·        Robust Fault Diagnosis combining machine/deep learning and model-based/structural analysis techniques using LPV, zonotopic set-membership approach;

·        Fault Detection using Game Theory and Reinforcement Learning

·        Decentralized/Distributed- Model based fault detection and isolation:

- Structural Analysis; Graph Theory and Decomposition, Structural Models, Test Sets;

- System partitioning; Structural Model Graph partitioning;

- Sensor Placement Optimization; Optimal Local Residual Generator Design

·        Distributed FDI (Fault Detection, Isolation) Approaches based on Unknown Input Observers (UIO) for model changes- uncertain parameters and couplings

- Global/Local Models;

- Distributed/Decentralized State Estimation;

·        Model Based Approaches

·        Advanced Machine/Deep Learning Approaches

·        Distributed Model Predictive Control using Optimality Condition - Sensitivity Analysis - Decomposition, Coordination, and Community Detection;

·        Distributed zonotope set-membership bound-tightening based Moving Horizon Estimation (D-MHE) considering Parametric Uncertainty, for robust distributed    control and state estimation

- Integration of controllers and observers within the hierarchical approach

- Static/Dynamic Partitioning approaches

·        Robust Economic Model Predictive Control (EMPC); a Zonotope based controller considering demand uncertainty

·        Hierarchical Model Predictive Control

·        Distributed active fault isolation approach for deciding strategies for disconnection/Plug and Play and local controller reconfiguration

·        Time-Varying (Information Sharing Network) Scheme for Noncentralized- Model Predictive- Control of Large-Scale Systems; Plug and Play ability;

- Static/Dynamic Partitioning approaches; Dynamical Tuning;

- Distributed Population Dynamics (Evolutionary Game Theory) Based Methodology

- Community Detection; Hierarchical Clustering Based Methodology;

·        Distributed MPC Using Reinforcement Learning Based Negotiation;

·        Machine Learning based Model Identification for adaptive Distributed Model Predictive Control using deep neural networks

·        MPC Reconfiguration based Fault Adaptive- Tolerant- Control Strategy using UIOs

·        Distributed Actuator and Sensor Reconfiguration Approaches

·        System Reconfiguration based on hardware redundancy, graph theory- structural analysis;

·        Automatic Response Methodology for Failure Recovery; Local/Global Scenario Approaches

·        a distributed/decentralized framework for Plug and Play ability - Modularity

·        Dynamic - Repartitioning approaches; Distributed Parallel Computing; Load Balancing; Cluster Optimization;

·        System/Control Reconfiguration Approaches

- Centralized/Decentralized/Distributed Learning;

- Distributed/Decentralized Execution

·        Model Predictive Control (MPC) based Reinforcement Learning (MPC-DDPG);

- Demand Response - Peak Power Management; Battery Storage/Charging; Safe Autonomous Driving;

·        Autonomous Driving; Cooperative Driving -Vision-Communication based- Traffic Safety;

·        Data-Driven Identification and the polytopic Takagi-Sugeno (T-S) Learning based Model Predictive control (MPC/MHE)

·        Autonomous Vehicle State Estimation and Mapping Using Takagi–Sugeno Modeling and LPV Approaches

·        Zonotope-Tube-based LPV-MPC Approach for Autonomous Driving

·        Control Reconfiguration of ADAS Controller in the Presence of Driver Errors Using LPV Approaches

·        Fault Tolerant Control based on Coordinated Reconfiguration allocation strategy considering Model Predictive Control (MPC) for the vehicle model

·        Linear-parameter-varying (LPV) Control Reconfiguration based on actuator selection strategy for Fault-Tolerant / Energy Efficient Control

·        Adaptive MPC for Optimal Power Flow of Hybrid- Electric Autonomous Vehicles

·        Game Theoretic/Reinforcement Learning Based Model Predictive control (MPC) for Autonomous Driving

·        Reinforcement Learning (RL) based Fault-Tolerant Control (RL-FTC) for Distributed Drive Electric Systems of Autonomous Vehicles with Fault Conditions

- RL based Fault-Tolerant Coordination Control utilizing Model Predictive Control (MPC) for the vehicle model (RL-FT-NMPC) considering Stability together with Optimal Power Consumption 

- Cooperative - Distributed/Decentralized MPC Approaches

·        Control Algorithms;

·        Monitoring Schemes;

Security of Consensus- Distributed Control of (Heterogeneous) Networks/Multi-Agent Systems under the effect of faults and attacks; model changes, uncertain parameters or couplings/communications

Attack Mitigation Strategies;

Active Strategies

- Robust Approaches/Techniques previously mentioned for Distributed Model Predictive Control (DMPC) and Distributed Moving Horizon Estimation (DMHE) applications:

·        Distributed/Decentralized State Estimation; observers to reconstruct state signals, MPC strategy

·        Machine Learning based Model Identification/Prediction for adaptive DMPC

·        Plug & play; Statistical methods for disconnection

·        Distributed Fault Detection (Estimator) architecture for disconnection

·        Zonotope/Tube based (parametric uncertainty) based MPC for fault isolation, and deciding strategies for disconnection of faulty subsystems or the       reconfiguration of local controllers

·        Set-theoretic receding horizon control

·        Controller Reconfiguration approach under attack; a hybrid- dynamic game approach

·        Hierarchical framework

Passive Strategies

- Preconfigured- attack scenario based robust controllers (reviewed)

·        Contract based distributed NMPC- coupled dynamics - sensitivity information

·        Contract based adaptive distributed NMPC based on distributed attack identification

Distributed Attack/Intrusion Detection, Identification Systems, Strategies

- Analytic and Learning Detection Strategies; Deception (Replay) attacks, etc.

·        Distributed/Decentralized State Estimation based on Observers

·        Distributed/Decentralized (FDI) Unknown Input Observers (UIOs)

·        Communication Attacks; Compromised Nodes/Edges; Observability & Isolability Techniques

·        Distributed Fault Detection architecture for Attack Detection; Nonlinear State Estimators;

·        Model-based Detection (Analytical Redundancy) of Attacks

·        Zonotope set-based approaches, quadratic parameter varying observers, etc.

·        Data-Driven and Learning Approaches; Anomaly Detection; Distributed ML; Deep Learning;

·        Game Theoretic Approaches;