Clonal Selection Identification and Adaptive PD Control for Uncertain Dynamical System

2009 ◽  
Author(s):  
Jiangqiang Hu ◽  
Jianchuan Yin ◽  
Tieshan Li
Keyword(s):  
Dynamical System ◽  
Clonal Selection ◽  
Pd Control ◽  
Uncertain Dynamical System

An Artificial Immune Dynamical System for Optimization

2011 ◽  
pp. 49-75
Author(s):  
Licheng Jiao ◽  
Maoguo Gong ◽  
Wenping Ma
Keyword(s):  
Dynamical System ◽  
Immune Response ◽  
Numerical Optimization ◽  
High Performance ◽  
Optimization Problems ◽  
Clonal Selection ◽  
Immune Memory ◽  
Artificial Immune ◽  
Dynamic Algorithm ◽  
Whole Process

Many immue-inspired algorithms are based on the abstractions of one or several immunology theories, such as clonal selection, negative selection, positive selection, rather than the whole process of immune response to solve computational problems. In order to build a general computational framework by simulating immune response process, this chapter introduces a population-based artificial immune dynamical system, termed as PAIS, and applies it to numerical optimization problems. PAIS models the dynamic process of human immune response as a quaternion (G, I, R, Al), where G denotes exterior stimulus or antigen, I denotes the set of valid antibodies, R denotes the set of reaction rules describing the interactions between antibodies, and Al denotes the dynamic algorithm describing how the reaction rules are applied to antibody population. Some general descriptions of reaction rules, including the set of clonal selection rules and the set of immune memory rules are introduced in PAIS. Based on these reaction rules, a dynamic algorithm, termed as PAISA, is designed for numerical optimization. In order to validate the performance of PAISA, 9 benchmark functions with 20 to 10,000 dimensions and a practical optimization problem, optimal approximation of linear systems are solved by PAISA, successively. The experimental results indicate that PAISA has high performance in optimizing some benchmark functions and practical optimization problems.

Adaptive Robust Model-Following Control and Application to Robot Manipulators

1987 ◽  
Vol 109 (3) ◽  
pp. 209-215 ◽  
Author(s):  
Y. H. Chen
Keyword(s):  
Dynamical System ◽  
Dynamical Systems ◽  
Robotic Manipulators ◽  
Time Varying ◽  
Feedback Controls ◽  
Adaptive Feedback ◽  
Model Following Control ◽  
Model Following ◽  
Uncertain Dynamical System ◽  
Robust Model

The model-following control problem for nonlinear uncertain dynamical systems is considered. Based only on the knowledge of functional properties relating to the bound of the time-varying uncertainty, a class of adaptive feedback controls is developed which, under some realistic assumptions, guarantees the error between the uncertain dynamical system and the model tends to be zero. Application to robotic manipulators is made.

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