Robust decentralized eigenvalues clustering for multi-time-scale systems with perturbations

Horng, Ing-Rong; Chang, Cheng-Kuo; Horng, Huey-Yang

doi:10.1080/00207720110065540

Cited by 1 publication

(2 citation statements)

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“…The typical development of the control strategy for these processes occurs mostly through singular perturbation techniques 6 and graph theory, 7 which subsequently implements the hierarchical control structure 8 that takes advantage of the identified timescale multiplicity. These strategies, system studied, and control techniques that are reported in recent times are such as those reported previously.…”

Section: Introductionmentioning

confidence: 99%

“…The typical development of the control strategy for these processes occurs mostly through singular perturbation techniques and graph theory, which subsequently implements the hierarchical control structure that takes advantage of the identified timescale multiplicity. These strategies, system studied, and control techniques that are reported in recent times are such as those reported previously. , A study of CSTR-distillation column network shows that the control strategy for the overall system can be designed through the implementation of separate distributed and supervisory controllers.…”

Section: Introductionmentioning

confidence: 99%

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Control of the Multi-Timescale Process Using Multiple Timescale Recurrent Neural Network-Based Model Predictive Control

Jian

Zabiri

Ramasamy

2023

Ind. Eng. Chem. Res.

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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 two-timescale 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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Section: Introductionmentioning

confidence: 99%