end-effector using an improved genetic algorithm

Robust Trajectory Design for Rendezvous and Proximity Operations with Uncertainties