A bionic operant conditioning reflex(OCR) learning control scheme is proposed based on the thought of sliding
model control(SMC) and Elman network for a class of SISO higher-order nonlinear control system. In this method, an Elman
network is used as an evaluation function of sliding surface and action in the scheme. Reward signal is designed according to
the change of sliding surface, and then the evaluation function is updated through the reward stimulation, while the behavior
choice probability is changed. Through the definition of entropy for each round, a quantitative analysis about the knowledge
change in the learning process is given. The results of the simulation experiment in the walking inverted pendulum system
show that, bionic OCR learning control is used, which realizes the balancing control for the walking inverted pendulum
system.