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• W2091647029 abstract "Since most chemical processes exhibit severe nonlinear and time-varying behavior, the control of such processes is challenging. In this paper, a novel two-layer online adjust algorithm is presented for chemical processes. The lower layer consists of a conventional proportional−integral−derivative (PID) controller and a plant process, while the upper layer is composed of identification and tuning modules. Using a lazy learning algorithm, a local valid linear model denoting the current state of system is automatically exacted for adjusting the PID controller parameters based on input/output data. This scheme can adjust the PID parameters in an online manner even if the system has nonlinear properties. In this online tuning strategy, the concepts of generalized minimum variance (GMV) and quadratic program with constraints are also considered. The capabilities of the proposed tuning strategy are investigated through a CSTR process available in an advanced algorithm simulation platform." @default.
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• W2091647029 date "2006-12-09" @default.
• W2091647029 modified "2023-10-09" @default.
• W2091647029 title "Lazy Learning-Based Online Identification and Adaptive PID Control: A Case Study for CSTR Process" @default.
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• W2091647029 doi "https://doi.org/10.1021/ie0608713" @default.
• W2091647029 hasPublicationYear "2006" @default.
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