Adaptive robust control based on RBF neural networks for duct cleaning robot

In this paper, a control strategy for duct cleaning robot in the presence of uncertainties and various disturbances is proposed which combines the advantages of neural network technique and advanced adaptive robust theory. First of all, the configuration of the duct cleaning robot is introduced and the dynamic model is obtained based on the practical duct cleaning robot. Second, the RBF neural network is used to identify the unstructured and dynamic uncertainties due to its strong ability to approximate any nonlinear function to arbitrary accuracy. Using the learning ability of neural network, the designed controller can coordinately control the mobile plant and cleaning arm of duct cleaning robot with different dynamics efficiently. The neural network weights are only tuned on-line without tedious and lengthy off-line learning. Then, an adaptive robust control scheme based on RBF neural network is proposed, which ensures that the trajectories are accurately tracked even in the presence of external disturbances and uncertainties. Finally, based on the Lyapunov stability theory, the stability of the whole closed-loop control system, and the uniformly ultimately boundedness of the tracking errors are all strictly guaranteed. Moreover, simulation and experiment results are given to demonstrate that the proposed control approach can guarantee the whole system converges to desired manifold with well performance.