Computational Intelligence with New Physical Controllability Measure for Robust Control Algorithm of Extension- Cableless Robotic Unicycle
V.S. Ulyanov*, K. Yamafuji*, S.V. Ulyanov** and K. Tanaka*
* Department of Mechanical and Control Engineering, The University of Electro-Communications, 1-5-1Chofugaoka,Chofu, Tokyo 182-8585, Japan
** Research & Development Office , Yamaha Motor Europe N.V., Polo Didattico e di Ricerca di Crema, Via Bramante, 65-26013 CREMA (CR) - Italy
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 extension-cableless robotic unicycle, uses a thermodynamic approach 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, extension-cableless robotic unicycle.