Fault-Tolerant Control via Information Theoretic Techniques

Dear Colleagues,

Most modern systems are actually safety-critical systems whose failure could result in loss of life, significant property damage or damage to the environment. Well-known examples of such systems are in application areas such as medical devices, aircraft flight control, nuclear systems, automotive systems, robotics, power plants, etc. In order to ensure the operational safety of critical dynamic systems, fault-tolerant control system (FTCS) technologies have undergone considerable and continuous development in the past several years. These developments have been driven by advances in model-based control and estimation theory, and are now increasingly fueled by machine learning and data-driven techniques. One of the fundamental concepts that has underpinned many developments in the whole area of control systems theory is that of information, which stems from Shannon's mathematical theory in communication. Indeed, several works have shown that an information-theoretic approach to control systems can be instructive and effective. A key observation has been the fact that a control system might be viewed intuitively as an information-gathering and decision-making system. The controller collects information from a dynamic system by measuring/estimating its state for the purpose of learning, and then use this information to perform a control action on that system. In particular, for stochastic systems, the information collected by a controller from a plant can be quantified using the related concept of mutual information. Furthermore, this mutual information has been shown to be at the source of some limits relating as a trade-off the informativeness of the data in a control system and its overall performance. With the emergence of new paradigms essentially based on data-driven approaches for real-time supervision of critical dynamical systems, one of the important properties of such systems will be the richness of the information collected in real-time for the online development of fault-tolerant control strategies under faulty system behaviors.

This Special Issue targets recent research, trends and practical developments in the field of fault-tolerant control (FTC), where the mathematical concept of information plays a key role in the synthesis of FTC algorithms. The aim of this Special Issue is to exchange researchers’ achievements in recent advances that treat various fault-tolerant control as well as fault detection and diagnosis (FDD) techniques using information-theoretic approaches and their combination with other approaches.

Specifically, this Special Issue seeks high quality theoretical and application papers as well as survey papers in FTCS/FDD using the following non-exhaustive information-theoretic based methods:

▪ Mutual information methods;

▪ Entropy-based measures methods;

▪ Minimum entropy control methods;

▪ Information-based dual control and probing methods;

▪ Dual adaptive fault-tolerant control methods;

▪ Maximum entropy method;

▪ Cross-entropy minimization methods;

▪ Minimum rational entropy methods;

▪ Neural network and deep learning entropy methods.

Dr. Joseph J. Yame
Dr. Tushar Jain
Guest Editors

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• fault accommodation
• control reconfiguration
• adaptive fault tolerant control
• MPC-based FTC
• stochastic control systems
• persistency of excitation
• dual control
• probing
• mutual information
• entropy measures