Adaptive sliding mode observers-based dissipative Hamilton finite time optimization control for the speed and tension system of reversible cold strip rolling mill

For the complex time-varying uncertain nonlinear speed and tension system of reversible cold strip rolling mill, an adaptive sliding mode observers (ASMOs)-based dissipative Hamilton finite time optimization control method is given in this article. First, the ASMOs are developed to observe rolling mill system's unmatched uncertainties, and their observation errors can converge to the origin in finite time. Second, combined with rolling mill system's physical structure characteristics, its dissipative Hamilton model is established by pre-feedback processing, and then the dissipative Hamilton finite time controllers are designed based on interconnection and damping configuration and energy shaping method, and the system states can converge in finite time. Third, the adaptive mutation particle swarm optimization algorithm is adopted to optimize the controller parameters, which can further improve rolling mill system's control performance. Finally, the simulation results verify the validity of the given method based on actual field data.