This study presents a reinforcement learning-based interval type-2 (IT-2) fuzzy formation tracking control strategy for leader-follower flexible joint robots (FJRs), addressing challenges arising from nonlinearities, unmodeled dynamics, and unknown external disturbances. The proposed approach employs fractional-order sliding mode control (FOSMC) to guarantee finite-time convergence of the tracking error with rigorously proven stability. Unlike conventional backstepping methods, the IT-2 fuzzy FOSMC framework effectively addresses uncertainties and disturbances. By considering the memory effect of fractional calculus, a Hamilton-Jacobi-Bellman equation for the formation tracking dynamics is derived using an auxiliary system and equivalent transformation. A novel cost function based on the derivative of the formation tracking error is introduced, resulting in a discounted cost that accounts for the Laplacian matrix$\widehat{L}$under a performance constraint. The optimal cost function and formation sliding mode control policy are shown to be gradually approximable via policy iteration. To achieve intelligent near-optimal control, a reinforcement learning algorithm based on an actor-critic neural network architecture is implemented, enabling all agents to compensate for system uncertainties and maintain formation tracking with the leader. Lyapunov stability analysis confirms that all follower FJRs can track the leader using actor-critic update laws for neural network weights, with distributed tracking error designs ensuring convergence to optimal values. Simulation results validate the proposed approach, demonstrating reduced chattering, improved formation tracking, and enhanced robustness.

This study presents a novel reinforcement learning (RL)-based, predefined-time tracking fault-tolerant control (FTC) scheme with prescribed performance for handling unknown stochastic nonlinear systems (SNSs) operating in various environments with actuator faults. The scheme addresses multiple actuator fault types, including loss-in-effectiveness (LIE) and lock-in-place (LIP), within a unified theoretical framework in which some actuators may become partially or completely disabled. This framework learns the stochastic nonlinear dynamics and control behaviors of the system using fuzzy logic systems (FLSs) within an RL-based identifier-critic-actor (ICA) structure. By combining prescribed performance control with predefined-time control, the proposed controller achieves fault-tolerant tracking performance, guarantees that all signals are probabilistically bounded, and preserves the output within a specified range. Unlike traditional FTC methods, which depend on knowing the system model to handle LIP faults and bias, this method addresses LIP faults without requiring prior knowledge of the system and achieves different performance levels by utilizing an RL-based ICA to adjust the FLS weights. A practical example using a single-link robot arm driven by a brushed dc (BDC) motor demonstrates the effectiveness and improved performance of the developed FTC scheme.

The main objective of this study is to develop an intelligent, resilient event-triggered control method for fractional-order multiagent networked systems (FOMANSs) using reinforcement learning (RL) to address challenges resulting from unknown dynamics, actuator faults, and denial-of-service (DoS) attacks. First, the challenge of unknown system dynamics within their environment must be addressed to achieve desired system stability in the face of unknown dynamics or to optimize consensus in FOMANSs. To address this problem, an adaptive learning law is implemented to handle unknown nonlinear dynamics, parameterized by a neural network, which establishes weights for a fuzzy logic system utilized in cooperative tracking protocols. A novel distributed control policy facilitates signal sharing through RL among agents, reducing error variables through learning. Moreover, this study combines an RL algorithm with the sliding mode control strategy to optimize the parameterization of the distributed control protocol, thereby eliminating its constraints on initial conditions. Second, realizing that DoS attacks typically make the actuator signal inaccessible for distributed control protocols, an innovative intelligent dual-event-triggered control strategy is formulated to reduce the effects of DoS attacks. By coordinating nested event triggers across various channels, the distributed control input is protected from incorrect signals from DoS attacks, thus ensuring its resilience. To address this problem, an intelligent security dual-event-triggered control protocol guarantees Mittag-Leffler stability of the closed-loop system and ensures effective sliding motion conditions. This distributed control protocol ensures robust tracking of control tasks and mitigates "Zeno behavior" during event triggering. The proposed control strategy is validated using a single-link flexible-joint robotic manipulator system.

In this article, the Mittag-Leffler synchronization (MLS) problem of a fractional-order permanent magnet synchronous generator (FOPMSG)-based wind turbine system against unknown disturbances, such as external load torque variations and system parameter uncertainties, an adaptive fractional sliding-mode control (AFSMC) method is proposed based on improved convergence rate performance of the FOPMSG to track accuracy, response speed, and robustness. The AFSMC method is based on a fractional-order term incorporated into the new law for reaching the sliding mode, improves the chattering in the control signal, and reduces the time required for the system to reach the sliding-mode surface. Sufficient conditions are derived to ensure the robust MLS for the sliding-mode dynamics by the designed robust controller. In this article, for the first time, an adaptive sliding-mode control (ASMC) with a terminal function that accurately controls the FOPMSG model at a prespecified time is proposed. Moreover, the designed ASMC can effectively attenuate the existence of disturbances and uncertainties by eliminating the reaching phase based on the Lyapunov stability theory. Finally, the simulation results applied to the FOPMSG model show that the proposed control method has better disturbance rejection ability, fast dynamic response, and suppression of the chattering effect.

This article studies dissipativity analysis of fractional-order quaternion-valued neural networks (FOQVNNs) with time delays. Two specific activation functions are considered along with common bounded and activation functions of Lipschitz-kind. Since quaternion multiplication is not commutative, we must divide the model, which is evaluated by quaternion, into four elements that are real-valued elements. On the basis of the construction of novel Lyapunov functional, and applying fractional-calculus theory, new criteria for the test of the global dissipativity and exponential stability of FOQVNNs model are established. FOQVNNs have also been suggested to provide global dissipativity and exponential stability, whereas nonlinear complex activation functions are constrained by the usage of linear matrix inequality methods, which utilize quaternion matrices and positive quaternion definite matrices. Finally, the effectiveness and superiority of the proposed approach is validated through numerical examples.

In this paper, the leader-following cyber secure consensus problem for discrete-time fractional-order multi-agent systems (DFOMASs) in the present of denial-of-service attacks by means of distributed delayed control strategy is investigated. As MASs work in networked environments, their security control becomes critically desirable in response to various cyberattacks, such as denial of service (DoS). The resulting topologies caused by DoS attacks may destabilize the consensus performance of MASs. Especially under connectivity-broken attacks, the connectivity between agents is destroyed. To deal with these difficulties, a novel defense strategy consisting of distributed delayed consensus control is proposed. To guarantee cyber secure consensus of the addressed systems to determine the stability of the resulting error system, sufficient criteria including the condition in terms of LMI are derived on the basis of the Caputo fractional difference operator, by employing the Lyapunov function approach, algebraic graph theory and average dwell time (ADT). At last, the effectiveness of the obtained results is demonstrated by performing simulations on the proposed systems.