Journal
INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH
Volume 62, Issue 15, Pages 6176-6195Publisher
AMER CHEMICAL SOC
DOI: 10.1021/acs.iecr.2c04114
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This study proposes an alternative approach to solve the problem of implementing model predictive controllers in conditions where the timescale multiplicity of the process model is not considered. The proposed method, based on multiple timescale recurrent neural network (MTRNN)-based neural network predictive controllers, effectively handles setpoint tracking scenarios. The optimized MTRNN-based controller exhibits stable response, minimal error, and improved setpoint tracking ability compared to a nonlinear autoregressive exogeneous-based NNPC.
This study attempts to offer an alternative to the problem of implementing model predictive controllers (MPC) in conditions where the timescale multiplicity of the process model is not accounted for when incorporated into the MPC. Modeling methods that do not account for the timescale multiplicity in system's dynamics tend to become ill-conditioned and stiff when inversed in model-based controllers, thus requiring high computational loads to solve the equations. Therefore, this study proposes an alternative approach to the control of multi-timescale processes based on the use of multiple timescale recurrent neural network (MTRNN)-based neural network predictive controllers (NNPC). The effectiveness in handling setpoint tracking scenarios by the proposed method is evaluated using a benchmark nonexplicit twotimescale continuous stirred tank reactor (CSTR). After undergoing controller parameter optimization, the optimum configuration is found to be at 110, 37, and 0.2 for the cost horizon, control horizon, and control weighting factor, respectively. Results show that the MTRNN-based NNPC is able to track the reference trajectory with stable response and minimal error with a root mean square error of 0.0642. The optimized MTRNN-based controller is tested for its robustness under plant-model mismatch and is compared for its setpoint tracking abilities with a nonlinear autoregressive exogeneous (NARX)-based NNPC which showed that the proposed controller can satisfy the desired setpoint, resulting in an error that is 1.8 times lower than NARX-based NNPC.
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