Abstract
Agitator tanks are widely used in industrial fields. Improvement in their efficiency is critical to achieving high productivity. That is to say, an agitator tank system should have a short response time to produce a desired reagent with an accurate solution concentration and a moderate liquid level. Therefore, a noise-rejection zeroing dynamics (NRZD) model for the control of the agitator tank based on a neural-dynamics method with anti-noise performance is proposed in this paper. The solution concentration and the liquid level of the agitator tank synthesized by the NRZD model are able to converge to the desired trajectories polluted with different noises. Then, theoretical analyses on the convergence and anti-noise performance of the agitator tank system equipped with the NRZD model are presented. Furthermore, to verify the superiority of the agitator tank system equipped with the NRZD model, we perform tracking trajectories simulations on solution concentration and the liquid level of the agitator tank with different noises. Moreover, the simulation results verify that the NRZD model is more effective than the existing models in the reagent preparation process.
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