Adaptive control of 2-wheeled balancing robot by cerebellar neuronal network model

Abstract

A new adaptive motor controller was constructed, and tested on the control of a 2-wheeled balancing robot in simulation and real world. The controller consists of a feedback (PD) controller and a cerebellar neuronal network model. The structure of the cerebellar model was configured based upon known anatomical neuronal connection in the cerebellar cortex. Namely it consists of 120 granular (Gr) cells, 1 Golgi cell, 6 basket/stellate cells, and 1 Purkinje (Pk) cell. Each cell is described by a typical artificial neuron model that outputs a weighted sum of inputs after a sigmoidal nonlinear transformation. The 2 components of the proposed controller work in parallel, in a way that the cerebellar model adaptively modifies the synaptic weights between Gr and Pk as in the real cerebellum to minimize the output of the PD controller. We demonstrate that the proposed controller successfully controls a 2-wheeled balancing robot, and the cerebellar model rapidly takes over the PD controller in simulation. We also show that an abrupt load change on the robot, which the PD controller alone cannot compensate for, can be adaptively compensated by the cerebellar model. We further confirmed that the proposed controller can be applied to the control of the robot in real world.