Control of a rope-driven self-leveling device for leveling adjustment

To solve the level-adjusting problem of high accurate and costly payloads when loading and unloading, a rope-driven self-leveling device is developed, and a neuro-fuzzy controller is proposed. After a brief introduction of the configuration characteristics of the device and the fundamentals of neuro-fuzzy control, the construction of the neuro-fuzzy controller is set up, in which the angles of two diagonal inclinations which are measured from the two angle sensors are chosen as input variables, and the changes of two linear motion units´ positions are the control variables. The neuro-fuzzy controller, whose rules are constructed based on human´s regulating experience, was tuned by a hybrid algorithm, which is a combination of the least square estimate (LSE) method and the back-propagation (BP) algorithm. Experimental results show that the proposed neuro-fuzzy controller can achieve the control objective with high accuracy of regulation and short adjusting time, and is easily applied to the practical device.