Adaptive Fuzzy Control for Coordinated Multiple Robots With Constraint Using Impedance Learning

• Published in: IEEE transactions on cybernetics Vol. 49; no. 8; pp. 3052 - 3063
• Main Authors: Kong, Linghuan, He, Wei, Yang, Chenguang, Li, Zhijun, Sun, Changyin
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.08.2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adaptive control; Algorithms; Artificial neural networks; Computer simulation; Fuzzy control; Fuzzy logic; Fuzzy neural networks; fuzzy systems; Impedance; impedance learning; Liapunov functions; Lyapunov methods; Machine learning; Multiple robots; Neural networks; Robot control; Robot kinematics; Robots; time-varying constraint; Tracking errors; Unknown environments
• ISSN: 2168-2267; 2168-2275; 2168-2275
• DOI: 10.1109/TCYB.2018.2838573

In this paper, we investigate fuzzy neural network (FNN) control using impedance learning for coordinated multiple constrained robots carrying a common object in the presence of the unknown robotic dynamics and the unknown environment with which the robot comes into contact. First, an FNN learning algorithm is developed to identify the unknown plant model. Second, impedance learning is introduced to regulate the control input in order to improve the environment-robot interaction, and the robot can track the desired trajectory generated by impedance learning. Third, in light of the condition requiring the robot to move in a finite space or to move at a limited velocity in a finite space, the algorithm based on the position constraint and the velocity constraint are proposed, respectively. To guarantee the position constraint and the velocity constraint, an integral barrier Lyapunov function is introduced to avoid the violation of the constraint. According to Lyapunov's stability theory, it can be proved that the tracking errors are uniformly bounded ultimately. At last, some simulation examples are carried out to verify the effectiveness of the designed control.