Computational Intelligence with New Physical Controllability Measure for Robust Control Algorithm of Extension- Cableless Robotic Unicycle

1999 ◽  
Vol 3 (2) ◽  
pp. 136-147
Author(s):  
V.S. Ulyanov ◽  
◽  
K. Yamafuji ◽  
S.V. Ulyanov ◽  
K. Tanaka ◽  
...  
Keyword(s):  
Robust Control ◽  
Entropy Production ◽  
Production Rate ◽  
Fitness Function ◽  
Control Object ◽  
Object Motion ◽  
Entropy Production Rate ◽  
Minimum Entropy ◽  
Physical Measure ◽  
Minimum Entropy Production

The biomechanical robotic unicycle system uses internal world representation described by emotion, instinct, and intuition. The basic intelligent control concept for a complex nonlinear nonholonomic biomechanical systems, as benchmark the <I>extension-cableless robotic unicycle,</I> uses a <I>thermodynamic approach</I> to study optimum control processes in complex nonlinear dynamic systems is represented here. An algorithm for calculating the entropy production rate is developed. A new physical measure, the minimum entropy production rate, is used as a Genetic Algorithm (GA) fitness function to calculate robotic unicycle robustness controllability and intelligent behavior. The interrelation between the Lyapunov function - a measure of stochastic stability - and the entropy production rate - the physical measure of controllability - in the biomechanical model is the mathematical background for designing soft computing algorithms in intelligent robotic unicycle control. The principle of minimum entropy production rate in control systems and control object motion in general is a new physical concept of smart robust control for the complex nonlinear nonholonomic biomechanical system, as benchmark, <I>extension-cableless robotic unicycle.</I>

Computational Intelligence for Robust Control Algorithms of Complex Dynamic Systems with Minimum Entropy Production

1999 ◽  
Vol 3 (2) ◽  
pp. 82-98 ◽  
Author(s):  
S.V. Ulyanov ◽  
◽  
K. Yamafuji ◽  
V.S. Ulyanov ◽  
I. Kurawaki ◽  
...  
Keyword(s):  
Nonlinear Systems ◽  
Lyapunov Function ◽  
Entropy Production ◽  
Production Rate ◽  
Dynamic Systems ◽  
Fitness Function ◽  
Object Motion ◽  
Entropy Production Rate ◽  
Minimum Entropy ◽  
Minimum Entropy Production

Our thermodynamic approach to the study and design of robust optimal control processes in nonlinear (in general global unstable) dynamic systems used soft computing based on genetic algorithms with a fitness function as minimum entropy production. Control objects were nonlinear dynamic systems involving essentially nonlinear stochastic differential equations. An algorithm was developed for calculating entropy production rate in control object motion and in control systems. Part 1 discusses relation of the Lyapunov function (measure of stability) and the entropy production rate (physical measure of controllability). This relation was used to describe the following qualitative properties and important relations: dynamic stability motion (Lyapunov function), Lyapunov exponent and Kolmogorov-Sinai entropy, physical entropy production rates, and symmetries group representation in essentially nonlinear systems as coupled oscillator models. Results of computer simulation are presented for entropy-like dynamic behavior for typical benchmarks of dynamic systems such as Van der Pol, Duffing, and Holmes-Rand, and coupled oscillators. Parts 2 and 3 discuss the application of this approach to simulation of dynamic entropy-like behavior and optimal benchmark control as a 2-link manipulator in a robot for service use and nonlinear systems under stochastic excitation.

scholarly journals Robotic Unicycle Intelligent Robust Control Pt I: Soft Computational Intelligence Toolkit

2020 ◽  
Vol 2 (1) ◽  
Author(s):  
Sergey Victorovich Ulyanov ◽  
Ulyanov Viktor ◽  
Yamafuji Kazuo
Keyword(s):  
Control System ◽  
Robust Control ◽  
Entropy Production ◽  
Intelligent Control ◽  
Computational Intelligence ◽  
Fitness Function ◽  
Control Object ◽  
Minimum Entropy ◽  
Intelligent Control System ◽  
Minimum Entropy Production

The concept of an intelligent control system for a complex nonlinear biomechanical system of an extension cableless robotic unicycle discussed. A thermodynamic approach to study optimal control processes in complex nonlinear dynamic systems applied. The results of stochastic simulation of a fuzzy intelligent control system for various types of external / internal excitations for a dynamic, globally unstable control object - extension cableless robotic unicycle based on Soft Computing (Computational Intelligence Toolkit - SCOptKBTM) technology presented. A new approach to design an intelligent control system based on the principle of the minimum entropy production (minimum of useful resource losses) determination in the movement of the control object and the control system is developed. This determination as a fitness function in the genetic algorithm is used to achieve robust control of a robotic unicycle. An algorithm for entropy production computing and representation of their relationship with the Lyapunov function (a measure of stochastic robust stability) described.

Soft Computing with New Physical Measure of Controllability for Intelligent Control of the Two-Link Manipulator (1st Report: Study of Free Motion of Nonlinear Control Object)

1997 ◽  
Vol 9 (4) ◽  
pp. 299-303
Author(s):  
Viktor S. Ulyanov ◽  
◽  
Kazuo Yamafuji ◽  
Sergei V. Ulyanov ◽  
Ludmila V. Litvintseva ◽  
...  
Keyword(s):  
Entropy Production ◽  
Intelligent Control ◽  
Force Control ◽  
Control Object ◽  
Free Motion ◽  
Minimum Entropy ◽  
Physical Measure ◽  
Mechanical Motion ◽  
Minimum Entropy Production ◽  
3D Computer Simulation

The free motion of a two-link manipulator as a nonlinear system is studied, and the interrelation between the Lyapunov function (criteria of dynamic stability) and entropy production of a mechanical motion for intelligent robust force control are introduced. A new physical measure of controllability as minimum entropy production is proposed and discussed, and the efficiency of the new physical measure for global control object stability was examined by 3D computer simulation.

scholarly journals Intelligent robust control system of robotic unicycle based on the end-to-end soft computing technology

2019 ◽  
pp. 58-86
Author(s):  
Sergey Ulyanov ◽  
Viktor Ulyanov ◽  
Andrey Reshetnikov ◽  
Olga Tyatyushkina ◽  
Kazuo Yamafuji
Keyword(s):  
Control System ◽  
Robust Control ◽  
Entropy Production ◽  
Soft Computing ◽  
Intelligent Control ◽  
Fitness Function ◽  
Control Object ◽  
Minimum Entropy ◽  
Intelligent Control System ◽  
Production Calculation

The concept of an intelligent control system for a complex nonlinear biomechanical system of an extension cableless robotic unicycle is considered. A thermodynamic approach to study optimal control processes in complex nonlinear dynamic systems is used. The results of stochastic simulation of a fuzzy intelligent control system for various types of external/ internal excitations for a dynamic, globally unstable control object -extension cableless robotic unicycle based on Soft Computing (Computational Intelligence Toolkit) technology are presented. A new approach to design an intelligent control system based on the principle of the minimum entropy production (minimum of useful resource losses) determination in the movement of the control object and the control system is developed. This determination as a fitness function in the genetic algorithm is used to achieve robust control of a robotic unicycle. An algorithm for entropy production calculation and representation of their relationship with the Lyapunov function (a measure of stochastic robust stability) is described.

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