A Comparison of Numerical Methods for Identification and Optimization Problems Involving Control Systems with Delays.

1979 ◽  
Author(s):  
H. T. Banks ◽  
J. A. Burns ◽  
E. M. Cliff
Keyword(s):  
Numerical Methods ◽  
Control Systems ◽  
Optimization Problems

A numerical method for the approximation of reachable sets of linear control systems

2017 ◽  
Vol 36 (2) ◽  
pp. 423-441 ◽  
Author(s):  
Lizhen Shao ◽  
Fangyuan Zhao ◽  
Guangda Hu
Keyword(s):  
Numerical Method ◽  
Control Systems ◽  
Optimization Problems ◽  
Approximation Error ◽  
Outer Approximation ◽  
Reachable Sets ◽  
Linear Control ◽  
Superior Performance ◽  
Linear Control Systems ◽  
Inner Approximation

Abstract In this article, a numerical method for the approximation of reachable sets of linear control systems is discussed. First a continuous system is transformed into a discrete one with Runge–Kutta methods. Then based on Benson’s outer approximation algorithm for solving multiobjective optimization problems, we propose a variant of Benson’s algorithm to sandwich the reachable set of the discrete system with an inner approximation and an outer approximation. By specifying an approximation error, the quality of the approximations measured in Hausdorff distance can be directly controlled. Furthermore, we use an illustrative example to demonstrate the working of the algorithm. Finally, computational experiments illustrate the superior performance of our proposed algorithm compared to a recent algorithm in the literature.

scholarly journals HIDDEN ATTRACTORS IN DYNAMICAL SYSTEMS. FROM HIDDEN OSCILLATIONS IN HILBERT–KOLMOGOROV, AIZERMAN, AND KALMAN PROBLEMS TO HIDDEN CHAOTIC ATTRACTOR IN CHUA CIRCUITS

2013 ◽  
Vol 23 (01) ◽  
pp. 1330002 ◽  
Author(s):  
G. A. LEONOV ◽  
N. V. KUZNETSOV
Keyword(s):  
Dynamical Systems ◽  
Numerical Methods ◽  
Automatic Control ◽  
Control Systems ◽  
Computational Procedure ◽  
Phase Locked Loops ◽  
Hidden Attractor ◽  
Hidden Attractors ◽  
Computational Point ◽  
Hidden Oscillations

From a computational point of view, in nonlinear dynamical systems, attractors can be regarded as self-excited and hidden attractors. Self-excited attractors can be localized numerically by a standard computational procedure, in which after a transient process a trajectory, starting from a point of unstable manifold in a neighborhood of equilibrium, reaches a state of oscillation, therefore one can easily identify it. In contrast, for a hidden attractor, a basin of attraction does not intersect with small neighborhoods of equilibria. While classical attractors are self-excited, attractors can therefore be obtained numerically by the standard computational procedure. For localization of hidden attractors it is necessary to develop special procedures, since there are no similar transient processes leading to such attractors. At first, the problem of investigating hidden oscillations arose in the second part of Hilbert's 16th problem (1900). The first nontrivial results were obtained in Bautin's works, which were devoted to constructing nested limit cycles in quadratic systems, that showed the necessity of studying hidden oscillations for solving this problem. Later, the problem of analyzing hidden oscillations arose from engineering problems in automatic control. In the 50–60s of the last century, the investigations of widely known Markus–Yamabe's, Aizerman's, and Kalman's conjectures on absolute stability have led to the finding of hidden oscillations in automatic control systems with a unique stable stationary point. In 1961, Gubar revealed a gap in Kapranov's work on phase locked-loops (PLL) and showed the possibility of the existence of hidden oscillations in PLL. At the end of the last century, the difficulties in analyzing hidden oscillations arose in simulations of drilling systems and aircraft's control systems (anti-windup) which caused crashes. Further investigations on hidden oscillations were greatly encouraged by the present authors' discovery, in 2010 (for the first time), of chaotic hidden attractor in Chua's circuit. This survey is dedicated to efficient analytical–numerical methods for the study of hidden oscillations. Here, an attempt is made to reflect the current trends in the synthesis of analytical and numerical methods.

scholarly journals Kepler, Newton and numerical analysis

Acta Numerica ◽  
2010 ◽  
Vol 19 ◽  
pp. 561-598 ◽  
Author(s):  
G. Wanner
Keyword(s):  
Numerical Analysis ◽  
Numerical Methods ◽  
Differential Equations ◽  
Optimization Problems ◽  
Modern Science ◽  
Planetary Motion ◽  
Kepler's Laws ◽  
Mathematical Problems ◽  
Kepler’S Laws

Numerical methods are usually constructed for solving mathematical problems such as differential equations or optimization problems. In this contribution we discuss the fact that numerical methods, applied inversely, were also important inestablishingthese models. We show in detail the discovery of the laws of planetary motion by Kepler and Newton, which stood at the beginning of modern science. The 400th anniversary of the publication of Kepler's laws (1609) is a good occasion for this investigation.

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