Neural network balance control of hopping robots in flight phase under unknown dynamics

In this paper, a balance control scheme is introduced for hopping robots using neural networks. The control strategy uses a trade-off strategy to achieve both hip and body angle control simultaneously with a single controller, which yields reduced complexity. Hence, the proposed controller allows the hopping robot to track a pre-defined state trajectory in flight-phase despite its modeling uncertainties. The overall dynamics complexity is decreased so that a conventional PID controller can be used for leg extension's length tracking. Unlike other control techniques, no velocity sensor, gyroscope, camera, or body location estimation is needed. Furthermore, no offline learning or a priori system dynamics knowledge is required. Results for different situations highlight the performance of the proposed control approach in compensating for nonlinearities and disturbances.