Signal-Compensated PI Control Based on Concurrent-Learning-Driven Optimal Tuning Parameters for Complex Ore Feeding Processes

Le résumé fourni par la source

Due to the influence of fluctuations in the particle size and throughput of ore from the upstream crushing stage, variations in the return flow of unqualified particles during grinding, and the frequent changes of unknown nonlinearities in the complex ore feeding process, it is difficult to apply existing disturbance-rejection control methods to control the feed rate within the target range. In this article, by utilizing the characteristic that the feed rate fluctuates around the target interval, a dynamic model of the feeding process is established. Based on the obtainable previous-time-step value of the unknown nonlinear dynamic system and the closed-loop tracking error generated by the unknown rate of change of this nonlinear system, and the PI closed-loop equation, a data-driven signal-compensator is designed by combining feedforward control with a one-step optimal control law. A signal-compensated PI controller is proposed. The performance index that minimizes the tracking error at each operating instant is introduced, and a concurrent-learning-driven optimal tuning method for parameters of both the PI controller and the compensator is proposed. Simulation and industrial application experiments demonstrate that the proposed control method is capable of controlling the tracking error within the target interval during the entire operational period.

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