Adaptive Critic Based Optimal Neurocontrol of a Distributed Microwave Heating System Using Diagonal Recurrent Network

Liu, Tong; Liang, Shan; Xiong, Qingyu; Wang, Kai

doi:10.1109/access.2018.2877206

Cited by 10 publications

(12 citation statements)

References 29 publications

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“…modeling technique offers a practical means of MHP identification [48]- [50]. Temperature is a crucial measurement during the operation of MHP, as thermal runaway often occurs due to the time-varying physicochemical properties of material [46].…”

Section: B Online Identification Of Microwave Heating System 1) Systmentioning

confidence: 99%

“…Therefore, accurate online temperature prediction is vital to detect thermal runaway in advance. 6 illustrates a real-world industrial microwave heating system [48], [51], which consists of five microwave generators and waveguides, temperature measurement sensors and the control system hosted in a programmable logic controller (PLC). Microwave generated by each microwave generator is transmitted through the corresponding waveguide, which is fed into the cavity and absorbed by the heated material.…”

Section: B Online Identification Of Microwave Heating System 1) Systmentioning

confidence: 99%

“…Each FOS measures the temperature y s j (t) at the FOS's location, where 1 ≤ j ≤ 3. Because of near instantaneous response of MHP, the temperature y s j (t) at the jth FOS's location can be adequately represented by [48], [51] y s j (t) = f nl−ns,j (x j (t); t),…”

Section: B Online Identification Of Microwave Heating System 1) Systmentioning

confidence: 99%

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Growing and Pruning Selective Ensemble Regression for Nonlinear and Nonstationary Systems

et al. 2020

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For a selective ensemble regression (SER) scheme to be effective in online modeling of fastarriving nonlinear and nonstationary data, it must not only be capable of maintaining a most up to date and diverse base model set but also be able to forget old knowledge no longer relevant. Based on these two important principles, in this paper, we propose a novel growing and pruning SER (GAP-SER) for timevarying nonlinear data. Specifically, during online operation, newly emerging process state is automatically identified and a local linear model is fitted to it. This adaptive growing strategy therefore maintains a most up to date and diverse local model set. The online prediction model is then constructed as a selective ensemble from the local linear model set based on a probability metric. Moreover, a pruning strategy is derived to remove 'unwanted' out of date local linear models in order to achieve low online computational complexity without sacrificing online modeling accuracy. A chaotic time series prediction and two real-world data sets are used to demonstrate the superior online modeling performance of the proposed GAP-SER over a range of benchmark schemes for nonlinear and nonstationary systems, in terms of online prediction accuracy and computational complexity. INDEX TERMS Nonlinear and nonstationary data, local linear model, growing model, pruning model, selective ensemble.

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Section: B Online Identification Of Microwave Heating System 1) Systmentioning

confidence: 99%

Section: B Online Identification Of Microwave Heating System 1) Systmentioning

confidence: 99%

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Growing and Pruning Selective Ensemble Regression for Nonlinear and Nonstationary Systems

et al. 2020

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“…A real-world distributed microwave heating system [19] is used in this case study, which consists of five microwave generators and waveguides. Microwave generated by each microwave generator is transmitted through the corresponding waveguide, fed into the cavity and absorbed by the heated material.…”

Section: A Process Descriptionmentioning

confidence: 99%

A Multiple Local Model Learning for Nonlinear and Time-Varying Microwave Heating Process

Liu

Liang

Chen

et al. 2019

2019 International Joint Conference on Neural Networks (IJCNN)

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This paper proposes a multiple local model learning approach for nonlinear and nonstationary microwave heating process (MHP). The proposed local learning framework performs model adaption at two levels: (1) adaptation of the local linear model set, which adaptively partitions the process's data into multiple process states, each fitted with a local linear model; (2) online adaptation of model prediction, which selects a subset of candidate local linear models and linearly combines them to produce the model prediction. Adaptive process state partition and fitting a new local linear model to the newly emerging process state is based on statistical hypothesis testing, and the optimal combining coefficients of the selected subset linear models are obtained by minimizing the mean square error with the constraint that the sum of these coefficients is unity. A case study involving a real-world industrial MHP is used to demonstrate the superior performance of the proposed multiple local model learning approach, in terms of online modeling accuracy and computational efficiency.

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“…Heretofore, the application of adaptive control has been extended from aviation and aerospace fields to industrial process, where also contains the microwave heating. Some relevant work for adaptive temperature control problem in microwave heating process is also reported, such as simple adaptive model-based controllers [7], adaptive neural fuzzy inference system [8], model reference adaptive control with expert control system [9], adaptive neural control [10], [11]. Thus, it is practicable to apply adaptive control in the microwave heating process.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Multi-Point Temperature Control for Microwave Heating Process via Multi-Rate Sampling

Liang

Liu

Song

et al. 2019

SICE Journal of Control, Measurement, and System Integration

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Microwave heating has been gradually extended to industrial material process from domestic microwave ovens because of its substantial advantages such as high-efficiency, pollution free, and selective heating. Unfortunately, the drawback of the temperature non-uniformity, which may cause thermal runaway, becomes an obstruction for the development of microwave energy. Besides, a common problem associated with microwave heating systems is that the speed of microwave power transmission is faster than the temperature detection period. Thus, to ensure the global temperature uniformity and to enhance the system adaptivity for deviation of the temperature detecting position in the microwave heating system with input constraints, a multi-rate simple adaptive multi-point temperature control strategy based on almost strictly positive real conditions is proposed, where the use of multi-rate sampling and lifting technique is to solve the case that the system has less inputs than outputs. Finally, simulation results demonstrate the effectiveness of the proposed control strategy.

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Adaptive Critic Based Optimal Neurocontrol of a Distributed Microwave Heating System Using Diagonal Recurrent Network

Cited by 10 publications

References 29 publications

Growing and Pruning Selective Ensemble Regression for Nonlinear and Nonstationary Systems

Growing and Pruning Selective Ensemble Regression for Nonlinear and Nonstationary Systems

A Multiple Local Model Learning for Nonlinear and Time-Varying Microwave Heating Process

Adaptive Multi-Point Temperature Control for Microwave Heating Process via Multi-Rate Sampling

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