Adaptive control for a class of nonlinear chaotic systems with quantized input delays

Asadollahi, Mostafa; Ghiasi, Amir Rikhtehgar; Badamchizadeh, Mohammad Ali

doi:10.1016/j.jfranklin.2019.10.004

Cited by 23 publications

(6 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, the limitations of PID control go evident when applied to more complicated systems, especially the multiple-input-multiple-output (MIMO) nonlinear systems with time-delay, nonlinearity and coupling characteristics (Wallam and Tan, 2019). They may lead to larger overshoot, longer adjusting time and system instability (Asadollahi et al, 2020; Bikas and Rovithakis, 2023).…”

Section: Introductionmentioning

confidence: 99%

Recursive d-step-ahead predictive control of MIMO nonlinear systems with input time-delay via multi-dimensional Taylor network extended from PID

Yan

Zhang

2023

Transactions of the Institute of Measurement and Control

View full text Add to dashboard Cite

In this paper, a recursive d-step-ahead predictive control scheme based on multi-dimensional Taylor network (MTN) is proposed for the real-time tracking control of multiple-input multiple-output (MIMO) nonlinear systems with input time-delay. The MTN predictive model is designed using a recursive approach to compensate the influence of time-delay, and an extended Kalman filter (EKF) is applied as its learning algorithm. An MTN controller is developed based on a proportional–integral–derivative (PID) controller where the closed-loop errors between the reference input and the system output are set as the MTN controller’s inputs. Then, a back propagation (BP) algorithm, designed to update its weights according to errors caused by system uncertainty, is used as a learning algorithm for the MTN controller. Meanwhile, the convergence of the MTN predictive model and the stability of the closed-loop system are evaluated. Two numerical examples and a practical example – continuous stirred tank reactor (CSTR) process are presented to verify the superiority of the proposed scheme. The experimental results and the computational complexity analysis show that the proposed scheme is effective, promising its desirable robustness, anti-disturbance, tracking and real-time performance.

show abstract

Section: Introductionmentioning

confidence: 99%

Recursive d-step-ahead predictive control of MIMO nonlinear systems with input time-delay via multi-dimensional Taylor network extended from PID

Yan

Zhang

2023

Transactions of the Institute of Measurement and Control

View full text Add to dashboard Cite

show abstract

“…As a bridge connecting chaos theory and real life, the chaotic time series [1,2] has attracted wide attention. Because of its rich variation of laws in a nonlinear [3] dynamic system, it has a high theoretical and research value in exposing the deterministic laws that may be implied in mining random phenomena. At the same time, because the chaotic time series involves nearly all areas of daily life, it has important applicational value in practical production and in life, and the research on chaotic time series prediction methods has very important practical value.…”

Section: Introductionmentioning

confidence: 99%

n-Dimensional Chaotic Time Series Prediction Method

et al. 2022

View full text Add to dashboard Cite

Chaotic time series have been involved in many fields of production and life, so their prediction has a very important practical value. However, due to the characteristics of chaotic time series, such as internal randomness, nonlinearity, and long-term unpredictability, most prediction methods cannot achieve high-precision intermediate or long-term predictions. Thus, an intermediate and long-term prediction (ILTP) method for n-dimensional chaotic time series is proposed to solve this problem. Initially, the order of the model is determined by optimizing the preprocessing and constructing the joint calculation strategy, so that the observation sequence can be decomposed and reorganized accurately. Furthermore, the RBF neural network is introduced to construct a multi-step prediction model of future sequences, with a feedback recursion mechanism. Compared with the existing prediction methods, the error of the ILTP method can be reduced by 1–6 orders of magnitude, and the prediction step can be increased by 10–20 steps. The ILTP method can provide reference technology for the application of time series prediction with chaotic characteristics.

show abstract

“…This highlights the need for a powerful robust or adaptive controller to synchronize chaotic systems. In order to solve this challenge, much research studies have been performed on chaos synchronization (Alfi, 2012; Asadollahi et al, 2020; Chen and Zhang, 2007; Ghosh and Banerjee, 2008; Hsu, 2011; Kuo, 2011; Li et al, 2001; Liu et al, 2012; Xiang and Huangpu, 2010; Zhang and Yang, 2012; Zhang et al, 2004). Fuzzy Systems (Ghosh and Banerjee, 2008; Kuo, 2011) and neural networks (Hsu, 2011; Liu et al, 2012) as universal approximators with excellent learning capabilities have been playing important roles in many research studies.…”

Section: Introductionmentioning

confidence: 99%

Chaos synchronization using q-Chlodowsky operators as uncertainty approximator

Izadbakhsh

Khalesi

Khorashadizadeh

2022

Journal of Vibration and Control

View full text Add to dashboard Cite

In this article, q-Chlodowsky operators are utilized for synchronizing a chaotic master-slave system. The universal approximation property enables q-Chlodowsky operators to approximate uncertainties, which consist of disturbances and unmodeled dynamics. It is verified that uniformly ultimately bounded stability of the tracking/approximation errors can be assured if the q-Chlodowsky operators are utilized as regressors and the polynomial coefficients are tuned by the adaptive laws calculated in the stability analysis. Moreover, it has been assumed that the rate of synchronization error is not at hand and a filter-based strategy has been adopted instead of designing an observer. The Duffing-Holmes oscillator is studied and the results are also compared with three powerful approximation-based and robust control methods. Unlike neural network/fuzzy estimators in which system states are necessary to define the regressor vector, the proposed uncertainty estimator does not require them in the regressor vector. Furthermore, in comparison with radial basis functions neural networks (RBFNNs), we are involved in fewer adaptive coefficients in the regressor vector of the proposed uncertainty estimator. As a result, the tedious and time-consuming procedure of trial and error for tuning the uncertainty estimator is eliminated.

show abstract

Adaptive control for a class of nonlinear chaotic systems with quantized input delays

Cited by 23 publications

References 21 publications

Recursive d-step-ahead predictive control of MIMO nonlinear systems with input time-delay via multi-dimensional Taylor network extended from PID

Recursive d-step-ahead predictive control of MIMO nonlinear systems with input time-delay via multi-dimensional Taylor network extended from PID

n-Dimensional Chaotic Time Series Prediction Method

Chaos synchronization using q-Chlodowsky operators as uncertainty approximator

Contact Info

Product

Resources

About