Considering the compliance control problem of hexapod robot under different environment, a control strategy based on the adaptive-fuzzy control algorithm was raised. Based on the model of robot structure and impedance control, the indirect adaptive control algorithm was derived. And through the analysis of its parameters, it could be noticed that the algorithm does not meet the requirements of the robot compliance control in a complex environment. According to this situation, the fuzzy control algorithm was used to modify the parameters of adaptive control and satisfied system response could be obtained based on the adjustment in real time according to the difference between input and output. The comparative analysis of traditional indirect adaptive control and the improved adaptive-fuzzy control algorithm was presented. It can be verified that not only desired contact force can be tracked when the environmental parameters are changing, but also small contact impact and high steady-state accuracy can be guaranteed under the fluctuations in the body height. The control strategy has great significance to enhance the adaptability of the hexapod robot.
ZHU Ya-guang, JIN Bo, LI Wei. Leg compliance control of hexapod robot based on adaptive-fuzzy control. JOURNAL OF ZHEJIANG UNIVERSITY (ENGINEERING SCIENCE), 2014, 48(8): 1419-1426.
［1］ LIN Y, SONG S M. Learning hybrid position/force control of a quadruped walking machine using a CMAC neural network ［J］. Journal of Robotic Systems, 1997, 14(6): 483-499.
［2］ KLEIN C A, OLSON K W, PUGH D R. Use of force and attitude sensors for locomotion of a legged vehicle over irregular terrain ［J］. The International Journal of Robotics Research, 1983, 2(2): 317.
［3］ IRAWAN A, NONAMI K. Optimal impedance control based on body inertia for a hydraulically driven hexapod robot walking on uneven and extremely soft terrain ［J］. Journal of Field Robotics, 2011, 28(5): 690-713.
［4］ SILVA M F, MACHADO J A, BARBOSA R S. Complex-order dynamics in hexapod locomotion［J］. Signal processing, 2006, 86(10): 2785-2793.
［5］ HUANG Q J, NONAMI K. Humanitarian mine detecting six-legged walking robot and hybrid neuro walking control with position/force control ［J］. Mechatronics, 2003, 13(8): 773-790.
［6］ RNNAU A, KERSCHER T, DILLMANN R. Dynamic Position/Force Controller of a Four Degree-of-Freedom Robotic Leg ［C］∥ Robot Motion and Control 2011. London: Springer, 2012: 117-126.
［7］ OKU M, KOSEKI H, OHROKU H, et al. Rough terrain locomotion control of hydraulically actuated hexapod robot COMET-IV ［C］∥ Proceedings of 2008 JSME Conference on Robotics and Mechatronics (ROBOMEC 2008). Nagano, Japan. ICRA, 2008.
［8］ PAVONE M, ARENA P, FORTUNA L, et al. Climbing obstacle in bio‐robots via CNN and adaptive attitude control ［J］. International Journal Of Circuit Theory And Applications, 2006, 34(1): 109-125.
［9］ HOGAN N. Impedance control of industrial robots ［J］. Robotics and Computer-Integrated Manufacturing, 1984, 1(1): 97-113.
［10］ KAZEROONI H. Automated roboting deburring using electronic compliancy; Impedance control［C］∥ Proceedings of IEEE International Conference on Robotics and Automation. North Carolina: IEEE, 1987, 4: 1025-1032.
［11］ YIN P, WANG P, LI M, et al. A novel control strategy for quadruped robot walking over irregular terrain［C］ ∥ Robotics, Automation and Mechatronics (RAM), 2011 IEEE Conference on. Qingdao: IEEE, 2011, 4: 184-189.
［12］ GALVEZ J A, ESTREMERA J, GONZALEZ DE SANTOS P. A new legged-robot configuration for research in force distribution ［J］. Mechatronics, 2003, 13(8): 907-932.
［13］ PALIS F, RUSIN V, SCHNEIDER A. Adaptive impedance/force control of legged robot systems ［C］∥ Proc. Int. Conf. Climbing and Walking Robots. Kalsruhe, Germany： Prof. Engineering Publ, 2001: 323-329.
［14］ YONEDA K, IIYAMA H, Hirose S. Sky-hook suspension control of a quadruped walking vehicle ［C］ ∥ Robotics and Automation, 1994. Proceedings., 1994 IEEE International Conference on. San Diego, USA: IEEE, 1994: 999-1004.
［15］ LIN Y, SONG S M. Learning hybrid position/force control of a quadruped walking machine using a CMAC neural network ［J］. Journal of Robotic Systems, 1997, 14(6): 483-499.
16］ HUANG Q, FUKUHARA Y. Posture and vibration control based on virtual suspension model using sliding mode control for six-legged walking robot ［C］.∥ 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems. Beijing: IEEE, 2006: 5232-5237.
［17］ GORINEVSKY D M, SHNEIDER A Y. Force control in locomotion of legged vehicles over rigid and soft surfaces ［J］. The International Journal of Robotics Research, 1990, 9(2): 423.
［18］ ZIELINSKA T, HENG J. Development of a walking machine: mechanical design and control problems ［J］. Mechatronics, 2002, 12(5): 737-754.
［19］ 金波,陈诚,李伟.基于能耗优化的六足步行机器人力矩分配［J］浙江大学学报:工学版, 2012, 46(07): 1168-1174.
JIN Bo, CHEN Cheng ,LI Wei, Optimization of energy-efficient torque distribution for hexapod walking robot ［J］. Journal of Zhejiang University: Engineering Science, 2012, 46(07): 1168-1174.
［20］ SERAJI H, COLBAUGH R. Force tracking in impedance control ［J］. The International Journal of Robotics Research, 1997, 16(1): 97-117.
［21］ SURDILOVIC D, COJBASIC Z. Robust robot compliant motion control using intelligent adaptive impedance approach ［C］∥ Robotics and Automation, 1999. Proceedings. 1999 IEEE International Conference on. Detroit, USA: IEEE, 1999, 3: 2128-2133.
