Conference Paper

Maximum entropy adaptive control of chaotic systems

Dept. of Electr. & Comput. Eng., Syracuse Univ., NY
DOI: 10.1109/ISIC.1998.713668 Conference: Intelligent Control (ISIC), 1998. Held jointly with IEEE International Symposium on Computational Intelligence in Robotics and Automation (CIRA), Intelligent Systems and Semiotics (ISAS), Proceedings
Source: IEEE Xplore


We present an adaptive control strategy for controlling chaos in
nonlinear dynamical systems. The proposed method is a neuro-fuzzy model
as a globally coupled map based on entropy optimization, which combines
an identified system fuzzy model and a control input update rule. The
stability analysis of the resulting control scheme is shown by a
property of contraction mappings. Numerical examples are given to
illustrate the transition between chaotic states and stable equilibrium

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    ABSTRACT: With the aim of validating the feasibility of applying a maximum-entropy-based adaptive control program to the constant force control of a machining process, the maximum-entropy criterion is first introduced. The control flow of the machining process and the mathematic model of the machine tool for experiment are then explained. Finally, both simulation and experiment results are studied to compare the proposed controller and the conventional PID controller. It thus indicates that maximum-entropy based controller is appropriate for the machining process.
    No preview · Conference Paper · Sep 2005
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    ABSTRACT: Force control is an effective means of improving the quality and productivity of machining operations. However, machining processes exhibit nonlinear, time-varying and uncertain characteristics due to the variations in the depth of cut, spindle speed, and damping ration, etc. One of the difficulties in control of machining processes comes from the uncertainty and nonlinearity in cutting processes. An entropy-based optimal control approach for machining is developed to deal with such a difficulty in this paper. With this structure, constant cutting force is obtained by varying the table feedrate under time-varying cutting conditions, and control is realized by minimizing the entropy used as the control performance index. Experiments are given to demonstrate the approach, and satisfactory results are obtained.
    No preview · Conference Paper · Sep 2008