This paper presents a data-driven framework for integrating encryption transmission and attack detection in cyber-physical systems (CPS) with nonlinear physical plants. The main focus of this research is to use deep n...
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This paper presents a data-driven framework for integrating encryption transmission and attack detection in cyber-physical systems (CPS) with nonlinear physical plants. The main focus of this research is to use deep neural networks to realize the coprime factorization (CF) of nonlinear systems. The definition of the CF guides the network training and designing process, and the model's topology is designed in the state-space form, which improves the interpretability of the data-driven CF. Based on the CF-aided neural networks, an encrypted transmission module is designed that projects information related to system dynamics into a perpendicular data space, which complements existing encryption methods from a control theory perspective. Subsequently, an anomaly detector are designed using the same CF pairs. This detector not only provides high-accuracy detection of attacks but also distinguishes between attacks and faults, thereby reducing the false positive rate and enhancing the reliability of the attack detection. The proposed method has been validated in a real CPS using a mecanum-wheeled vehicle as the physical plant, demonstrating its effectiveness and applicability.
This paper focuses on tracking control for a specific class of discrete-time single-input single-output (SISO) nonlinear repetitive systems that encounter data quantization and dropouts during transmission. The output...
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To address the problems of poor physical interpretability and huge sample size requirement when using neural networks to fit nonlinear control system models for state prediction, this paper proposes a model predictive...
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ISBN:
(纸本)9798350321050
To address the problems of poor physical interpretability and huge sample size requirement when using neural networks to fit nonlinear control system models for state prediction, this paper proposes a model predictive control algorithm based on a physics-informed long short-term memory(LSTM) network. Firstly, the neural network incorporating physical information is extended to model the ordinary differential equations with variable initial states and external control quantities, which makes the network adaptable to the control task and makes the training model physically interpretable. Secondly, a network structure with a mixture of fully connected layers and LSTM layers is built by using the good learning ability of LSTM for time-series data, and the loss function is designed according to the system characteristics and prediction requirements. The trained neural network model is then used as an internal prediction model to construct a nonlinear model predictive control algorithm. Finally, taking the continuous stirring reactor system as an example, the method is verified to be able to fit the system model highly and reduce the time to reach the steady state with a small number of samples.
In multi-input multi-output (MIMO) ultra-precision motion control system, high-performance decoupling method is the key to reduce the interactions between different degree of freedoms (DOFs). However, the manufacturin...
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ISBN:
(纸本)9798350321050
In multi-input multi-output (MIMO) ultra-precision motion control system, high-performance decoupling method is the key to reduce the interactions between different degree of freedoms (DOFs). However, the manufacturing and assembling errors will greatly affect the decoupling performance. Some non-negligible factors, such as the center of gravity (CoG) and actuator positions, cannot be accurately determined, which will deteriorate the control accuracy. To tackle this problem, an iterative control decoupling tuning method by feedforward compensation is proposed in this paper. The proposed strategy using feedback signal to tune the parameters of feedforward compensator iteratively and further compensate for the closed-loop dynamics. The simulation results show that the proposed method is more effective, including the convergence accuracy, convergence speed and the improvement of the tracking error caused by the coupling.
Hexapod robots have become an indispensable part of legged robots due to their excellent stability and traversability, and have recently become a hot research topic. In complex outdoor environments, the ability of the...
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ISBN:
(纸本)9798350321050
Hexapod robots have become an indispensable part of legged robots due to their excellent stability and traversability, and have recently become a hot research topic. In complex outdoor environments, the ability of the hexapod robot to switch its motion strategy according to the terrain is crucial for improving its mobility. This paper presents a terrain recognition network that enables our hexapod robot to switch its forward gait based on the different types of outdoor terrain. This method provides better stability for the hexapod robot than traditional single-gait walking through different terrains.
The reliance of modern power systems on open communication networks for load frequency control (LFC) increases vulnerability to cyber-attacks, particularly denial-of-service (DoS) attacks. This study explores a novel ...
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This article develops a fixed-time identifier for modeling unknown discrete-time nonlinear systems without requiring the standard persistence of excitation (PE) condition. A data-driven update law based on a modified ...
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This article develops a fixed-time identifier for modeling unknown discrete-time nonlinear systems without requiring the standard persistence of excitation (PE) condition. A data-driven update law based on a modified gradient descent (GD) update law is presented to learn the system parameters, which relies on the concurrent learning approach under which the recorded past data and the current data are employed concurrently. Fixed-time convergence guarantees are provided for the modified GD update law under the condition that the recorded data fulfills a rank condition, which is less restrictive than the standard PE condition. To guarantee fixed-time convergence, fixed-time Lyapunov analysis is leveraged. Compared to typical GD-based update laws, two main advantages of the presented approach are: 1) the modified GD update law guarantees fixed-time convergence instead of asymptotic convergence and 2) the convergence guarantee is provided under an easy-to-check rank condition rather than the standard PE condition, which is hard or even impossible to check online. Simulation results are provided to verify the obtained results.
Emergency control in power systems is crucial for maintaining stability and preventing widespread outages. data-driven methods have emerged as vital tools in optimizing response-driven and event-driven emergency contr...
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ISBN:
(纸本)9798350377958;9798350377941
Emergency control in power systems is crucial for maintaining stability and preventing widespread outages. data-driven methods have emerged as vital tools in optimizing response-driven and event-driven emergency controls, particularly in Under-Voltage Load Shedding (UVLS), Under-Frequency Load Shedding (UFLS), and event-driven load shedding (ELS). This systematic review aims to synthesize the current state of research on datadriven methods in the emergency control of power systems, focusing on UVLS, UFLS, and ELS. A systematic review was conducted using the PRISMA framework, examining research papers published between 2014 and 2024. databases such as ieee Xplore and ScienceDirect were searched using relevant keywords. Inclusion and exclusion criteria were applied to ensure the relevance and quality of selected studies. The review identified various data-driven techniques, including optimization algorithms, machine learning approaches, probabilistic methods, and real-time data analytics and control, applied in both response driven and event-driven emergency controls. Each cluster is analyzed for its application, advantages, disadvantages, and complexity. This review highlights the advancements in these methodologies and suggests future research directions to enhance power system stability during emergencies.
The Spin-Exchange Relaxation-Free Comagnetometer (SERFCM) is a new quantum instrument with ultra-high accuracy. Normally, the atomic ensembles of SERFCM operate in an open-loop state, which is not conducive to long-te...
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ISBN:
(纸本)9798350321050
The Spin-Exchange Relaxation-Free Comagnetometer (SERFCM) is a new quantum instrument with ultra-high accuracy. Normally, the atomic ensembles of SERFCM operate in an open-loop state, which is not conducive to long-term highprecision measurements. In order to realize closed-loop control of its atomic polarization state, it is necessary to model and analyze the dynamic characteristics of the SERFCM system. In this paper, a data-driven physical mechanism (DDPM) modeling method is proposed to realize the modeling of the SERFCM, a multi-input multi-output system. First, the state space equations of the SERFCM are established based on the Bloch equation, which are transformed into a discrete transfer function matrix. Then, based on the criterion of least variance in estimation, we realize the modeling of the discrete transfer function matrix using the excitation input data, the measured output data, and the estimated output data. Finally, the simulation results of modeling under different longitudinal magnetic fields confirm the validity of the proposed method. This work enables the online modeling of SERFCM system and facilitates the analysis of the effects of various parameters on the dynamic characteristics.
The problem of time-varying formation analysis and control protocol design for a multi-UAV system with a nonlinear term is considered. Firstly, a formation control protocol for multi-UAV system is proposed for a prede...
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ISBN:
(纸本)9798350321050
The problem of time-varying formation analysis and control protocol design for a multi-UAV system with a nonlinear term is considered. Firstly, a formation control protocol for multi-UAV system is proposed for a predefined time-varying formation. Then, the multi-UAV formation problem is transformed into a consensus problem through the formation reference function. The condition for the multi-UAV system to reach time-varying formation is proposed. And the reference function expression is given. Moreover, the stability of the system is proved by the partial stability method. In addition, the design process of time-varying formation control protocol for multi-UAV system with nonlinear term is given. Finally, a multi-UAV system composed of five UAVs is utilized to verify the feasibility of the method by simulink. Simulation results show that the proposed time-varying formation control method is effective.
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