Unified Model and Robust Neural-Network Control of Omnidirectional Mobile Manipulators

Abstract

As a typical holonomic mechanical system, the omnidirectional mobile manipulator, due to its large-scale mobility and dexterous manipulability, has attracted lots of attention in the last decades. While the omnidirectional mobile manipulator provides many advantages, modeling and control of such a system are very challenging because of its complicated mechanism. In this paper, a unified dynamic model is developed by Lagrangian formalism. In terms of the proposed model, a tracking controller, based on computed torque control (CTC) method and radial basis function neural-network (RBFNN), is presented subsequently. Although CTC is an effective motion control strategy for mobile manipulators, it requires precise models. To handle the unmodeled dynamics and the external disturbance, a RBFNN, serving as a compensator, is adopted. This proposed controller combines the advantages of CTC and RBFNN. Simulation results show the correctness of the proposed model and the effectiveness of the control approach.