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This study proposes a fault-tolerant optimized backstepping (OB) control for the attitude system having actuator failure of quadrotor unmanned aerial vehicle (QUAV). The design principle of OB technology is to perform reinforcement learning (RL) in every backstepping subsystem, so that the entire backstepping control can attain optimization performance. However, owing to the complication and intricateness of OB control algorithm, it will make the QUAV attitude control tenderly happen system failures when executing the long-time running and high-intensity works. For devising the OB attitude control to have the fault-tolerate capability, the RL is integrated with an adaptive estimation of the proportion term of unknown efficiency factor and bias signal of fault actuator model. So as to achieve the integration smooth, the RL training laws of critic and actor networks are deduced in accordance with gradient descent method of a simple positive function that is equivalent to Hamilton-Jacobi-Bellman (HJB) equation, so that it can significantly reduce the complexity of RL algorithm. Finally, effectiveness of this attitude control scheme is demonstrated through Lyapunov stability proof and simulation example.

This paper presents a novel reinforcement learning-based framework for pitch control in wind turbines, addressing the challenges posed by high nonlinearity and uncertainties in wind dynamics. Traditional pitch control methods, such as proportional-integral controllers, often struggle with adaptability and load mitigation under fluctuating wind conditions. To enhance control efficiency, the proposed framework employs a two-stage approach: policy transfer and policy refinement. In the policy transfer stage, a proportional-integral controller is used to estimate an initial control policy. This policy is then refined using the double deep Q-Network algorithm. Furthermore, a novel reward function is introduced as an improved version of a recent formulation in the literature, ensuring better alignment with practical operational scenarios. Simulations demonstrate that the proposed controller outperforms the industry-standard ROSCO controller, achieving a 15.87% reduction in power fluctuations, while preserving comparable structural load levels, with slight reductions in some cases. These results highlight the potential of reinforcement learning-based control to enhance wind turbine power regulation without compromising structural safety in terms of loads and vibrations.

This paper investigates the bipartite cooperative output consensus (BCOC) problem of heterogeneous multi-agent systems with external disturbances using direct data-driven control. Unlike most existing bipartite consensus, the controller is directly designed using data collected within a finite time, which eliminates the requirement for an accurate system model. The leader's system matrix is estimated using sampling data generated by an auxiliary system. In addition, a distributed observer is constructed to handle the situation where some followers lack direct interaction with the leader. Moreover, the specific expression of the regulation equations for BCOC is derived. For the follower dynamics influenced by external disturbances and the leader, the criteria for the data to be informative for the stabilization of the error system and the data-driven solution of the regulation equation are established by employing the data informativity conditions and the relevant data. These results are further extended to the special case of bipartite consensus. Ultimately, a numerical simulation example is showcased to validate the effectiveness of the theoretical results.

This study proposes a robust fixed-time safety control scheme for a quadrotor unmanned aerial vehicle (QUAV) operating in environments with obstacles and external disturbances. Firstly, a robust high-order time-varying control barrier function (R-HO-TV-CBF) is designed as an obstacle avoidance constraint. This method achieves robustness without relying about any prior assumptions on the bounds of the disturbance and its derivative, effectively handles dynamic obstacle avoidance, suppresses oscillation and ensures optimal trajectory tracking. Subsequently, a nonsingular fixed-time sliding mode control (NFxSMC) method is developed to guarantee fixed-time convergence, independent of the initial state. Finally, simulation results demonstrate the superior performance of the proposed scheme in obstacle avoidance, disturbance rejection and error convergence.

This paper presents a co-design framework for an observer-based controller and a dynamic event-triggered mechanism (DETM) in networked control systems subject to hybrid cyber attacks. The hybrid attacks, including false data injection and replay attacks, are modeled using two independent Bernoulli processes. To reduce the network load a DETM is introduced between the sensor and controller with limited communication bandwidth. The closed-loop system's stability and H_∞ performance are ensured through the derivation of nonlinear matrix inequalities. To address the nonlinear coupling in the co-design conditions, a variable transformation and matrix decomposition approach are employed to decouple the controller gains. However, this method relies on predefined DETM parameters, which may limit design flexibility. To overcome this issue a GA-LMI-based co-design algorithm is proposed to jointly optimize the controller and DETM parameters. Simulation results demonstrate that the proposed method achieves better H_∞ performance compared to some existing approaches. The effectiveness and low conservatism of the method are further validated through its application to a buck DC-DC converter system under hybrid cyber attacks.

Distributed convex optimization with time-varying or time-invariant cost functions remains one of the central challenges in multi-agent systems (MASs). Achieving efficient distributed optimization within a fixed/predefined-time continues to face difficulties such as restrictive assumptions and high computational complexity. This paper proposes innovative distributed optimization frameworks to address the aforementioned limitations. For time-invariant optimization problems, an estimator-based two-stage distributed protocol is introduced, which achieves both inter-agent consensus and convergence to the global optimum within a fixed/predefined-time. Notably, this protocol only requires strong convexity of the global cost function, thereby relaxing the constraints on local functions. For time-varying scenarios, an enhanced zero-gradient-sum (ZGS) framework is developed by integrating a zeroing neural network (ZNN) with sliding mode control. This framework not only eliminates dependence on initial conditions but also implicitly computes the Hessian inverse through ZNN dynamics, effectively avoiding the O(n³) computational burden associated with explicit matrix inversion. Numerical simulations validate the superior convergence speed and broad applicability of our method, attesting to its great potential for distributed optimization.

For heterogeneous linear multi-agent systems (MASs) in which not all followers have access to the information of multiple leaders, this paper studies the distributed output formation-containment problem. By converting the research question into the cooperative output regulation problem, a fully distributed control framework via a fully adaptive observer approach is developed. Since a part of the followers cannot directly access the states of leaders, a new observer is designed for reconstructing the system matrices and state information. Specifically, by introducing adaptive gains in place of fixed coupling coefficients in the observer design, the global topological information is no longer needed. Moreover, the conventional regulator equations cannot be directly applied for controller synthesis due to the time-varying estimated system matrices of the leaders. To overcome this challenge, the adaptive regulator equations are developed to dynamically compute the controller gains. Subsequently, both state and output feedback control strategies are presented to achieve the desired output formation-containment control objective. The validation of the proposed method is performed in the simulation results with practical and comparative examples.

This paper is concerned with a prescribed-time observer (PTO) for a class of linear systems by using a novel dynamic event-triggered mechanism (DETM) with bounded triggering conditions. Traditional PTOs often require real-time updates of the measurement output signal, leading to high communication costs. Event-triggered approaches can address this issue. However, event-triggered approaches hardly achieve prescribed-time convergence. In order to achieve fast state estimation while maintaining low communication costs, a dynamic event-triggered prescribed-time observer (DETPTO) is developed by using a parametric Lyapunov equation (PLE), in which the estimation of all system states can also be achieved within a prescribed-time T. To achieve this goal, a DETM is proposed by integrating both estimation error and measurement error, ensuring that the observer updates the measurement output only when necessary rather than continuously. Moreover, it is proven that the proposed DETPTO can avoid the Zeno behavior. Finally, numerical simulations validate the effectiveness of the proposed methods.

This paper studies an adaptive bipartite fuzzy consensus problem with privacy preservation in stochastic nonlinear multi-agent systems (SNMASs) under Markovian switching topologies. To handle unknown nonlinearities and protect sensitive information, a novel observer-based control strategy is proposed, in which adaptive fuzzy logic systems (FLSs) are employed to approximate unknown nonlinear functions and a vanishing affine mask function is designed to ensure the privacy of the agents' initial states. A continuous-time Markov process governs stochastic topology changes, improving network robustness and adaptability. Theoretical analysis demonstrates that all signals of the closed-loop system are uniformly ultimately bounded in the mean-square sense, and practical bipartite consensus is achieved in the face of stochastic disturbances and nonlinearities. Notably, the proposed method is further extended to structurally unbalanced signed graphs by constructing a virtually balanced graph through pinning-type compensations, enabling the consensus protocol to operate directly on the original structurally unbalanced network while preserving convergence guarantees. Finally, the effectiveness of the proposed theoretical approach is validated through numerical simulations.

Under time-varying operating conditions, vibration signals from rotating machinery typically contain dense and non-proportional frequency components, which make it difficult for current time-frequency analysis (TFA) techniques to achieve high-precision and highly concentrated time-frequency representations (TFRs). Accordingly, the high-dimensional optimized extraction chirplet transform (HOECT) is proposed. In the HOECT, a window parameter adaptive optimization criterion is first designed to dynamically match local characteristics of the signal; second, the component matching CT (CMCT) is improved based on the proposed criterion, and its results are mapped into the time-frequency-chirprate domain to separate frequency components; finally, based on estimated local frequency and chirprate, the smoothing extraction operator (SEO) is constructed in the frequency-chirprate domain, which significantly enhances the energy concentration. In addition, a novel connected domain analysis (CDA) algorithm is proposed to effectively suppress strong noise interference and highlight key features of signal components. Simulation results illustrate that the HOECT can accurately characterize dense and non-proportional frequency components with high energy concentration. The superiority of the HOECT in operational state characterization and fault feature identification is further verified through two vibration signals of planetary gearboxes. Additionally, the whale sound signal results indicate that the proposed method exhibits strong robustness and applicability in analyzing complex non-stationary signals.