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

Sergey Victorovich Ulyanov (Dubna State University, Institute of system analysis and management, Dubna, Moscow, 141980, Russia;INESYS LLC (EFKO GROUP), Ovchinnikovskaya naberezhnaya 20, Bld 1, Business Centre “Central City Tower”, Moscow, 115035, Russia)
Ulyanov Viktor (INESYS LLC (EFKO GROUP), Ovchinnikovskaya naberezhnaya 20, Bld 1, Business Centre “Central City Tower”, Moscow, 115035, Russia; NUST MISIS IYS Lab, Leninskiy prospekt 4, Moscow, 119049, Russia)
Yamafuji Kazuo (Dept. of Mechanical and Intelligent Control Eng., University of Electro-Communications, 1-5-1 Chofu, Chofugaoka, 182 Tokyo, Japan)

Article ID: 1556

DOI: https://doi.org/10.30564/aia.v2i1.1556

Abstract


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.


Keywords


Robotics unicycle; Intelligent control systems; Essentially nonlinear model; Globally unstable model; Stochastic simulation; Soft computing

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References


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