Neural-Network-Based Robust Tracking Control for Condenser Cleaning Crawler-Type Mobile Manipulators with Delayed Uncertainties

In this paper, the problem of the robust tracking for two-arm condenser cleaning crawler-type mobile manipulators (CCCMM) with delayed angle-velocity uncertainties is original investigated. The two-arm condenser cleaning crawler-type mobile manipulators are composed of a crawler-type mobile platform and two-arm industrial manipulators.The uncertainty is nonlinear time-varying and does not require a matching condition. A wavelet transform and probabilistic neural network (WTPNN) system is used to approximate an unknown controlled system from the strategic manipulation of the model following tracking errors. Based on the Lyapunov method and the linear matrix inequality (LMI) approach, several sufficient conditions, which guarantee the state variables of the closed loop system to converge, globally, uniformly and exponentially, to a ball in the state space with any pre-specified convergence rate, are derived. Experiment results are given to illustrate the superior control performance of the proposed intelligent control method.