Adaptive neural network based dynamic surface control for uncertain dual arm robots
This work addresses control challenges for dual arm robots in uncertain environments, representing an incremental improvement by combining existing methods for enhanced robustness.
The paper tackles the problem of controlling nonlinear manipulation motions of a dual arm robot under system uncertainties, proposing an adaptive neural network-based dynamic surface control approach that robustly tracks desired trajectories and guarantees stability, with results shown to be highly promising in a synthetic environment.
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.