A new cerebellar learning scheme of feedback error based on Kalman estimator

The cerebellum has long been thought to play a crucial role in the forming of graceful movements, and it is viewed as a set of modules, each of which can be added to a control system to improve smooth coordinated movement, with improvements continuing and improving over time. The present paper proposes a new feedback error learning scheme for tracing in motor control system. In the scheme, the model of cerebellar cortex is regarded as the feedforward controller. Specifically, a neural network and an estimator are adopted in the cerebellar cortex model which can predict the future state and eliminate faults caused by time delay. Then the new scheme was used to control inverted pendulum. The simulation experimental results show that the new scheme can learn to control the inverted pendulum for tracing successfully.