Ludmila V. Litvintseva

Soft computing for the intelligent robust control of a robotic unicycle with a new physical measure for mechanical controllability

Abstract The posture stability and driving control of a human-riding-type unicycle have been realized. The robot unicycle is considered as a biomechanical system using an internal world representation with a description of emotion, instinct and intuition mechanisms. We introduced intelligent control methods based on soft computing and confirmed that such an intelligent control and biological instinct as well as intuition together with a fuzzy inference is very important for emulating human behaviors or actions. Intuition and instinct mechanisms are considered as global and local search mechanisms of the optimal solution domains for an intelligent behavior and can be realized by genetic algorithms (GA) and fuzzy neural networks (FNN) accordingly. For the fitness function of the GA, a new physical measure as the minimum entropy production for a description of the intelligent behavior in a biological model is introduced. The calculation of robustness and controllability of the robot unicycle is presented. This paper provides a general measure to estimate the mechanical controllability qualitatively and quantitatively, even if any control scheme is applied. The measure can be computed using a Lyapunov function coupled with the thermodynamic entropy change. Interrelation between Lyapunov function (stability condition) and entropy production of motion (controllability condition) in an internal biomechanical model is a mathematical background for the design of soft computing algorithms for the intelligent control of the robotic unicycle. Fuzzy simulation and experimental results of a robust intelligent control motion for the robot unicycle are discussed. Robotic unicycle is a new Benchmark of non-linear mechatronics and intelligent smart control.

Intelligent robust control of a robotic unicycle based on a new physical measure for mechanical controllability

The posture stability driving control of a human riding-type unicycle has been realized. The robotic unicycle is considered as a biomechanical system using an internal world representation with a description of emotion, instinct and intuition mechanisms. We introduced intelligent control methods based on soft computing, and confirmed that such an intelligent control and biological instinct as well as intuition together with a fuzzy inference is very important for emulating human behaviors or actions. For the fitness function of the generic algorithm, a new physical measure of the minimum entropy production for a description of the intelligent behavior in a biological model is introduced. The calculation of robustness and controllability of the robotic unicycle is presented. This paper provides a general measure to estimate the mechanical controllability both qualitatively and quantitatively, even if any control scheme is applied. The measure can be computed using a Lyapunov function coupled with the therm...

Design of self-organized intelligent control systems based on quantum fuzzy inference: intelligent system of systems engineering approach

This report presents a generalized design strategy of intelligent robust control systems based on quantum/soft computing technologies that enhance robustness of fuzzy controllers by supplying a self-organizing capability. It is demonstrated that fuzzy controllers prepared to maintain control object in the prescribed conditions are often fail to control when such a conditions are dramatically changed. We propose the solution of such kind of problems by introducing a generalization of strategies in fuzzy inference from a set of pre-defined fuzzy controllers by new quantum fuzzy inference based systems (prototype of intelligent system of systems engineering). We stress our attention on the robustness features of intelligent control systems.

A mobile robot for service use: behaviour simulation system and intelligent control

The structure of hardware and software of AI control system of a mobile robot for service use are described. Hardware of the mobile robot described include an autonomous wheel vehicle and a five degree of freedom manipulator. The software of the AI control system is based on soft computing including fuzzy control rules, fuzzy neural network and genetic algorithms. The intelligent control of cooperative motion between the autonomous vehicle and manipulator realises flexible operations such as navigation of a mobile robot in presence of static and dynamic obstacles, processes of opening door in rooms and pushing buttons of an elevator. New hierarchical structure of the AI control system includes direct human-robot communication line based on natural language and cognitive graphics, and a generator of virtual reality for simulation of artificial life conditions for the mobile service robot. Simulation and experimental results of navigation and technical operations with the manipulator mobile service robot used in office building are described.

Soft computing algorithms for intelligent control of a mobile robot for service use: Part 1: Direct human-robot communications and managing system for cooperative control

Abstract The arrangement principles and design methodology on soft computing for complex control framework of AI control system are introduced. The basis of this methodology is computer simulation of dynamics for mechanical robotic system with the help of qualitative physics and search for possible solutions by genetic algorithms (GA). New approach for direct human-robot communication with natural language (NL) and cognitive graphics is introduced. Active adaptation block which helps to mobile robot to learn a new actions and scripts based on soft computing as fuzzy neural networks, fuzzy control and genetic algorithms are proposed.

Intelligence computing for direct human-robot communication using natural language and cognitive graphics

A direct human-robot communication system based on natural language (NL) and cognitive graphics (CG) as a part of a new hierarchical structure of an AI control system of a mobile robot for service use (MRSU) is developed. The software of the simulation system for direct human-robot communication and MRSU behavior based on NL and CG is described. The NL and CG are used for description and representation of possible external worlds in the robot artificial life. This system allows us to evaluate the control algorithms of real time robot behavior and to reduce difficulties connected with such troubles as robot collisions with real objects and robot hardware damage. The mathematical background of direct NL human-robot communication and robot behavior simulation system is knowledge engineering based on spatio-temporal and action logics, default reasoning, cognitive graphics and soft computing. The main concepts, structure, conceptual model of the simulation system for description of the artificial life of the MRSU are discussed. A simulation example and real experimental results are described.