Multierror stochastic gradient algorithm for identification of a Hammerstein system with random noise and its application in the modeling of a continuous stirring tank reactor

Jing, Shaoxue

doi:10.1002/oca.2760

Cited by 8 publications

(9 citation statements)

References 53 publications

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“…29,30 There are many gradient-based algorithms, such as information gradient algorithms and accelerated gradient algorithms. [31][32][33][34] Among these algorithms, a key term separation gradient iterative algorithm was derived to identify a fractional-order nonlinear system, 30 a recursive gradient algorithm was proposed to estimate the nonlinear parameters using multifrequency sine signals, 35 a maximum likelihood gradient iterative algorithm was developed for identifying the parameters of bilinear systems, 36 a gradient-based recursive least squares estimator was applied in the model-free extremum seeking control. 37 However, the estimate for the NRM given by the traditional SG algorithm is biased because the output y(k) in the information vector is correlated to the noise v(k) (see Equation (6) for detail).…”

Section: Introductionmentioning

confidence: 99%

“…To decrease the complexity, the stochastic gradient (SG) algorithm is an alternative because it costs only

O (n)

flops each iteration 29,30 . There are many gradient‐based algorithms, such as information gradient algorithms and accelerated gradient algorithms 31‐34 . Among these algorithms, a key term separation gradient iterative algorithm was derived to identify a fractional‐order nonlinear system, 30 a recursive gradient algorithm was proposed to estimate the nonlinear parameters using multifrequency sine signals, 35 a maximum likelihood gradient iterative algorithm was developed for identifying the parameters of bilinear systems, 36 a gradient‐based recursive least squares estimator was applied in the model‐free extremum seeking control 37 …”

Section: Introductionmentioning

confidence: 99%

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Bias compensated stochastic gradient algorithm for identification of an ARX‐type nonlinear rational model and its application in modeling of the dynamic of the cellular toxicity

Jing

Kiely

Luxton

2022

Intl J Robust & Nonlinear

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Nonlinear rational model (NRM) is a generalized nonlinear model, the NAR-MAX model and Volterra model can be regarded as its special cases. In this article, the parameter identification of a class of nonlinear rational models is studied. Due to the coupling of the model output and the information vector, the parameter identification of the NRM is very challenging. To reduce the complexity of the identification, the stochastic gradient algorithm is used. However, the estimate given by traditional stochastic gradient algorithm is biased. To obtain unbiased estimation, the bias is calculated by using the observations and the previous estimate and then compensated to the biased estimate. A variable factor considering the estimation error is introduced to speed up the algorithm. Theoretical analysis shows that the proposed algorithm can obtain an unbiased estimate and has the complexity of O(n). The proposed algorithm is validated by a numerical example and is applied in modeling the dynamics of the cellular toxicity using the tetra-ethyl ammonium chloride. Results show that the proposed algorithm can obtain an accurate estimate for the nonlinear rational model with less computation.

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Section: Introductionmentioning

confidence: 99%

“…To decrease the complexity, the stochastic gradient (SG) algorithm is an alternative because it costs only

O (n)

Section: Introductionmentioning

confidence: 99%

Bias compensated stochastic gradient algorithm for identification of an ARX‐type nonlinear rational model and its application in modeling of the dynamic of the cellular toxicity

Jing

Kiely

Luxton

2022

Intl J Robust & Nonlinear

Self Cite

View full text Add to dashboard Cite

show abstract

“…The measurable disturbance can be a function of the system load in the thermal power plant temperature control system or a function of excitation current in the DC variable speed motor system. 20 Many identification methods were proposed for generalized time-varying systems with white noises, [21][22][23] Ding et al proposed the least squares identification method for generalized time-varying systems. 24 The recursive identification and the iterative identification are two important branch of parameter estimation methods.…”

Section: Introductionmentioning

confidence: 99%

Iterative parameter identification algorithms for the generalized time‐varying system with a measurable disturbance vector

Jiang

Wan

2021

Intl J Robust & Nonlinear

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This article deals with the parameter identification of the generalized time-varying systems. The time-varying parameter vector can be expressed as a coefficient matrix multiplied by a measurable disturbance vector, the common identification methods cannot be used to estimate the parameters of the generalized time-varying systems directly. This motivates us to develop new iterative identification algorithms. The gradient-based iterative (GI) algorithm is proposed by means of the iterative technique. Moreover, a moving data window (MDW) is introduced, which can update the dynamical data by removing the oldest data and adding the newest measurement data. The MDW GI algorithm is proposed to improve the parameter estimation accuracy. The numerical simulation is provided and the simulation results show that the proposed identification methods are effective for the generalized time-varying systems.

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mentioning

confidence: 99%

“…The sixth group of papers 28‐30 considers applications of data‐based learning methods to industrial processes. A stochastic gradient algorithm based on the minimum Shannon entropy is proposed to identify a type of Hammerstein system with random noise 28 . A predictive control strategy based on Hammerstein–Wiener inverse model compensation is proposed aiming at the nonlinearity and large lag of the pH change in wet flue gas desulfurization process 29 .…”

mentioning

confidence: 99%

Data‐based learning control for optimization of nonlinear systems

Wei

Song

Zhang

et al. 2023

Optim Control Appl Methods

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With the development of science and technology, practical systems such as the power systems, traffic systems, robot manipulator systems, etc., have become more complex. Therefore, it is difficult to build practical systems by accurate models. Under the lack of accurate process models, using system data to improve system performance and learn optimal decisions becomes very important. Through the recent years, data-based learning control theories and technologies have widely been investigated, including adaptive dynamic programming, reinforcement learning, iterative learning control, and so on. Data-based methods require the system data instead of the accurate knowledge of system dynamics that can be considered as model-free learning control methods. The data-based methods are effective solutions for the optimal control of nonlinear systems, which motivate this special issue.This special issue aims to collect and present original research dealing with data-based learning and their applications for optimization and control problems. The first group of papers [1][2][3][4][5][6][7] focuses on data-based control theory, approaches, and applications. A fuzzy model predictive control approach is proposed for stick-slip type piezoelectric actuator to realize the precise control of the end effector. 1 A systematic online adaptive dynamic programming control framework is proposed for smart buildings control to ensure hard constraints to be satisfied. 2 A multi-verse optimizer tuned PI-type active disturbance rejection generalized predictive control method is described for the motion control problems of ships. 3 The sufficient optimality conditions for the optimal controls are established under some convexity assumptions. 4 A receding-horizon reinforcement learning algorithm is proposed for near-optimal control of continuous-time systems under control constraints. 5 In order to solve the interference compensation control problem of a class of nonlinear systems, a method based on memory data is introduced to suppress interference greatly. 6 A new controller design method is proposed for the trajectory tracking problem of robots with imprecise dynamic properties and interference. 7 The second group of papers 8-12 considers iterative learning identification and iterative learning control. An iterative learning control approach is proposed for linear parabolic distributed parameter systems with multiple actuators and multiple sensors. 8 The quantized data-based iterative learning tracking control problem is studied for nonlinear networked control systems with signals quantization and denial-of-service attacks. 9 The output tracking problem is considered for a class of nonlinear parabolic distributed parameter systems with moving boundaries. 10 A just-in-time learning based dual heuristic programming algorithm is proposed to optimize the control performance of autonomous wheeled mobile robots under faults or disturbances. 11 A novel optimal constraint-following controller is proposed for uncertain mechanical systems. 12 The third...

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Multierror stochastic gradient algorithm for identification of a Hammerstein system with random noise and its application in the modeling of a continuous stirring tank reactor

Cited by 8 publications

References 53 publications

Bias compensated stochastic gradient algorithm for identification of an ARX‐type nonlinear rational model and its application in modeling of the dynamic of the cellular toxicity

Bias compensated stochastic gradient algorithm for identification of an ARX‐type nonlinear rational model and its application in modeling of the dynamic of the cellular toxicity

Iterative parameter identification algorithms for the generalized time‐varying system with a measurable disturbance vector

Data‐based learning control for optimization of nonlinear systems

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