The wastewater treatment is an effective method for alleviating the shortage of water resources. In this chapter, a data-driven iterative adaptive tracking controlTracking control approach is developed to improve the control performance of the dissolved oxygen concentrationDissolved oxygen concentration and the nitrate nitrogen concentrationNitrate nitrogen concentration in the nonlinear wastewater treatment plant. First, the model networkModel network is established to obtain the steady control and evaluate the new system state. Then, a nonquadratic performance functional is provided to handle asymmetric control constraints. Moreover, the new costate function and the tracking control policy are derived by using the dual heuristic dynamic programming algorithm. In the present control scheme, two neural networks are constructed to approximate the costate function and the tracking controlTracking control law. Finally, the feasibility of the proposed algorithm is confirmed by applying the designed strategy to the wastewater treatment plantWastewater treatment plant.


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    Title :

    Constrained Neural Optimal Tracking Control with Wastewater Treatment Applications


    Additional title:

    Intelligent Control & Learning Systems


    Contributors:
    Wang, Ding (author) / Ha, Mingming (author) / Zhao, Mingming (author)


    Publication date :

    2023-01-22


    Size :

    21 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




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