Neural Network-Based Dual-Cylinder Synchronous Control of a Multi-Link Erection Mechanism

A dual-cylinder erection mechanism, in which two telescopic cylinders physically connect to a load, is a nonlinear system with model uncertainties and coupled dynamics. In this paper, a novel synchronous control algorithm with thrust-allocation law is proposed for eliminating the excessive internal forces caused by the unbalanced rotation and lateral moments during the erection process. With regulated internal forces, the “pull and drag” issue is attenuated and better synchronization performance is attained. For improved tracking accuracy, the inter-stage collision dynamics of the telescopic cylinder are considered for model compensation to enhance stage-changing and in-position performance. A radial basis function (RBF) neural network is utilized to estimate the model uncertainties and external disturbances, which alleviates reliance upon the accuracy of a system model for controller implementation. As a result, theoretical analysis revealed that the semi-global asymptotic stability and synchronized motion performance with decreased internal forces can be achieved via the presented synchronous controller with thrust-allocation strategy. Contrasting simulations were implemented on a multi-link erection mechanism and the results confirmed the superiority and effectiveness of the proposed synchronous control algorithm.