Quantized data‐based iterative learning control under denial‐of‐service attacks

Zhou, Chang-Ren; Che, Wei‐Wei

doi:10.1002/oca.2769

Cited by 8 publications

(11 citation statements)

References 34 publications

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“…Under the assumption that all iterative uncertainties were bounded, conditions to ensure the boundedness of all system trajectories and to ensure the convergence of tracking errors were given in [18]. In the presence of data quantization and DoS attacks, a quantized data-based iterative learning tracking control algorithm was proposed for a class of repetitive nonlinear systems in [19].…”

Section: Introductionmentioning

confidence: 99%

Data-Based Security Fault Tolerant Iterative Learning Control under Denial-of-Service Attacks

Zhou

Che

et al. 2022

Actuators

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This paper mainly studies the data-based security fault tolerant iterative learning control (SFTILC) problem of nonlinear networked control systems (NCSs) under sensor failures and denial-of-service (DoS) attacks. Firstly, the radial basis function neural network (RBFNN) is used to approximate the sensor failure function and a DoS attack compensation mechanism is proposed in the iterative domain to lessen the impact of DoS attacks. Then, using the dynamic linearization technology, the nonlinear system considering failures and network attacks is transformed into a linear data model. Further, based on the designed linearization model, a new data-based SFTILC algorithm is designed to ensure the satisfactory tracking performance of the system. This process only uses the input and output data of the system, and the stability of the system is proved by using the compression mapping principle. Finally, a digital simulation is used to demonstrate the effectiveness of the proposed SFTILC algorithm.

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Section: Introductionmentioning

confidence: 99%

Data-Based Security Fault Tolerant Iterative Learning Control under Denial-of-Service Attacks

Zhou

Che

et al. 2022

Actuators

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“…In recent works, QILC method has also been applied to control multi-agent systems 14 and deals with network attacks. 15 However, it is worth pointing out that there is an intrinsic issue with static logarithmic quantizer, that is, it needs to be recalculated each time and the quantized data are independent of the previous state variable data. Therefore, it cannot store data and the computational load is heavy.…”

Section: Introductionmentioning

confidence: 99%

“…Subsequently, several QILC works 12,13 have been done to improve the tracking performance iteratively by quantifying the tracking error using a static logarithmic quantizer. In recent works, QILC method has also been applied to control multi‐agent systems 14 and deals with network attacks 15 …”

Section: Introductionmentioning

confidence: 99%

Predictive compensation based quantization iterative learning control for nonlinear nonaffine discrete‐time systems

Zhang

Chi

Huang

2022

Intl J Robust & Nonlinear

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In this work, the problems of predictive compensation, unknown nonlinearity, and nonaffine structure are considered simultaneously for a quantized iterative learning control (QILC) design and analysis under a data‐driven framework. The compensation strategy can avoid deteriorated data transmission quality owing to limited channel capacities. First, a dynamic linearization methodology is employed to transform the nonlinear plant into a virtual iterative linear data model (iLDM) which includes all the input signals over a time‐window from the initial time instant to the current one. The iLDM is also used as a predictive model to estimate the unavailable information caused by the encoding–decoding mechanism. Then, a predictive compensation‐based QILC is proposed by optimizing quadratic functions, which includes an output prediction mechanism, a quantized iterative learning updating law, a quantized iterative parameter estimation law, and a resetting algorithm. The result is also extended to a class of MIMO nonlinear nonaffine discrete‐time systems. The developed control laws are data‐driven and independent of any system information. The theoretical results are proved by the use of contraction mapping principle and induction method. Examples are provided to verify the effectiveness of the proposed methods.

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“…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 .…”

mentioning

confidence: 99%

“…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 .…”

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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Quantized data‐based iterative learning control under denial‐of‐service attacks

Cited by 8 publications

References 34 publications

Data-Based Security Fault Tolerant Iterative Learning Control under Denial-of-Service Attacks

Data-Based Security Fault Tolerant Iterative Learning Control under Denial-of-Service Attacks

Predictive compensation based quantization iterative learning control for nonlinear nonaffine discrete‐time systems

Data‐based learning control for optimization of nonlinear systems

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