Model predictive position tracking control for motion system with random communication delay

Li, Qiu; Yang, Xiaomei; Ahsan, Usama; Pan, Jianfei; Zhang, Bo; Yang, Rong

doi:10.1049/iet-cta.2020.0667

Cited by 6 publications

(3 citation statements)

References 28 publications

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“…Proof Choose the Lyapunov functional for the closed‐loop system (13) as follows 42,43

{V}_{(k)}=\sum \limits_{m=1}^5{V}_{(k)}^m

with

\begin{matrix} alignleft \\ align-1 V_{(k)}^{1} & align-2 = ε_{k}^{T} P_{d_{k}, v_{k}} ε_{k}, \\ align-1 V_{(k)}^{2} & align-2 = \sum_{α = k - \overset{true‾}{d}}^{k - 1} ε_{α}^{T} Q_{1} ε_{α} + \sum_{α = k - \underline{d}}^{k - 1} ε_{α}^{T} Q_{2} ε_{α}, \\ align-1 V_{(k)}^{3} & align-2 = \sum_{α = k - d_{k}}^{k - 1} x_{α}^{T} Q_{3} x_{α} + \sum_{θ = - \overset{true‾}{d} + 2}^{- \underline{d} + 1} \sum_{α = k + θ - 1}^{k - 1} x_{α}^{T} Q_{3} x_{α} \end{matrix}

…”

Section: Resultsmentioning

confidence: 99%

Predictive output feedback control of networked control system with Markov DoS attack and time delay

Xiang

Wen

et al. 2023

Intl J Robust & Nonlinear

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Summary This paper studies the networked control systems (NCS) predictive output feedback control issue with random time delay and denial of service (DoS) attacks. The mathematical description of the random DoS attacks and time delays in controller‐to‐actuator (C‐A) and sensor‐to‐controller (S‐C) communication channels is researched based on Markov stochastic theory. Considering the random characteristics of network‐induced attacks and delays, the closed‐loop NCS is modeled as a discrete‐time linear system with Markov DoS attack and time delay. The stochastic stability criterion of a class of networked predictive control systems with random attack and delay is proposed based on Lyapunov stability theory and the predictive compensation control method. The networked model predictive output feedback controller is designed by applying the parameter compensation method and linear matrix inequality (LMI) technique. Numerical simulation results are proposed to verify the effectiveness of the proposed control strategy.

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“…Proof Choose the Lyapunov functional for the closed‐loop system (13) as follows 42,43

{V}_{(k)}=\sum \limits_{m=1}^5{V}_{(k)}^m

with

\begin{matrix} alignleft \\ align-1 V_{(k)}^{1} & align-2 = ε_{k}^{T} P_{d_{k}, v_{k}} ε_{k}, \\ align-1 V_{(k)}^{2} & align-2 = \sum_{α = k - \overset{true‾}{d}}^{k - 1} ε_{α}^{T} Q_{1} ε_{α} + \sum_{α = k - \underline{d}}^{k - 1} ε_{α}^{T} Q_{2} ε_{α}, \\ align-1 V_{(k)}^{3} & align-2 = \sum_{α = k - d_{k}}^{k - 1} x_{α}^{T} Q_{3} x_{α} + \sum_{θ = - \overset{true‾}{d} + 2}^{- \underline{d} + 1} \sum_{α = k + θ - 1}^{k - 1} x_{α}^{T} Q_{3} x_{α} \end{matrix}

…”

Section: Resultsmentioning

confidence: 99%

Predictive output feedback control of networked control system with Markov DoS attack and time delay

Xiang

Wen

et al. 2023

Intl J Robust & Nonlinear

Self Cite

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“…By introducing nonlinear random matrix and esg factor to modify the traditional deep learning model, the optimized deep learning model is obtained, which can describe and analyze different types of indicators [23,24]. In order to analyze the calculation of stock excess return prediction by the optimized learning model, the corresponding calculation process is drawn, as shown in Figure 9.…”

Section: Application Of Optimized Deep Learning Model In Stock Excess...mentioning

confidence: 99%

Deep Learning Model for Stock Excess Return Prediction Based on Nonlinear Random Matrix and Esg Factor

Meng

Yahya

Chai

2022

Mathematical Problems in Engineering

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Aiming at the problem that the traditional model has low accuracy in describing stock excess return, in order to further analyze the change law of stock excess, based on the nonlinear random matrix and esg factor theory, the traditional learning model is analyzed, and the corresponding optimized deep learning model is obtained by introducing the single ring theorem and statistical data. Through the analysis and research of related indexes, the change rules of different indexes are obtained, and the optimization model is used to calculate and forecast the excess return of stocks. The results show that the statistics and spectral radius show typical local linear variation with the increase of eigenvalue. The corresponding statistics show a trend of gradual increase. The corresponding spectral radius has a decreasing variation law, and the two curves have obvious symmetry at some eigenvalues. It can be seen from the change curves under different factors that the change trend of the yield curve is mainly affected by the investment factor, while the change rule of the specific value of the yield curve is controlled by the profit factor. This shows that the two factors have the same influence on the stock excess return. The influence of optimized deep learning model on stock excess index has typical linear characteristics, which can be divided into linear increase and linear decline according to different change rules. The basic type has the greatest influence, while the corresponding pattern analysis type has the least influence. Finally, the method of experimental verification is used to verify the stock excess data, and the results show that the optimized deep learning model can better characterize the experimental results. Therefore, the optimized deep learning model based on nonlinear random matrix and esg factor can carry out targeted analysis of different types of stock returns, thus improving research ideas and calculation methods for the application of deep learning model in different fields.

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“…A combination of backstepping and adaptive techniques (in [19][20][21]) was introduced for input-delay systems represented in a strict-feedback form with uncertainties to achieve-in the best cases-semi-uniform ultimate boundedness of systems. In addition, by taking advantage of modern hardware, more complex control techniques can be implemented with delay systems, such as aperiodic sampling [22] and model predictive controller [23]. Otherwise, by treating the difference between the current and delayed control signals as input disturbance, the control objective becomes the attenuation of the matching disturbances, which the SMC and robust control schemes are well known to be able to deal with.…”

Section: Introductionmentioning

confidence: 99%

A Nonlinear Backstepping Controller Design for High-Precision Tracking Applications with Input-Delay Gimbal Systems

Park

Chakir

Kim

et al. 2021

JMSE

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This paper proposes a novel nonlinear control approach for a two-axis gimbal to achieve accurate real-time tracking performance in maritime surveillance applications. For this objective, the control system must overcome system complexities and limitations, including nonlinear dynamics, coupled Euler angle-based measurements, and delay time constraints. The nonlinear backstepping controller was designed, taking into consideration the nonlinearities and system couplings to preserve the system stability. Then, an extra backstep was incorporated to minimize the control errors due to the delay time. The proposed control scheme enhances the tracking performances and expands the system’s bandwidth, which is validated in the simulations and experimental studies in comparison with a super-twisting sliding mode controller introduced in a previous study.

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Model predictive position tracking control for motion system with random communication delay

Cited by 6 publications

References 28 publications

Predictive output feedback control of networked control system with Markov DoS attack and time delay

Predictive output feedback control of networked control system with Markov DoS attack and time delay

Deep Learning Model for Stock Excess Return Prediction Based on Nonlinear Random Matrix and Esg Factor

A Nonlinear Backstepping Controller Design for High-Precision Tracking Applications with Input-Delay Gimbal Systems

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