| Due to high nonlinearity and parameter uncertainties, the precious motion control of hydraulic systems is not easy。One of the difficulties is to build their exact mathematical model. This thesis focuses on the application of approximate internal model-based neural control (AIMNC) strategy in heavy-load large-inertia hydraulic systems for the grippers of 250 Ton forging manipulators.A heavy-load large-inertia hydraulic system is identified using nonlinear identification method based on neural networks and the neural network model of the system is thus built, which solves the problem of building an exact mathematical model of complex, uncertain and nonlinear hydraulic systems.In view of the high nonlinearity, model uncertainties and variation of operation points of the system, the application of a novel AIMNC strategy in heavy-load large-inertia hydraulic systems is studied. The neural inverse control law can be derived directly from the identified neural network model without further training and only one neural network needs to be trained. Simulation studies demonstrate that the AIMNC strategy exhibits better control performance and robustness to model uncertainties and variation of operation points than the PID controller.In view of the influence of internal dynamic friction on hydraulic systems, the friction compensation control of heavy-load large-inertia hydraulic systems based on LuGre model is studied. The parameters in the LuGre model are estimated through a velocity—torque experiment.As two hydraulic motors are needed to drive heavy-load large-inertia hydraulic systems and the synchronization error among hydraulic motors, a cross-coupled controller for synchronization error compensation based on feedback of velocity and pressure is studied for two hydraulic motor driving systems. Simulation studies demonstrate that the cross-coupled controller based on pressure feedback can achieve better velocity synchronization and load balance. |
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