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.
Based on the work process and the structure of a cold milling machine, the mathematical models of the engine, the pump system and the work equipment are derived; the models of the drive system and the fixed displacement motor are also constructed. A model of the whole system is established. The laws between the load and the work speed at a milling depth of 8 cm are investigated through simulation and experiment. Whether the utilization of the engine power is reasonable or not is verified through the simulation. The results show that it is reasonable and effective to use the work speed of the cold milling machine as the main control parameter, and the engine can be set to work at its rated power by self-adjustment of the adaptive power control system. In this case, the rated power utilization ratio of the engine is improved by more than 8% and the machine efficiency is raised at least 10%. Furthermore, the error between the machine work speed in simulation and the experimental value is a maximum of 8.1%. The machine performance can be expressed appropriately by the model. This model can provide a theoretical reference for the machine control system and a new way to study the milling machine system.
There are three motion stages for an industrial robot manipulator, including the acceleration stage, the constant velocity stage, and the deceleration stage. Aiming at reducing the residual vibration of the manipulator after the movement of the deceleration, a new method is proposed by configuring the movement parameters of the flexible manipulator. Firstly, we conduct experiments to verify a numerical vibration model of the manipulator, and then, we analyze the vibration suppression effect under different conditions based on the numerical model. The results show that in the range of one movement, the residual vibration can be well suppressed when the acceleration and deceleration time are set as a positive integer to the natural period of the manipulator operation; otherwise, the vibration suppression effect is not obvious and proportional to the difference between the acceleration/deceleration time and the manipulator natural period.
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