This paper maidenly formulates a distributed secure consensus tracking control framework for a leader-following cyber-physical multi-agent system (CP-MAS) subject to malicious cyber-intrusions. The proposed consensus tracking control framework synergistically combines model reference adaptive control (MRAC) and super-twisting terminal sliding mode control (ST-TSMC), applied to a partially unknown CP-MAS, to enable cyber-resiliency with minimal tracking error and improved chattering performance. Due to stealthy deception attacks on sensor measurements, the states of follower agents are compromised. Thus, to minimize the conservativeness in realizing the proposed cyber-resilient control framework, a radial basis function neural network (RBF-NN)-assisted state observer has been formulated to estimate the uncompromised states of CP-MAS. Using graph theory and the Lyapunov argument, online adaptation laws have been derived that guarantee the boundedness of all signals and finite-time global asymptotic stability of consensus errors. Numerical simulations have been conducted on a group of four single-link robot manipulator agents subjected to hybrid cyberattacks. The presented qualitative and quantitative assessment demonstrates the proficiency and cyber-resiliency of the proposed hybrid control framework over MRAC, ST-SMC, and other reported methodologies in the face of unknown malicious attacks.

The growth in adoption of communication technologies in the power system results in an increase in its vulnerability towards cyberattacks. This paper presents a novel state partition-particle filter (SP-PF) based detection algorithm that dynamically adapts to varying operating conditions. The algorithm partitions state variables into size-restricted blocks, effectively grouping highly correlated variables to enhance computational efficiency and detection accuracy. Our approach consists of two main steps: (i) state-partition estimation of variables and (ii) detection based on likelihood conditions. The proposed detection algorithm was tested in a real-time cyber-physical environment using a real-time digital simulator (RTDS) in hardware-in-loop configuration with PMUs and a synchronization clock, all connected via standard TCP/UDP protocols. Experimental results demonstrate successful detection of false data injection attacks, replay attacks, and hybrid attacks under various operating conditions. Comparative analysis with extended Kalman filter shows that our approach achieves significantly improved accuracy in state estimation with reduced mean square error, enhancing the overall robustness of the detection mechanism.

In this paper, a two term control law is developed for position control of Twin rotor multiple-input-multiple-output (MIMO) system (TRMS). The proposed controller is an added delay term in conventional Composite Nonlinear Feedback (CNF) control which improves robustness of the controller with fast transient response and better damping characteristics. Computer simulations are performed on MATLAB to validate the results. Stability analysis is done by Lyapunov-Krasovskii functional.

In this paper, a stable neuro adaptive controller for trajectory tracking is developed for robot manipulators without velocity measurements, taking into account the actuator constraints. The controller is based on structural knowledge of the dynamic equations of the robot and measurements of joint positions only. The gravity torque which may include payload variation and disturbances etc represent system uncertainty, which is estimated by a single layer Chebyshev neural network (CNN). The adaptive controller represents an amalgamation of a filtering technique to eliminate velocity measurements and the theory of function approximation using CNN to estimate the gravity torque. The proposed controller ensures the local asymptotic stability and the convergence of the position error to zero. The proposed controller is robust not only to structured uncertainty such as payload parameter variation but also to unstructured one such as disturbances. The validity of the control scheme is shown by simulation studies on a two link robot manipulator.

The stable fuzzy adaptive controller for trajectory tracking is developed for robot manipulators without velocity measurements, taking into account the actuator constraints. The controller is based on structural knowledge of the dynamics of the robot and measurements of link positions only. The gravity torque which may include payload variation etc. represents system uncertainty, which is estimated by a fuzzy logic system (FLS). The adaptive controller represents an amalgamation of a filtering technique to eliminate velocity measurements and the theory of function approximation using FLS to estimate the gravity torque. The proposed controller ensures the local asymptotic stability and the convergence of the position error to zero. The proposed controller is robust not only to structured uncertainty such as payload parameter variation but also to unstructured one such as disturbances. The validity of the control scheme is shown by simulations of a two link robot manipulator.