By Dianwei Qian, Jianqiang Yi
This e-book reviews at the most modern advancements in sliding mode overhead crane keep watch over, providing novel study rules and findings on sliding mode keep an eye on (SMC), hierarchical SMC and compensator design-based hierarchical sliding mode. the implications, that have been formerly scattered throughout quite a few journals and convention lawsuits, at the moment are awarded in a scientific and unified shape. The publication could be of curiosity to researchers, engineers and graduate scholars up to the mark engineering and mechanical engineering who are looking to study the equipment and functions of SMC.
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Additional resources for Hierarchical Sliding Mode Control for Under-actuated Cranes: Design, Analysis and Simulation
Example text
2]); subplot(1,2,1),plot(t,simout(:,1));xlabel('t');ylabel('x'); H Simulink Model to Plot Figs. 16 39 40 1 Plant program: plant. ');hold on; figure(2)%% control signal subplot(1,3,1),plot(t,simout(:,1)) xlabel('t');ylabel('x'); subplot(1,3,2),plot(t,simout(:,4)) xlabel('t');ylabel('s'); subplot(1,3,3),plot(t,simout(:,3),'k'); xlabel('t');ylabel('u'); Introduction Appendices I Simulink Model to Plot Figs. 20 Plant program: plant. ');hold on; figure(2)%% control signal subplot(1,3,1),plot(t,simout(:,1)) xlabel('t');ylabel('x'); subplot(1,3,2),plot(t,simout(:,4)) xlabel('t');ylabel('s') subplot(1,3,3),plot(t,simout(:,3),'k'); xlabel('t');ylabel('u'); References 1.
This section provides a view to highlight some of these popular control methods. For the purpose of illustration, the survey of closed-loop control for cranes is demonstrated in Fig. 22. 26 1 Introduction Fig. 1 Linear Control Linear control calls for linearized crane model. Based on the linearized crane model, Hazlerigg [38] was one of the first to propose this method in 1972. Since then, a variety of linear control methods has been applied to crane control practice. , linear quadratic regulator (LQR) control by Grassin et al.
The framework can assist crane operators to replan safe paths in near real time. Fang and his colleagues [21] investigated a kinematic coupling-based offline trajectory planning method for overhead cranes, where the trajectory was tuned by an iterative learning strategy to guarantee accurate trolley positioning. Further, they [22] considered an offline trolley trajectory planning method for underactuated overhead cranes, where some rigorous geometric analysis were utilized to address the coupling behavior between the actuated trolley motion and the underactuated payload swing.