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This paper presents an adaptive iterative learning control approach for the PH neutralization in Batch Processes. The proposed approach includes a feedback control law and a parameter iterative updating law together. The parameter updating law is designed by a projection algorithm to estimate the time-varying parametric uncertainties of the PH neutralization. Both the rigorous analysis and the simulation...
This paper presents a new data-driven optimal terminal iterative learning control (TILC) using time-varying control input signals to enhance control performance. The iterative learning control input is updated using the terminal output in previous runs, together with the control input information in previous runs and previous time instants of the current run, without the need of any reference trajectory...
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