Zhou Entao

Predictive control of hydraulic winch motion control

Hydraulic winch is widely used in field of engineering mechanism. Motion control is an important application for hydraulic winch. The hydraulic winch is a mechanism with heavy load and big inertia. The response speed of hydraulic winch deeply affects the controlling precision when the hydraulic winch is in motion control to track a definite motion. Feedforward control of hydraulic winch can improve the control precision of hydraulic winch motion control. But the non-linear character of hydraulic winch decreases the control precision of motion control. The predictive control is designed based on parameters identification of hydraulic winch. The real time least squares parameters identification of hydraulic winch is used to gain the model parameters of hydraulic winch by using the control voltage of electronic hydraulic proportional valve and the speed of hydraulic winch. The conclusion of simulation test shows that the hydraulic winch motion predictive control which base on the real time parameters identification improves the control precision of motion controlling.

Magnetosensitive Displacement Sensor - A Non-Contact Type Sensor

A sensor is proposed for measuring the displacement of a moving part in a hydraulic system. Magnetosensitive resistance is used as the sensing element; the displacement of the measured part is reflected by the variation of the magnetic field and sensed by a magnetosensitive resistance. The magnetosensitive element is a thin film resistance with strong magnetism. It is very sensitive not only to the variation of magnetic field intensity but also to the change of direction of magnetic field. It has a high sensitivity and a good temperature characteristic. The relationship between output voltage and temperature is linear making compensation easy.

The self-learning CMAC control of the oil temperature in hydraulic system

In view of the large time delay of the temperature control system of hydraulic system, a self-learning cerebellar model articulation controller (CMAC) algorithm is proposed to control the cooling system. The temperature control precision exceeds the Grade A of ISO standard (50/spl plusmn/1/spl deg/C) and reaches the level of 50/spl plusmn/0.5/spl deg/C. The cooling system is composed of digital and proportional controllers and in hybrid form. This can increase the control precision. Considering the speciality of the cooling system, an inverse dynamics model different from the one commonly used is established. The computing speed of the self-learning CMAC is fast and thus can be used online. Different learning rules are used for different error bands to meet the requirement on speed of response and steady state precision. The experimental result shows that the proposed self-learning CMAC control can be effectively used in the temperature control of hydraulic systems.