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Application of deep learning in iron ore sintering process: a review
In the wake of the era of big data, the techniques of deep learning have become an essential
research direction in the machine learning field and are beginning to be applied in the steel …
research direction in the machine learning field and are beginning to be applied in the steel …
Distributed data-driven model predictive control for heterogeneous vehicular platoon with uncertain dynamics
To alleviate the adverse effects of heterogeneous vehicular platoon (HVP) with uncertain
dynamics, a distributed data-driven model predictive control (DDMPC) strategy is proposed …
dynamics, a distributed data-driven model predictive control (DDMPC) strategy is proposed …
Real-time attack-recovery for cyber-physical systems using linear-quadratic regulator
The increasing autonomy and connectivity in cyber-physical systems (CPS) come with new
security vulnerabilities that are easily exploitable by malicious attackers to spoof a system to …
security vulnerabilities that are easily exploitable by malicious attackers to spoof a system to …
Real time congestion management using generation re-dispatch: Modeling and controller design
Increased power demand, poor transmission infrastructure, and uncertain renewable
generation cause congestion in the power network. Traditionally network congestion is …
generation cause congestion in the power network. Traditionally network congestion is …
Recovery from adversarial attacks in cyber-physical systems: Shallow, deep, and exploratory works
Cyber-physical systems (CPS) have experienced rapid growth in recent decades. However,
like any other computer-based systems, malicious attacks evolve mutually, driving CPS to …
like any other computer-based systems, malicious attacks evolve mutually, driving CPS to …
Learn-to-respond: Sequence-predictive recovery from sensor attacks in cyber-physical systems
While many research efforts on Cyber-Physical System (CPS) security are devoted to attack
detection, how to respond to the detected attacks receives little attention. Attack response is …
detection, how to respond to the detected attacks receives little attention. Attack response is …
Real-time data-predictive attack-recovery for complex cyber-physical systems
Cyber-physical systems (CPSs) leverage computations to operate physical objects in real-
world environments, and increasingly more CPS-based applications have been designed …
world environments, and increasingly more CPS-based applications have been designed …
Driver-centric data-driven robust model predictive control for mixed vehicular platoon
The penetration rate of automated vehicles (AVs) may remain unsaturated for a long time,
resulting in the coexistence of AVs and human-driven vehicles (HDVs). The non-ideal …
resulting in the coexistence of AVs and human-driven vehicles (HDVs). The non-ideal …
Resilient self-triggered predictive control for nonlinear system under dual-channel deception attacks
K Ma, N He, Z Fan - Nonlinear Dynamics, 2024 - Springer
For nonlinear cyber physical systems (CPS) with additional disturbances and system
constraint, a resilient self-triggered model predictive control (RST-MPC) strategy is …
constraint, a resilient self-triggered model predictive control (RST-MPC) strategy is …
Robust intrusion detection for resilience enhancement of industrial control systems: An extended state observer approach
We address the problem of attack signal estimation in industrial control systems that are
subjected to actuator false data injection attack (FDIA) and where the sensor measurements …
subjected to actuator false data injection attack (FDIA) and where the sensor measurements …