Controller parameter optimization for complex industrial system with uncertainties

Chen, Heping; Bowels, Seth; Zhang, Biao; Fuhlbrigge, Thomas

doi:10.1177/0020294019830108

Cited by 12 publications

(7 citation statements)

References 10 publications

(16 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As described above, the output y* contains pole information because y* is calculated usingt, as shown in equation (17). The sum of squares of the error e* between output y* and the reference model output is an objective function that contains pole information of the closed-loop system.…”

Section: Derivation Of the Frit Cost Function Considering Destabilization Detectionmentioning

confidence: 99%

“…Poles are not directly calculated in this algorithm. Instead, we use the output y* calculated using equation (17) because the complementary sensitivity functiont contains pole information. Therefore, the convergence of the control parameters that cause the closed-loop system to become unstable can be avoided when the objective function involving y* is minimized.…”

Section: Step 1-2 Computing Complementary Sensitivity Functions In the Time Domainmentioning

confidence: 99%

“…Therefore, studies on model-free control and data-driven control (DDC), wherein controller parameters are directly tuned using the input/output data of a controlled object, are attracting attention. 2–21 Model-free control is effective when applied to time-varying systems because optimal parameters can be obtained via online calculations. However, some difficulties are associated with its application to mass products from the viewpoint of stability and CPU performance.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Direct tuning of the data-driven controller considering closed-loop stability based on a fictitious reference signal

Yahagi

Kajiwara

2021

Measurement and Control

View full text Add to dashboard Cite

The direct tuning of controller parameters, which is based on data-driven control, has been attracting considerable attention because of the ease of its control system design. In practical use, it is important to consider the stability of the closed-loop system and model matching with few design parameters. In this study, we propose a direct tuning method based on a fictitious reference signal that considers the bounded-input bounded-output (BIBO) and model matching without repeating experiments. The proposed method includes two steps. In the first step, the BIBO stability is satisfied. The pole information is lost in the cost function of the conventional method using a fictitious reference signal. Then, we derive a new cost function that can prevent the loss of the pole information. This provides controller parameters that can stabilize the closed-loop system. The model matching between the reference model and the closed-loop system is considered in the second step. When model matching is achieved, the characteristics of the reference model almost match those of the closed-loop system, including the gain and phase margins. The parameters of the reference model are automatically tuned to realize model matching. Using the two-step method, we can obtain parameters considering BIBO stability and the model matching. In addition, there are no design parameters apart from the dealing noise. Two simulations and an experiment were performed on a system with dead time to verify the effectiveness of the proposed two-step method. The results showed that the proposed method provides BIBO stability and model-matched control parameters from the measured data through a one-time experiment without trial and error.

show abstract

Section: Derivation Of the Frit Cost Function Considering Destabilization Detectionmentioning

confidence: 99%

Section: Step 1-2 Computing Complementary Sensitivity Functions In the Time Domainmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Direct tuning of the data-driven controller considering closed-loop stability based on a fictitious reference signal

Yahagi

Kajiwara

2021

Measurement and Control

View full text Add to dashboard Cite

show abstract

“…Here, we use Bayesian optimization that can estimate regions with a small number of samples or extrapolation regions and can estimate from a small number of samples. Bayesian optimization has been used to search for optimal experimental conditions [20,21,22,23] and process conditions, [24,25,26] and to optimize hyperparameters. [27] Here, we apply Bayesian optimization to process design.…”

Section: Adaptive Doementioning

confidence: 99%

Design of ethylene oxide production process based on adaptive design of experiments and Bayesian optimization

Iwama

Kaneko

2021

J Adv Manuf & Process

View full text Add to dashboard Cite

In process design, the values of design variables X for equipment and operating conditions should be appropriately selected for entire processes, including all unit operations, such as reactors and distillation columns, to consider effects between unit operations. However, as the number of X increases, many more simulations are required to search for the optimal X values. Furthermore, multiple objective variables Y, such as yields, make the optimization problem difficult. We propose a process design method based on adaptive design of experiments and Bayesian optimization. Selection of X values that satisfy the target values of multiple Y variables are searched, and simulations for the selected X values are then repeated. Therefore, the X will be selected by a small number of simulations. We verify the effectiveness of this method by simulating an ethylene oxide production plant.

show abstract

“…Here, we use Bayesian optimization that can estimate regions with a small number of samples or extrapolation regions and can estimate from a small number of samples. Bayesian optimization has been used to search for optimal experimental conditions 16,17,18,19 and process conditions 20,21,22 , and to optimize hyperparameters 23 . Here, we apply Bayesian optimization to process design.…”

Section: Adaptive Doementioning

confidence: 99%

Design of ethylene oxide production process based on adaptive design of experiments and Bayesian optimization

Iwama

Kaneko

2020

Preprint

View full text Add to dashboard Cite

In process design, the values of design variables X for equipment and operating conditions should be optimized for entire processes, including all unit operations, such as reactors and distillation columns, to consider effects between unit operations. However, as the number of X increases, many more simulations are required to search for the optimal X values. Furthermore, multiple objective variables Y, such as yields, make the optimization problem difficult. We propose a process design method based on adaptive design of experiments and Bayesian optimization. Optimization of X values that satisfy target values of multiple Y variables are searched, and simulations for the optimized X values are then repeated. Therefore, X will be optimized by a small number of simulations. We verify the effectiveness of this method by simulating an ethylene oxide production plant.

show abstract

Controller parameter optimization for complex industrial system with uncertainties

Cited by 12 publications

References 10 publications

Direct tuning of the data-driven controller considering closed-loop stability based on a fictitious reference signal

Direct tuning of the data-driven controller considering closed-loop stability based on a fictitious reference signal

Design of ethylene oxide production process based on adaptive design of experiments and Bayesian optimization

Design of ethylene oxide production process based on adaptive design of experiments and Bayesian optimization

Contact Info

Product

Resources

About