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首页> 外文期刊>Journal of Computer and Systems Sciences International >A Soft Computing-Based Mathematical Model of an Intelligent Control System for a Complex, Globally Unstable Object
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A Soft Computing-Based Mathematical Model of an Intelligent Control System for a Complex, Globally Unstable Object

机译：基于软计算的复杂全局不稳定对象智能控制系统数学模型

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摘要

In this paper, the concept of an intelligent control system for the complex nonlinear biomechanical system of a robotic unicycle is presented. The results of stochastic modeling of the fuzzy intelligent control sys- tem are demonstrated under various types of external and internal random perturbations for a dynamic, globally unstable object represented by a robotic unicycle. The thermodynamic approach to the investigation of optimal control processes in complex nonlinear dynamic systems is employed. A relationship between the entropy pro- duction and the Lyapunov function (the measure of stochastic stability) is established. A new approach to designing intelligent control systems, which is based on the principle of minimum entropy production during the motion of the control object and in the control system itself, is developed. The minimum of entropy produc- tion is the fitness function of a genetic algorithm, which allows one to construct a robust control for a robotic unicycle.
机译：在本文中，提出了用于机器人单轮车的复杂非线性生物力学系统的智能控制系统的概念。在以机器人独轮车为代表的动态全局不稳定对象的各种外部和内部随机扰动下，证明了模糊智能控制系统的随机建模结果。采用热力学方法研究复杂非线性动力学系统的最优控制过程。建立了熵产生与Lyapunov函数（随机稳定性的量度）之间的关系。提出了一种新的设计智能控制系统的方法，该方法基于在控制对象运动期间以及控制系统本身中产生最小熵的原理。最小的熵产生是遗传算法的适应度函数，它允许人们为机器人独轮车构建一个健壮的控件。

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