D-FNN Based Modeling and BP Neural Network Decoupling Control of PVC Stripping Process

Gao, Shuzhi; Yang, Jing; Wang, Jiesheng

doi:10.1155/2014/681259

Cited by 11 publications

(6 citation statements)

References 19 publications

(23 reference statements)

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“…. Recently, the FLC has emerged as an effective tool for stabilizing a nonlinear system, such as an AMB system, a magnetic levitation system, or other electronic devices [24][25][26][27][28][29][30]. The FLC is a methodical approach for controlling a nonlinear system and is a heuristic technique for enhancing the operation of a closed loop system.…”

Section: Fuzzy Logic Controller (Flc)mentioning

confidence: 99%

Nonlinear Control of an Active Magnetic Bearing System Achieved Using a Fuzzy Control with Radial Basis Function Neural Network

Chen

Nguyen

et al. 2014

Journal of Applied Mathematics

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Studies on active magnetic bearing (AMB) systems are increasing in popularity and practical applications. Magnetic bearings cause less noise, friction, and vibration than the conventional mechanical bearings; however, the control of AMB systems requires further investigation. The magnetic force has a highly nonlinear relation to the control current and the air gap. This paper proposes an intelligent control method for positioning an AMB system that uses a neural fuzzy controller (NFC). The mathematical model of an AMB system comprises identification followed by collection of information from this system. A fuzzy logic controller (FLC), the parameters of which are adjusted using a radial basis function neural network (RBFNN), is applied to the unbalanced vibration in an AMB system. The AMB system exhibited a satisfactory control performance, with low overshoot, and produced improved transient and steady-state responses under various operating conditions. The NFC has been verified on a prototype AMB system. The proposed controller can be feasibly applied to AMB systems exposed to various external disturbances; demonstrating the effectiveness of the NFC with self-learning and self-improving capacities is proven.

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Section: Fuzzy Logic Controller (Flc)mentioning

confidence: 99%

Nonlinear Control of an Active Magnetic Bearing System Achieved Using a Fuzzy Control with Radial Basis Function Neural Network

Chen

Nguyen

et al. 2014

Journal of Applied Mathematics

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“…In [51], an adaptive NN-based PIDC is offered for the multi-input multi-output nonlinear vehicle system. Different combinations of NNC and FLC are proposed in [52] to control the EVs in blended braking. A neuro-fuzzy controller applied in [53] solves the torque distribution problem for regenerative braking of a hybrid bus.…”

Section: Introductionmentioning

confidence: 99%

Neural Network-Based Model Reference Control of Braking Electric Vehicles

et al. 2021

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The problem of energy recovery in braking of an electric vehicle is solved here, which ensures high quality blended deceleration using electrical and friction brakes. A model reference controller is offered, capable to meet the conflicting requirements of intensive and gradual braking scenarios at changing road surfaces. In this study, the neural network controller provides torque gradient control without a tire model, resulting in the return of maximal energy to the hybrid energy storage during braking. The torque allocation algorithm determines how to share the driver’s request between the friction and electrical brakes in such a way as to enable regeneration for all braking modes, except when the battery state of charge and voltage levels are saturated, and a solo friction brake has to be used. The simulation demonstrates the effectiveness of the proposed coupled two-layer neural network capable of capturing various dynamic behaviors that could not be included in the simplified physics-based model. A comparison of the simulation and experimental results demonstrates that the velocity, slip, and torque responses confirm the proper car performance, while the system successfully copes with the strong nonlinearity and instability of the vehicle dynamics.

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“…Due to technological constraints, sensor characteristics, environmental factors, etc., many variables cannot be measured or the measurement frequency is very low in actual industrial processes. Soft measurement provides an excellent solution to construct mathematical models from easily measured variables to hard ones [1][2][3]. Neural networks (NNs) are advanced methods that can precisely model complex and nonlinear system and therefore have been widely used in soft sensors [4][5][6].…”

Section: Introductionmentioning

confidence: 99%

Development of a Novel Soft Sensor with Long Short-Term Memory Network and Normalized Mutual Information Feature Selection

Li¹,

Sun

2020

Mathematical Problems in Engineering

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In this paper, a novel soft sensor is developed by combining long short-term memory (LSTM) network with normalized mutual information feature selection (NMIFS). In the proposed algorithm, LSTM is designed to handle time series with high nonlinearity and dynamics of industrial processes. NMIFS is conducted to perform the input variable selection for LSTM to simplify the excessive complexity of the model. The developed soft sensor combines the excellent dynamic modelling of LSTM and precise variable selection of NMIFS. Simulations on two actual production datasets are used to demonstrate the performance of the proposed algorithm. The developed soft sensor could precisely predict the objective variables and has better performance than other methods.

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D-FNN Based Modeling and BP Neural Network Decoupling Control of PVC Stripping Process

Cited by 11 publications

References 19 publications

Nonlinear Control of an Active Magnetic Bearing System Achieved Using a Fuzzy Control with Radial Basis Function Neural Network

Nonlinear Control of an Active Magnetic Bearing System Achieved Using a Fuzzy Control with Radial Basis Function Neural Network

Neural Network-Based Model Reference Control of Braking Electric Vehicles

Development of a Novel Soft Sensor with Long Short-Term Memory Network and Normalized Mutual Information Feature Selection

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