A model weighted adaptive neural backstepping sliding-mode controller for cooperative manipulator system

Abstract

Since dynamic model can be obtained through various approaches with certain different accuracies, the accuracy of model in simulation plant compared to reality is found to be influential but seldom concerned under robotic cooperation. This may result in acceptable simulation but unsatisfactory practical application. Based on different dynamics' demands or accuracies, an inverse model-based backstepping sliding-mode controller and another model-free controller are derived using feedforward radial basis function neural networks to compensate the uncertain dynamics of the system. Modelbased algorithm is found to provide much more satisfactory contribution than that of model free. Thus, a model weight is introduced to adjust contribution of inverse dynamics and neural compensation, and a novel model weighted adaptive neural backstepping sliding-mode controller is proposed to cope with different dynamics' demands or accuracies. The neural networks require no off-line learning and it is shown that they can deal well with the dynamic uncertainty, joint friction, and external disturbances. With Lyapunov stability approach, these controllers are proved to be robust, both position/force convergence and tracking stability are guaranteed. Simulation and analysis indicate that these controllers provide good tracking performance in position and internal force. Moreover, the model weighted controller achieves better tracking accuracy and stability under various degrees of model errors.

Distributed Adaptive Neural Consensus Tracking Control for Multiple Euler-Lagrange Systems with Unknown Control Directions

“…Theorem 1. For the ELS (1) with Assumptions 1-2, then the ξ i,1 in (11), α i,1 in (13), u i in (14), and θ i in (16)…”

“…Backstepping is one of the most commonly used approaches for consensus control of multiple ELSs [11][12][13][14], such as [12] considered the backstepping-based attitude consensus control for a group of flexible spacecraft; [13] proposed the backstepping-based adaptive sliding mode control approach for multiple manipulators; [14] studied the backstepping-based synchronization of uncertain networked Lagrangian systems. However, for the traditional distributed backstepping, the state is used as the virtual input for each subsystem, and the derivatives of virtual input are used in the next step.…”

Adaptive fuzzy-neural network effectively disturbance compensate in sliding mode control for dual arm robot

“…Sliding Mode Control (SMC) [1] has been applied to the robot's load-carrying movement. Additionally, Adaptive Control (AC) methods have been applied to address disturbances affecting DAR [2][3][4][5]. Besides, nonlinear backstepping control algorithm with adaptive fuzzy control laws to adjust controller parameters is proposed in [2].…”

“…Besides, nonlinear backstepping control algorithm with adaptive fuzzy control laws to adjust controller parameters is proposed in [2]. In [3,4], the application of neural network learning rules is suggested to control stable systems impacted by disturbances. In [6], the authors designed a Backstepping Sliding Mode Control via Nonlinear Disturbance Observer.…”