Vision-based optimization of the generalized predictive active disturbance rejection controller

Abstract

The batching system of the integrated mixing and spreading equipment for MOH material is a nonlinear system with large uncertainty. It is difficult for conventional control strategies to meet the requirements for system performance. This research combines generalized predictive control and active disturbance rejection technique to propose a new generalized predictive active disturbance rejection controller (GPADRC) used in the batching system of MOH material. For the nonlinearity and uncertainty of the batching system, the extended state observer in the active disturbance rejection technique is used for estimation and compensation. The batching system model is converted into an integrator form, based on which the use of generalized predictive control can greatly reduce the impact of nonlinear models and uncertainties on the controller. Aiming at the problem that the parameters of the proposed new controller are numerous and difficult to tune, the adaptive genetic algorithm is used to realize the automatic tuning of the parameters. The simulation experiment shows that the designed GPADRC can well adapt to the working conditions of the batching system and can meet the requirements for various control indicators. At the same time, the adaptive genetic algorithm can realize the rapid tuning of the controller parameters, which reduce the difficulty and time consumption of the tuning process, and improve the applicability and achievability of the designed controller.