Optimization of PI and Fuzzy-PI Controllers on Simulation Model of Szabad(ka)-II Walking Robot

The Szabad(ka)-II 18 DOF walking robot and its simulation model is suitable for research into hexapod walking algorithm and motion control. The complete dynamic model has already been built, and is used as a black box for walking optimization in this research. First, optimal straight line walking was chosen as our objective, since the robot mainly moves in this mode. This case can be tested and validated as well on the current version of our robot. An ellipse-based leg trajectory has been generated for this low-cost straight line walking. Currently a simple new Fuzzy-PI controller with three input variables is being constructed and compared with an previously used PI controller. The purpose of defining the rules and its optimization are to obtain a controller that provides walking with higher quality. Both the compared controllers have been optimized together with the parameters of the leg trajectory. The particle swarm optimization (PSO) method was chosen from several methods with our benchmark-based selection research and the help of specific test functions; moreover the previous research (the comparison of genetic algorithm (GA) and PSO) also led to this conclusion.

[1]  E. Burkus,et al.  Autonomous Hexapod Walker Robot "Szabad(ka)" , 2007, 2007 5th International Symposium on Intelligent Systems and Informatics.

[2]  Masao Fukushima,et al.  Tabu Search directed by direct search methods for nonlinear global optimization , 2006, Eur. J. Oper. Res..

[3]  Mustafa Suphi Erden,et al.  Optimal Protraction of a Biologically Inspired Robot Leg , 2011, J. Intell. Robotic Syst..

[4]  Ching-Chang Wong,et al.  PSO-based Motion Fuzzy Controller Design for Mobile Robots , 2008 .

[5]  János C. Fodor,et al.  Structural and gait optimization of a hexapod robot with Particle Swarm Optimization , 2013, 2013 IEEE 11th International Symposium on Intelligent Systems and Informatics (SISY).

[6]  Péter Odry,et al.  Swarm-based optimizations in hexapod robot walking , 2014, 2014 IEEE 9th IEEE International Symposium on Applied Computational Intelligence and Informatics (SACI).

[7]  R. Venkata Rao,et al.  Teaching-learning-based optimization: A novel method for constrained mechanical design optimization problems , 2011, Comput. Aided Des..

[8]  Istvan Kecskes,et al.  Full kinematic and dynamic modeling of “Szabad(ka)-Duna” hexapod , 2009, 2009 7th International Symposium on Intelligent Systems and Informatics.

[9]  Gregory J. Barlow,et al.  Article in Press Robotics and Autonomous Systems ( ) – Robotics and Autonomous Systems Fitness Functions in Evolutionary Robotics: a Survey and Analysis , 2022 .

[10]  Singiresu S. Rao.  Engineering Optimization : Theory and Practice , 2010 .

[11]  Kalyanmoy Deb,et al.  Optimal path and gait generations simultaneously of a six-legged robot using a GA-fuzzy approach , 2002, Robotics Auton. Syst..

[12]  I. Kecskes,et al.  Fuzzy route control of dynamic model of four-wheeled mobile robot , 2012, 2012 4th IEEE International Symposium on Logistics and Industrial Informatics.

[13]  Stefan Preitl,et al.  Evolutionary optimization-based tuning of low-cost fuzzy controllers for servo systems , 2013, Knowl. Based Syst..

[14]  Ming-Shyan Wang,et al.  Fuzzy Logic Control Design for a Stair-Climbing Robot , 2009 .

[15]  Mohammad Teshnehlab,et al.  Training ANFIS as an identifier with intelligent hybrid stable learning algorithm based on particle swarm optimization and extended Kalman filter , 2009, Fuzzy Sets Syst..

[16]  Pakize Erdogmus,et al.  Heuristic Optimization Algorithms in Robotics , 2012 .

[17]  Nikolaos V. Sahinidis,et al.  Derivative-free optimization: a review of algorithms and comparison of software implementations , 2013, J. Glob. Optim..

[18]  Hossein Nezamabadi-pour,et al.  GSA: A Gravitational Search Algorithm , 2009, Inf. Sci..

[19]  Peter Odry,et al.  Simple definition of adequate fixed time-step size of Szabad(ka)-II robot model , 2013, 2013 IEEE 9th International Conference on Computational Cybernetics (ICCC).

[20]  Tibor Csendes,et al.  Noname manuscript No. (will be inserted by the editor) The GLOBAL Optimization Method Revisited , 2022 .

[21]  Rong-Jong Wai,et al.  Robust fuzzy neural network control for nonlinear motor-toggle servomechanism , 2003, Fuzzy Sets Syst..

[22]  Okyay Kaynak,et al.  Fuzzy adaptive sliding mode control of a direct drive robot , 1996, Robotics Auton. Syst..

[23]  Peter Odry,et al.  PSO and GA optimization methods comparison on simulation model of a real hexapod robot , 2013, 2013 IEEE 9th International Conference on Computational Cybernetics (ICCC).

[24]  Istvan Kecskes,et al.  Protective Fuzzy Control of Hexapod Walking Robot Driver in Case of Walking and Dropping , 2010 .

[25]  Zoltan Pap,et al.  Optimization of the hexapod robot walking by genetic algorithm , 2010, IEEE 8th International Symposium on Intelligent Systems and Informatics.

[26]  Ching-Chang Wong,et al.  Fuzzy Controller Designed by GA for Two-wheeled Mobile Robots , 2007 .

[27]  Radu-Emil Precup,et al.  A survey on industrial applications of fuzzy control , 2011, Comput. Ind..