Neural adaptive with impedance learning control for uncertain cooperative multiple robot manipulators

Abstract

In this paper, an adaptive radial basis function neural network (RBFNN) control scheme is studied for the desired tracking of multiple robot manipulators with carrying a common object in joint-space. First, a non-zero time-varying parameter is introduced into the RBFNN, as well as a novel universal approximation of RBFNN with this non-zero parameter is introduced. Second, a switching control scheme based on this approximation property is designed. When the states of uncertainties terms leave the universal approximation domains, the suggested adaptive laws established by a sliding surface would pull them back, then the uncertain nonlinear functions are approximated by the RBFNNs in the domains. With this new structure of control technique, the limitation of a finite universal approximation can be broken. Third, the cooperative multiple manipulators can track the desired trajectory determined through impedance learning, which is used to modulate the control input in order to optimize the environment robot interaction. Finally, simulation results are shown to demonstrate the effectiveness in terms of expected tracking performance and position tracking errors constraints.