Abstract
This paper presents a supervised Hebb learning single neuron adaptive proportional-integral-derivative (PID) controller for the power control of a cold milling machine. The proposed controller aims to overcome the deficiency of the current power control algorithm, and to achieve as high an output power as possible for the cold milling machine. The control process and system model are established and presented to provide the insight and guidance to the controller design and analysis. The adaptive PID controller is developed using a supervised Hebb learning single neuron method with detailed algorithm and structure analysis. The field test is performed to validate the proposed single neuron adaptive PID control for the power control. In the test, the 8 cm-depth milling is conducted on a cement concrete pavement in which the cement is not well-distributed. The test results show that when the machine speed is adjusted by the machine itself or manually without the adaptive power control system, the machine is often overloaded or underloaded, and the average work speed is 2.4m/min. However, when the adaptive control system is implemented on the machine, it works very close to its rated work condition during its work process. With the developed controller, the machine work speed is adjusted in time to the load variation and uncertain dynamics. The average machine work speed can reach up to 2.766 m/min, which is 15.25% higher than the wok speed of the machine without an adaptive power control system.
Highlights
A cold milling machine is an off-highway vehicle which is widely used for the controlled surface removal of existing pavement to the desired depth in highway maintenance and reconstruction [1].The amount of removal material can be controlled to meet project specific requirements.The resulting textured pavement can be used immediately as a driving surface.The structure and the working process of a cold milling machine are illustrated in Figures 1 and 2.The machine is equipped with a cutting drum to mill the pavement surface to the specified grade and cross-slope
When the machine works on the downhill pavement, the work speed of the milling machine will increase under the gravity along the ramp as the tractive force
The control algorithm and method has been validated by a field test
Summary
A cold milling machine is an off-highway vehicle which is widely used for the controlled surface removal of existing pavement to the desired depth in highway maintenance and reconstruction [1]. A traditional PID controller with the fixed-control gains cannot achieve the optimal control performance and is not adaptive to environment variations for the cold milling machine power control featured with a nonlinear and uncertain system [10,11]. The neural network control has strong self-study ability to control a nonlinear system, but it needs lots of computation It is not feasible as the cold milling machine is equipped with a 16 bit single chip microcomputer, which has a very limited computation capability. The strong ability of self-studying and self-adapting of the single neuron PID control can improve the performance and robustness of the adaptive power control.
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