Kinematic and dynamic adaptive control of a nonholonomic mobile robot using a RNN

In this paper, an adaptive neurocontrol system with two levels is proposed for the motion control of a nonholonomic mobile robot. In the first level, a recurrent network improves the robustness of a kinematic controller and generates linear and angular velocities, necessary to track a reference trajectory. In the second level, another network converts the desired velocities, provided by the first level, into a torque control. The advantage of the control approach is that, no knowledge about the dynamic model is required, and no synaptic weight changing is needed in presence of robot´s parameters variation. This capability is acquired through prior ´meta-learning´. Simulation results are demonstrated to validate the robustness of the proposed approach.