Motor Learning Based on the Cooperation of Cerebellum and Basal Ganglia for a Self-Balancing Two-Wheeled Robot

Abstract

A novel motor learning method is present based on the cooperation of the cerebellum and basal ganglia for the behavior learning of agent. The motor learning method derives from the principle of CNS and operant learning mechanism and it depends on the interactions between the basal ganglia and cerebellum. The whole learning system is composed of evaluation mechanism, action selection mechanism, tropism mechanism. The learning signals come from not only the Inferior Olive but also the Substantia Nigra in the beginning. The speed of learning is increased as well as the failure time is reduced with the cerebellum as a supervisor. Convergence can be guaranteed in the sense of entropy. With the proposed motor learning method, a motor learning system for the self-balancing two-wheeled robot has been built using the RBF neural networks as the actor and evaluation function approximator. The simulation experiments showed that the proposed motor learning system achieved a better learning effect, so the motor learning based on the coordination of cerebellum and basal ganglia is effective.

Advanced Proportional-Integral-Derivative Control Compensation Based on a Grey Estimated Model in Dynamic Balance of Single-Wheeled Robot

“…According to Newton's laws of motion, an external force produces an angular acceleration x(t) of the single-wheel car. While the single wheel robot becomes stable, the force along the direction perpendicular to the ground can be estimated by the model inclination angle and the acceleration of the wheel movement [15,17,18].…”

“…When t = 1, x(t) = x(1), and c = x(1) − u a , according to Equation (17), it can obtain the discrete form differential equation, as shown in Equation (18).…”

“…The control methods of the inverted pendulum can be categorized into three types, namely linear control methods [8][9][10][11][12][13][14], prediction control and structure-changing control methods [13,15], and intelligence control [16][17][18][19].…”

“…Intelligence control: Intelligence control methods, including mainly nerve control, fuzzy control, and simulation intelligence control, have been applied to the inverted pendulum system [16][17][18][19]. Nerve control is characterized by self-adjustment, parallel processing, high tenacity, faster speed, and adjustment.…”

Open‐Closed‐Loop PD Iterative Learning Control with a Variable Forgetting Factor for a Two‐Wheeled Self‐Balancing Mobile Robot

“…Experiments showed that the combination of these two control algorithms could achieve a good control effect. Because of its small capacity, feasible rotation and strong ability to adapt to terrain changes, this combined algorithm is widely used in many fields [8][9][10], such as traffic, survey, rescue and entertainment. However, this method is cost prohibitive and time consuming because of its fuzzy rules.…”