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 humanriding-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 system of robotic unicycle based on the end-to-end soft computing technology

“…Для оценки качества управления применялся новый физический принцип: минимум производства энтропии в движении системы и в системе управления [2][3][4][5][7][8][9][10][11][12][13]. Физическая мера энтропии является функцией пригодности в генетическом алгоритме (ГА).…”

“…Данная мера является разностью между производством энтропии самим объектом управления и включенной в него системы управления. Это позволяет адаптировать параметры линейной системы управления к нелинейному объекту управления [7,12].…”

Genetic Algorithm Optimization for Type-2 Non-singleton Fuzzy Logic Controllers

“…Furthermore, most reported designs rely on intelligent control approaches as fuzzy logic control (FLC) [3,19,26,29,30,35,36] and neural networks [11,33]. However the majority of the publications mentioned above, have concentrated on kinematics models for mobile robots, which are controlled by the velocity input, while less attention has been paid to the control problems of non-holonomic dynamics systems, where forces and torques are true inputs: Bloch [4], Driankov et al [10] and Chwa [8], used a sliding mode control to the tracking control problem.…”

Optimization of interval type-2 fuzzy logic controllers for a perturbed autonomous wheeled mobile robot using genetic algorithms

“…Furthermore, most reported designs rely on intelligent control approaches such as fuzzy logic control (FLC) [4,25,34,38,39,45,46] and neural networks [15,43]. However the majority of the publications mentioned above, have concentrated on kinematics models of mobile robots, which are controlled by the velocity input, while less attention has been paid to the control problems of non-holonomic dynamic systems, where forces and torques are the true inputs: Bloch and Drakunov [5,13] and Chwa [10], used a sliding mode control to the tracking control problem.…”