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



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.


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


[1] Schoonwinkel A. Design and test of a computer stabilized unicycle. Ph. D. dissertation of Stanford Univ., USA, 1987.

[2] David William Vos. Nonlinear control of an autonomous unicycle robot: practical issues. Ph. D. dissertation of Massachusetts Institute of Technology, 1992.

[3] Ulyanov S.V., Sheng Z.Q., Yamafuji K. Fuzzy Intelligent control of robotic unicycle: A New benchmark in nonlinear mechanics. Proc. Intern. Conf. on Recent Advanced Mechatronics, Istanbul, Turkey, 1995, 2: 704-709.

[4] Ulyanov S.V., Sheng Z.Q., Yamafuji K., Watanabe S., Ohkura T. Self-organization fuzzy chaos intelligent controller for a robotic unicycle: A New benchmark in AI control. Proc. of 5th Intelligent System Symposium: Fuzzy, AI and Neural Network Applications Technologies (FAN Symp,’95), Tokyo., 1995: 41-46.

[5] Sheng Z.Q., Yamafuji K., Ulyanov S.V. Study on the stability and motion control of a unicycle. Pts 3,4,5. JSME International Journal, 1996, 39(3): 560-568; 569-576; Journal of Robotics & Mechatronics, 1996, 8(6): 571-579.

[6] Panfilov S.A., Ulyanov V.S., Litvintseva L.V., Ulyanov S.V., Kurawaki I. Robust Fuzzy Control of Non-Linear Dynamic Systems Based on Soft Computing with Minimum of Entropy Production Rate. Proc. Int. Conf. ICAFS 2000, Siegen, Germany, 2000: 59-75.

[7] Rouch N., Habets P. Laloy M. Stability Theory by Lyapunov’s Direct Method. Berlin, Springer, 1977.

[8] Ulyanov V.S., Panfilov S.A., Ulyanov S.V. etc. Principle of minimum entropy production in applied soft computing for advanced intelligent robotics and mechatronics. Soft Computing, 2000, 2: 141-146.

[9] Ulyanov V.S., Yamafuji K., Ulyanov S.V., Tanaka K. Computational intelligence with new physical controllability measure for robust control algorithms of extension-cableless robotic unicycle, Journal of Advanced Computational Intelligence. 1999, 3(2): 82 - 98.

[10] Ulyanov S.V., Yamafuji K. Fuzzy Intelligent emotion and instinct control of a robotic unicycle, Proc. 4th Intern. Workshop on Advanced Motion Control. Mie, Japan, 1996, 1: 127-132.

[11] Ulyanov S.V., Watanabe S., Yamafuji K., Ohkura T. A new physical measure for mechanical controllability and intelligent control of a robotic unicycle on basis of intuition, instinct and emotion computing. Proc. 2nd Intern. Conf. on Application on Fuzzy Systems and Soft Computing (ICAF’96), Siegen, Germany, 1996: 49-58.

[12] Ulyanov S.V., Watanabe S., Ulyanov V.S., Yamafuji K., Litvintseva L.V., Rizzotto G.G. Soft computing for the intelligent control of a robot unicycle based on a new physical measure for mechanical controllability. Soft Computing, 1998, 2(2): 73-88.

[13] Lauk M., Chow C.C., Pavlik A.E., Colloins J.J. Human balance out of equilibrium: Non-equilibrium statistical mechanics of posture control. Physical Review Letters, 1998, Vol. 80(2): 413-416.

[14] Ulyanov S.V., Yamafuji K., Ulyanov V.S., Computational intelligence for robust control algorithms of complex dynamic systems with minimum entropy production. Part1: simulation of entropy-like dynamic behavior and Lyapunov stability. Journal of Advanced Computational Intelligence, 1999, 3(2): 82-98.

[15] Murata Manufacturing Company, Ltd. MURATA GIRL, Japan, 2011.

[16] Wieser E. Machine learning for a miniature robotic unicycle. Master of science thesis of Cambridge University, 2017, UK.

[17] De Vries J.F. Redesign & implementation of a moment exchange unicycle robot. Master of science thesis of Twente University, Netherlands, 2018.

[18] Kim S., Lee J., Hwang J. et al. Dynamic modeling and performance improvement of a unicycle robot. J. Inst. of Control, Robotics and Systems, 2010, 16(11): 1074-1081.

[19] Ulyanov S.V. et al. System and method for stochastic simulation of nonlinear dynamic systems with a high degree of freedom for soft computing applications. USA Patent Application Publication - US 2004/0039555 Al., 2004.

[20] Ulyanov S.V. et al. Soft computing optimizer of intelligent control system structures. Patent No.: US 2005/0119986 A1, Fil.: Jul. 23, 2004, Pub. Date: Jun. 2, 2005.

[21] Ulyanov S.V. et al. System for soft computing simulation. Patent No.: US 2006/0218108 A1, Fil.: Oct. 4, 2005, Pub. Date: Sep. 28, 2006.

[22] Ulyanov S.V. et al. Soft computing optimizer of intelligent control system structures. Patent No.: WO 2005/013019 A2, Priority data: 25 July 2002, Pub. Date: 10.02. 2005.

[23] Ulyanov S.V. System and method for control using quantum soft computing)[P]. US Patent No 7,383,235 B1, 2003; EP PCT 1 083 520 A2, 2001; Efficient simulation system of quantum algorithm gates on classical computer based on fast algorithm[P]. US Patent No 883 2006/0224547 A1, 2006.

[24] itvintseva L.V., Ulyanov S.V. Quantum fuzzy inference for knowledge base design in robust intelligent controllers[J]. Journal of Computer and Systems Sciences Intern, 2007, 46(6): 908 - 961.

[25] Ulyanov S.V. Self-organizing quantum robust control methods and systems for situations with uncertainty and risk[P]. Patent US 8788450 B2, 2014.

[26] Ulyanov S.V. Quantum fast algorithm computational intelligence PT I: SW / HW smart toolkit[J]. Artificial Intelligence Advances, 2019, 1(1): 18-43.DOI:

[27] Shen J., Hong D. OmBURo: A novel unicycle robot with active omnidirectional wheel.


How to Cite

Ulyanov, S. V., Viktor, U., & Kazuo, Y. (2020). Robotic Unicycle Intelligent Robust Control Pt I: Soft Computational Intelligence Toolkit. Artificial Intelligence Advances, 2(1), 71–92.


Article Type