System identification for a MIMO process

Valarmathi, R.; Guruprasath,

doi:10.1109/iccpeic.2017.8290408

Cited by 4 publications

(3 citation statements)

References 3 publications

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“…Therefore, the weight w ij is updated by adopting the gradient descent, expressed as Equation (11). The correction ∆W ij ensures w ij changes in a way that always decreases the error, given in Equation (12), where α represents the learning rate of the back propagation:…”

Section: Multi-layer Nn Controllermentioning

confidence: 99%

“…However, the modeling error may decrease system performance as decoupling is guaranteed under the condition that the precise mathematical model is obtained. The quality of the obtained model depends on many on-site factors, such as the excitation condition and selected identification algorithm [9][10][11]. In addition, the decoupling control is not enough to reach uniform temperature control in transient state without adjusting the PID parameters in different channels [12].…”

Section: Introductionmentioning

confidence: 99%

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A Reference-Model-Based Neural Network Control Method for Multi-Input Multi-Output Temperature Control System

Liu

Hashimoto

et al. 2020

Processes

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Neural networks (NNs), which have excellent ability of self-learning and parameter adjusting, has been widely applied to solve highly nonlinear control problems in industrial processes. This paper presents a reference-model-based neural network control method for multi-input multi-output (MIMO) temperature system. In order to improve the learning efficiency of the NN control, a reference model is introduced to provide the teaching signal for the NN controller. The control inputs for the MIMO system are given by the sum of the output of the conventional integral-proportional-derivative (I-PD) controller and the outputs of the neural network controller.The proposed NN control method can not only improve the transient response of the system, but can also realize temperature uniformity in MIMO temperature systems. To verify the proposed method, simulations are carried out in MATLAB/SIMULINK environment and experiments are carried out on the DSP (Digital Signal Processor)-based experimental platform, respectively. Both results are quantitatively compared to those obtained from the conventional I-PD control systems. The effectiveness of the proposed method has been successfully verified.

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Section: Multi-layer Nn Controllermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Reference-Model-Based Neural Network Control Method for Multi-Input Multi-Output Temperature Control System

Liu

Hashimoto

et al. 2020

Processes

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show abstract

“…In the field of nonlinear systems, the Hammerstein model is the most common block-oriented model. This model can well characterize the process properties and represent a large class of nonlinear processes, for instance, neutralization process [1], heat exchanger [2], power system [3], distillation towers [4,5], drying processes [6], and continuous stirred tank reactors [7,8].…”

Section: Introductionmentioning

confidence: 99%

Parameter learning for the nonlinear system described by Hammerstein model with output disturbance

Zhu

et al. 2022

Asian Journal of Control

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A novel parameter learning scheme using multi-signal processing is developed that aims at estimating parameters of the Hammerstein nonlinear model with output disturbance in this paper. The Hammerstein nonlinear model consists of a static nonlinear block and a dynamic linear block, and the multi-signals are devised to estimate separately the nonlinear block parameters and the linear block parameters; the parameter estimation procedure is greatly simplified. Firstly, in view of the input-output data of separable signals, the linear block parameters are computed through correlation analysis method, thereby the influence of output noise is effectively handled. In addition, model error probability density function technology is employed to estimate the nonlinear block parameters with the help of measurable input-output data of random signals, which not only controls the space state distribution of model error but also makes error distribution tends to normal distribution. The simulation results demonstrate that the developed approach obtains high learning accuracy and small modeling error, which verifies the effectiveness of the developed approach.

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Parameter Learning for the Nonlinear System Described by a Class of Hammerstein Models

Zhu

Cao

2022

Circuits Syst Signal Process

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System identification for a MIMO process

Cited by 4 publications

References 3 publications

A Reference-Model-Based Neural Network Control Method for Multi-Input Multi-Output Temperature Control System

A Reference-Model-Based Neural Network Control Method for Multi-Input Multi-Output Temperature Control System

Parameter learning for the nonlinear system described by Hammerstein model with output disturbance

Parameter Learning for the Nonlinear System Described by a Class of Hammerstein Models

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