Hybrid position/virtual-force control for obstacle avoidance of wheeled robots using Elman neural network training technique. (7th June 2017) - Zheng, Wei; Wang, Hongbin; Zhang, Zhiming; Lu, Xiaozhao
The hybrid force position control algorithm based on neural network is considered for a class of robot system with nonlinear uncertainties. Compared with previous work, not only the steady-state performance but also the transient-state performance is considered. Firstly, in order to relax the control design dependent on detailed system information, a fast hybrid position/virtual-force controller is presented to build a virtual-force field between the obstacles and robot. The virtual force is the control parameter, which is set to maintain an expected distance between obstacles and the robot with unknown nonlinear and parameter uncertainty. Secondly, in order to alleviate the computation burden of parameter learning, and enhance the dynamic mapping of network ability, the Elman neural network is introduced. The output signal come from hybrid position/virtual-force controller is fed back to Elman neural network. Furthermore, since uncertainties of robot dynamics and obstacle location information, Elman neural network is also used to compensate for uncertainties and improve system stability performance. The control design conditions are relaxed because of the developed dynamic compensator. Finally, both simulations and results of obstacle avoidance are performed to show the potential of the proposed methods.