Adaptive control based on IF-THEN rules for grasping force regulation with unknown contact mechanism

An industrial gripping application with unknown contact mechanism is considered as a class of unknown nonlinear discrete-time systems. The control scheme is developed by an adaptive network called multi-input fuzzy rules emulated network (MiFREN) within discrete-time domain. The network structure is directly constructed regarding to IF-THEN rules related to gripper and contact mechanism properties. All adjustable parameters require only the on-line learning phase to improve the closed loop performance. The time varying learning rate is devised for gradient reach with the proof of stability analysis. Furthermore, the estimated sensitivity of system dynamic is directly considered within the parameter adaptation. The experimental system with an industrial parallel grip model WSG-50 validates the performance of the proposed controller.

[1]  M. Ridding,et al.  Effect of human grip strategy on force control in precision tasks , 2005, Experimental Brain Research.

[2]  Yu Sun,et al.  Learning grasping force from demonstration , 2012, 2012 IEEE International Conference on Robotics and Automation.

[3]  Han-Xiong Li,et al.  Feedback-Linearization-Based Neural Adaptive Control for Unknown Nonaffine Nonlinear Discrete-Time Systems , 2008, IEEE Transactions on Neural Networks.

[4]  Jiang Li,et al.  Design and experiment of fruit and vegetable grasping system based on grey prediction control. , 2010 .

[5]  Jagannathan Sarangapani,et al.  Neural Network Control of Nonlinear Discrete-Time Systems , 2018 .

[6]  Jianghua Zheng,et al.  Parallel gripping explicit force control of robot hand with dual fingers , 2011, Proceedings of 2011 International Conference on Electronic & Mechanical Engineering and Information Technology.

[7]  P. Dzitac,et al.  A method to control grip force and slippage for robotic object grasping and manipulation , 2012, 2012 20th Mediterranean Conference on Control & Automation (MED).

[8]  Ioannis Kanellakopoulos,et al.  Active identification for discrete-time nonlinear control. II. Strict-feedback systems , 2002, IEEE Trans. Autom. Control..

[9]  Haris E. Psillakis,et al.  Sampled-Data Adaptive NN Tracking Control of Uncertain Nonlinear Systems , 2009, IEEE Transactions on Neural Networks.

[10]  Jorge Armendariz,et al.  Force feedback controller based on fuzzy-rules emulated networks and Hertzian contact with ultrasound , 2012 .

[11]  Wei Wu,et al.  Convergence analysis of online gradient method for BP neural networks , 2011, Neural Networks.

[12]  S. Jagannathan,et al.  Neural network control of a class of nonlinear discrete time systems with asymptotic stability guarantees , 2009, 2009 American Control Conference.

[13]  R.D. Lorenz,et al.  A direct-drive, robot parts and tooling gripper with high performance force feedback control , 1989, Conference Record of the IEEE Industry Applications Society Annual Meeting,.

[14]  Yu Sun,et al.  5-D force control system for fingernail imaging calibration , 2011, 2011 IEEE International Conference on Robotics and Automation.

[15]  Chin-Teng Lin,et al.  Neural fuzzy systems , 1994 .

[16]  Richard Volpe,et al.  A theoretical and experimental investigation of explicit force control strategies for manipulators , 1993, IEEE Trans. Autom. Control..

[17]  Chidentree Treesatayapun,et al.  A discrete-time stable controller for an omni-directional mobile robot based on an approximated model , 2011 .

[18]  Jyh-Shing Roger Jang,et al.  ANFIS: adaptive-network-based fuzzy inference system , 1993, IEEE Trans. Syst. Man Cybern..