Fixed-time prescribed performance optimization control for the speed and tension system of the cold strip rolling mill with output constraints

For the speed and tension system of the reversible cold strip rolling mill with output constraints, parameter perturbations and load disturbance, a fixed-time prescribed performance optimization control method is proposed based on disturbance observers in this article. First, the disturbance observers are constructed to estimate the system's unmatched uncertainties, and the observer errors can converge in fixed time. Second, a time-varying logarithmic barrier Lyapunov function (TLBLF) is given and combined with the command filtered backstepping approach, the fixed-time control and the prescribed performance control to complete the controller designs for the speed and tension system of the cold strip rolling mill, which make the system states converge in fixed time and are always constrained within predefined ranges. Third, particle swarm optimization-gray wolf optimization (PSO_GWO) hybrid intelligent algorithm is used to optimize the main control parameters of the designed controllers, which further improves the convergence speed and steady-state accuracy of the rolling mill system. Theoretical analysis proves that the proposed control method can ensure the closed-loop system is stable in fixed time. Finally, the simulation comparison study is carried out by using the field actual data of a reversible cold strip mill system, and the simulation results verify the effectiveness of the proposed control method.