Robust Tracking Control For A Wheeled Mobile Manipulator With Dual Arms Using Hybrid Sliding‐Mode Neural Network

Abstract

In this paper, a robust tracking controller is proposed for the trajectory tracking problem of a dual-arm wheeled mobile manipulator subject to some modeling uncertainties and external disturbances. Based on backstepping techniques, the design procedure is divided into two levels. In the kinematic level, the auxiliary velocity commands for each subsystem are first presented. A sliding-mode equivalent controller, composed of neural network control, robust scheme and proportional control, is constructed in the dynamic level to deal with the dynamic effect. To deal with inadequate modeling and parameter uncertainties, the neural network controller is used to mimic the sliding-mode equivalent control law; the robust controller is designed to compensate for the approximation error and to incorporate the system dynamics into the sliding manifold. The proportional controller is added to improve the system's transient performance, which may be degraded by the neural network's random initialization. All the parameter adjustment rules for the proposed controller are derived from the Lyapunov stability theory and e-modification such that uniform ultimate boundedness (UUB) can be assured. A comparative simulation study with different controllers is included to illustrate the effectiveness of the proposed method.

Artificial Neural Network Based Prediction Model of the Sliding Mode Control in Coordinating Two Robot Manipulators

“…Some researchers have been investigated on some soft computing methods which have been adapted from artificial intelligence such as fuzzy logic, neural network, evolutionary computing and so on [12][13] [14] [15]. A hybrid sliding mode control with neural network has been investigated by several researchers to develop one robot manipulator which was initially proposed [13] and investigated by some researchers [12] [14] [16].…”

Adaptive tracking in mobile robots with input-output linearization

“…Adaptive methods can be implemented adopting several structures regarding the adaptive element: Fuzzy Sets [8], [10], Wavelet Networks [5], Feedforward Neural Networks [4], Radial Basis Function Neural Networks [9], Affine parameter combinations [7], [11]. Adaptive methods are also accompanied by more traditional and complimentary robust techniques like Back-Stepping [4], Sliding Mode [10].…”

Adaptive fuzzy sliding mode controller for Wheeled Mobile Robots

“…Robust sliding mode techniques have been considered recently to improve the performance of the nonlinear controllers [6]. An ideal Sliding Mode Controller (SMC) using a PID sliding surface gives the system dynamics a sufficient robustness with respect to the uncertainties and external disturbances [7].…”