  The paper discusses an adaptive strategy to effectively control nonlinear
manipulation motions of a dual arm robot (DAR) under system uncertainties
including parameter variations, actuator nonlinearities and external
disturbances. It is proposed that the control scheme is first derived from the
dynamic surface control (DSC) method, which allows the robot's end-effectors to
robustly track the desired trajectories. Moreover, since exactly determining
the DAR system's dynamics is impractical due to the system uncertainties, the
uncertain system parameters are then proposed to be adaptively estimated by the
use of the radial basis function network (RBFN). The adaptation mechanism is
derived from the Lyapunov theory, which theoretically guarantees stability of
the closed-loop control system. The effectiveness of the proposed RBFN-DSC
approach is demonstrated by implementing the algorithm in a synthetic
environment with realistic parameters, where the obtained results are highly
promising.

Robotics;Artificial Intelligence;Systems and Control;Systems and Control