Abstract
The electromechanical system of a crawler is a multi-input, multioutput strongly coupled nonlinear system. In this study, an adaptive inverse control method based on kriging algorithm and Lyapunov theory is proposed to improve control accuracy during adaptive driving. The electromechanical coupling model of the electromechanical system is established on the basis of the dynamic analysis of the crawler. In accordance with the kriging algorithm, the inverse model of the electromechanical system of the crawler is established by offline data. The adaptive travel control law of the crawler is obtained on the basis of Lyapunov theory. Combined with the kriging algorithm, the adaptive driving reverse control method is designed, and the online system is used to update and perfect the inverse system model in real time. Finally, the virtual prototype model of the crawler is established, and the control effect of the adaptive inverse control method is verified by theoretical analysis and virtual prototype simulation.
Highlights
As the most common construction machinery traveling device used in engineering applications, the crawler mechanism incurs increasing requirements on the driving force due to the trend of large-scale construction vehicles
An adaptive inverse control algorithm based on kriging algorithm is proposed in this study, and the control algorithm is applied to the adaptive control of the crawler
An inverse modeling system based on the kriging algorithm and an online adaptive inverse control system are established
Summary
As the most common construction machinery traveling device used in engineering applications, the crawler mechanism incurs increasing requirements on the driving force due to the trend of large-scale construction vehicles. With the development of intelligent control technology, intelligent modeling technologies have been applied extensively to the inverse system control for this complex MIMO system, which provides the possibility of precise control of the adaptive travel process of a crawler. Among these technologies, inverse system control is a control strategy based on feedback linearization method. An INN trained using the backpropagation learning algorithm from data sets of a fundamental model provided an artificial neural network application of the recognition and control of nonlinear systems [7]. The control method provides a theoretical basis for the practical application of the crawler in the unmanned driving situation
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