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This work studies the effect of different models on the performance of multistep model predictive control (MMPC) via simulation examples and bench-and pilot-scale experiments. The models used in the study are two common types of artificial neural networks (ANNs), namely, feedforward networks (FFNs) and external recurrent networks (ERNs). The steady-state offset of MMPC using FFN models is observed throughout simulation cases and experiments in case that prediction horizon is longer than the control horizon. This study further explains the FFNinduced offset phenomena mathematically. In the experimental part of this work, we compare the performances of MMPC using these two ANN models, conventional proportional-integral controllers and linear model predictive control in the dual-temperature control problems, which include a bench-scale ethanol and water distillation column and a pilot-scale i-butane and n-butane distillation column.

Obtaining all feasible parameters of the proportional-integral-differential (PID) controller is the key goal in uncertain systems. This paper proposes a graphical tuning method based on an internal model control (IMC) strategy for uncertain systems with time delay. Specifically, the Kharitonov theorem is introduced first to simplify the uncertain system into 32 polynomials. Then, for each polynomial, the IMC structure is applied to reduce the tuning parameters of the PID controller in order to rapidly determine the controller parameters. Finally, the maximum sensitivity (Ms) is used to further guarantee the controlled system with a certain robustness and dynamic performance, which can portray constant gain margin and phase margin boundaries, and can even determine the range of parameters of the proposed IMC filter. Three example results from simulations are presented to demonstrate the effectiveness and applicability of the proposed method.

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