Search Results (1,225)

Distributed economic dispatch in AC distribution systems relies heavily on communication networks and is therefore vulnerable to denial-of-service (DoS) attacks. To address this issue, this paper proposes a resilient event-triggered distributed economic dispatch control strategy. Two typical DoS attack scenarios, namely communication-link blocking and node isolation, are first modeled, and an event-triggered distributed economic dispatch controller is then developed to maintain incremental cost consensus and system power balance while reducing communication overhead. Based on Lyapunov stability theory and a linear matrix inequality approach, sufficient conditions for the asymptotic stability of the closed-loop system are derived, tolerable bounds on the frequency and duration of DoS attacks are established, and the absence of Zeno behavior is proved. Simulations on the IEEE 33-bus AC distribution system show that, under load disturbances, dispatch-command variations, and DoS attacks, the proposed strategy can maintain stable system operation, restore dispatch performance after attacks, and reduce communication overhead by 91.86% compared with a fixed-step periodic updating baseline. These results demonstrate the effectiveness and resilience of the proposed method for distributed economic dispatch in AC distribution systems under DoS attacks. Full article

This paper is concerned with the problems of mean-square global dissipativity and global asymptotic stability for a class of stochastic fractional-order memristive BAM neural networks with leakage terms and mixed time-varying delays, including discrete delays and distributed delays. By using differential inclusion theory, stochastic analysis, matrix measure approach, and Lyapunov stability theory combined with linear matrix inequalities (LMIs), several new sufficient conditions are derived to ensure the mean-square global dissipativity and global asymptotic stability of the considered system. Compared with the existing results, the obtained stability and dissipativity criteria are less conservative due to the adoption of matrix measure and fractional-order differential inequalities. The proposed model simultaneously incorporates stochastic perturbations, memristive discontinuity, leakage effects, and mixed delays, which makes it more consistent with actual engineering scenarios such as pattern recognition and intelligent control. Finally, a numerical example is provided to demonstrate the effectiveness and correctness of the theoretical results. Full article

In the anti-sway control of variable-length double-pendulum gantry cranes, traditional sliding mode control relies on high switching gains, which can cause chattering. Additionally, the introduction of neural networks presents challenges in tuning high-dimensional parameters. To address these issues, this study proposes an adaptive hierarchical sliding mode control strategy based on an RBF neural network and particle swarm optimization. First, a low-energy-dissipation dynamic model is established without the small-angle assumption. Second, a composite hierarchical sliding surface is designed to achieve multi-objective decoupling, and an RBF neural network is utilized to approximate the system’s unknown dynamics online, thereby reducing switching gains and suppressing chattering. The asymptotic stability of the closed-loop system is proven based on Lyapunov theory. Finally, a particle swarm optimization algorithm is introduced to achieve automated, high-precision matching of high-dimensional controller parameters. Simulation results indicate that the control method designed in this paper can achieve automatic matching of high-dimensional parameters, effectively resolving the chattering issue in sliding mode control. Furthermore, under wide-range parameter perturbations and external multi-source disturbances, the controller exhibits strong robustness and demonstrates excellent positioning and anti-chattering performance. Full article

For nonlinear problems in robotic systems, such as parametric uncertainties and external disturbances, this paper proposes a control method based on bilateral dual-kernel fuzzy control. To address the issue that joint angular velocities cannot be directly measured, an extended state observer (ESO) is introduced to simultaneously estimate the joint positions, velocities, and system nonlinearities, thereby achieving effective reconstruction of the system states. In terms of controller design, a dual-kernel function is adopted instead of the conventional single-kernel function. By exploiting its enhanced feature representation capability and fast response characteristics, the proposed approach improves the system dynamic response speed and reduces the settling time. For nonlinear residuals, the bilateral parallel control strategy further improves the approximation accuracy of the control system. Multiple dual-kernel fuzzy sub-controllers are integrated in a bilateral parallel manner, and the weighting parameters of both the fuzzy system and the bilateral structure are updated in real time based on the approximation error. This enables accurate approximation and compensation of the residuals estimated by the extended state observer. The stability of the closed-loop system is rigorously proved based on Lyapunov theory. Finally, simulations on the MATLAB R2022b platform and experiments on a robotic experimental platform are conducted to verify that the proposed bilateral dual-kernel fuzzy controller achieves significantly improved control accuracy for a two-degree-of-freedom robotic manipulator system compared with conventional controllers, thereby demonstrating the effectiveness and superiority of the proposed algorithm. Full article

This paper proposes a parametrization transform method for model-unknown networked control systems by using a data-driven event-triggered scheme. The key contribution is that an easy-to-apply parametrization transform method is proposed to convert the model-based linear matrix inequality (LMI) conditions into data-driven ones. Compared with existing ones, using the proposed transform method is without requirements on the specified sizes, structures, and unknown system matrices’ positions of model-based LMI conditions. On this basis, by using Lyapunov theory and some inequality techniques, some data-driven condition are derived to guarantee stability. Without considering model dynamics, the controller gain and trigger parameters can be easily derived by learning from collecting offline data packets. Finally, an illustrative example is presented to showcase the outcomes. Full article

In this paper, we investigate the practical exponential stability of a class of nonlinear systems governed by the tempered $\varpi$-Caputo fractional derivative. A new Lyapunov-based criterion is established to derive sufficient conditions ensuring $\varpi$-practical exponential stability. The obtained result is formulated in a general framework involving suitable growth bounds on the Lyapunov function together with a tempered fractional derivative inequality and a boundedness condition on a weighted integral term. The proposed theorem provides an explicit practical exponential estimate for the system trajectories and extends existing stability results that are available for standard fractional and tempered fractional systems. To demonstrate the applicability of the developed theory, two applications are presented. First, the general criterion is applied to a class of perturbed tempered $\varpi$-fractional systems, for which verifiable sufficient conditions are derived in terms of quadratic Lyapunov functions and perturbation bounds. Second, a state-feedback stabilization result is established for a class of nonlinear tempered fractional control systems, showing that the proposed theorem can be used as an effective tool for closed-loop practical exponential stabilization. Finally, numerical examples are provided to validate the theoretical developments and to illustrate the effectiveness of the proposed approach. An additional test case with ${\eta }_3>0$ is included to demonstrate the nontrivial range of Theorem 1. Furthermore, a socio-economic tempered fractional cobweb model is incorporated to show how the proposed criterion applies to price-adjustment dynamics with memory and persistent market perturbations. Full article

This paper investigates the nonlinear dynamics of a decision delay duopoly model that characterizes the competitive landscape within China’s Low-Temperature Milk market. These dynamics include equilibrium points, period-doubling bifurcations, complex torus-like motions, and closed invariant curves induced by Neimark–Sacker bifurcation. Then the Neimark–Sacker bifurcation phenomenon is analyzed. Next, to further stabilize the nonlinear dynamics, an improved impulsive control strategy is introduced. Sufficient conditions for the asymptotic stability of the controlled system are derived using Lyapunov stability theory. Numerical simulations demonstrate that, under appropriate impulsive control, an originally divergent system can converge to equilibrium, effectively stabilizing market profits. This research provides a theoretical reference for oligopolistic firms to optimize their marketing rhythm and for policymakers to maintain market stability. Full article

This article addresses the challenge of positioning accuracy in robotic manipulators applied to automated tape placement (ATL). A hybrid control strategy is proposed that integrates a Proportional-Integral-Derivative (PID) controller with a Backpropagation Neural Network (BP-NN). The proposed approach, called PID + NN, acts as a robust control scheme designed to compensate for parametric uncertainties and unmodeled perturbations arising from the integration of high-inertia tools in the end effector, dynamic mass variation due to tape consumption, and external reaction forces during the compaction process. Within this framework, the PID controller manages the nominal dynamics of the system, while the neural network operates as an adaptive compensator that adjusts the control signal in real time to minimize trajectory tracking errors. A rigorous stability analysis based on Lyapunov theory is presented, and the results are validated through numerical simulations on a six-degree-of-freedom manipulator. In addition, experimental tests are performed in a real operating environment to verify the practical performance of the strategy. The experimental results indicate that the proposed PID + NN controller significantly improves trajectory tracking accuracy, achieving a substantial reduction in tracking error and smoother control torque profiles compared to the conventional PID controller. These findings validate the effectiveness and robustness of the method for advanced manufacturing applications that demand high precision. Full article

In this article, we use truncated M-fractional derivatives to analyze the bifurcation and chaotic behavior of and traveling-wave solutions to the Zhiber–Shabat equation. By introducing truncated M-fractional derivatives, the equation exhibits richer dynamic properties. Based on phase diagram analysis and dynamical system theory, the bifurcation behavior of the equilibrium point of a two-dimensional dynamical system is discussed. At the same time, the dynamical behavior of a two-dimensional dynamical system with periodic disturbances is considered, revealing the complex chaotic phenomena of the system under specific parameters. A planar phase diagram, a three-dimensional phase diagram, a sensitivity analysis, and a maximum Lyapunov exponent diagram of the perturbed two-dimensional dynamical system were employed. Furthermore, various forms of accurate analytical solutions were obtained through traveling-wave transformation and numerical simulation. The three-dimensional, two-dimensional, density, and polar coordinates of the solutions were plotted using mathematical software. The results indicate that the fractional order and system parameters have a significant impact on the morphology and chaotic characteristics of the solution. This study provides new theoretical insights into the nonlinear dynamics of fractional-order Zhiber–Shabat equations. Full article

Hydro-wind-solar hybrid power systems have become a mainstream configuration for new-type power systems. However, the high proportion of power-electronics-interfaced generation alters system inertia and damping characteristics, leading to complex high-dimensional frequency dynamics and severe stability challenges. This paper investigates the frequency response mechanism and Hopf bifurcation characteristics of the aggregated frequency response model for hydro-wind-solar hybrid power systems. First, primary frequency response models for hydropower, wind power, and photovoltaic (PV) generation are established under a small-signal analysis framework. On this basis, a tenth-order nonlinear dynamic model of the integrated system is constructed by considering hydraulic nonlinearities, virtual inertia control of wind power, and reserve-based frequency regulation of PV systems. Then, Hopf bifurcation theory is applied to analyze stability and oscillatory instability mechanisms of the high-dimensional system. The bifurcation conditions are derived via high-dimensional Jacobian matrix analysis and Routh-Hurwitz criterion, with supplementary normal form calculation and first Lyapunov coefficient derivation to identify the supercritical/subcritical nature of the bifurcation. Finally, numerical simulations under both small and large disturbances validate the theoretical analysis. The results demonstrate that variations in key control parameters may induce Hopf bifurcation, leading the high-dimensional system from a stable equilibrium to sustained low-frequency oscillations. The findings provide insights and practical guidance for stable operation and parameter tuning of hydro-wind-solar hybrid power systems. Full article

This article presents a modeling and cooperative kinematic control framework for heterogeneous mobile manipulators operating in a shared task space. The proposed approach integrates systems with different kinematic structures into a unified pose–shape representation, derived from individual and cooperative Jacobian models, enabling coordinated motion under a common formulation and extendable to multiple robots through a hierarchical architecture. The control strategy exploits system redundancy via null-space projection to incorporate secondary objectives without affecting the primary task. In particular, collision-free obstacle avoidance of the mobile bases and safe joint configuration of the robotic arms are achieved simultaneously while preserving formation tracking. The stability of the cooperative system is established using Lyapunov theory, ensuring asymptotic convergence of tracking errors. The proposed method is validated through numerical simulations in MATLAB under two representative scenarios, demonstrating its capability to handle heterogeneous configurations, maintain coordination, and execute safe and scalable cooperative behaviors. Full article

The control of nonlinear, open-loop unstable dynamics is a prevalent engineering challenge, often benchmarked through magnetic levitation (Maglev) systems. While continuous-time adaptive neural networks are commonly used to reject disturbances, their direct digital implementation often induces closed-loop instability due to unaccounted sampling effects. To address this, this paper proposes a discrete-time Fourier Series Neural Network (FSNN) control architecture for nonlinear Single-Input Single-Output (SISO) systems that can be transformed into the Brunovsky canonical form. The parameter adaptation laws are synthesized strictly in the discrete-time domain using Lyapunov stability theory. This approach yields an explicit upper bound for the digital sampling period, ensuring a proper implementation. Furthermore, it guarantees the Uniform Ultimate Boundedness (UUB) of the tracking error in the presence of bounded unmodeled dynamics and periodic disturbances. Numerical simulations of Maglev dynamics validate the theoretical bounds, demonstrating that the FSNN controller achieves rapid learning and generates a smooth control effort. Ultimately, by eliminating the instability risks of continuous-time approximations, this methodology bridges the gap between theoretical design and digital implementation, providing a practical framework for the robust control of electromagnetic actuators and other nonlinear industrial processes. Full article

This paper investigates the formation tracking problem for a group of autonomous underwater vehicles (AUVs) operating under quantized communication and actuation. A novel adaptive learning framework is proposed, capable of extracting cooperative control policies directly from quantized relative measurements and quantized input signals. Unlike conventional approaches that rely on continuous signal assumptions, the developed method enables each AUV to learn and adapt its behavior in real time from coarsely quantized data, thereby enhancing robustness in digital and bandwidth-limited environments. Within a backstepping control structure, an improved quantized consensus mechanism and a hysteresis quantizer compensation strategy are integrated to mitigate quantization effects. Using Lyapunov stability theory, it is proven that all closed-loop signals remain bounded and the formation tracking errors converge to an adjustable neighborhood of zero. Simulation results demonstrate that the proposed learning-based controller achieves accurate formation tracking and exhibits strong adaptability under dual quantization constraints. Full article

Tendon-driven manipulators possess obvious advantages compared to rigid-link manipulators, such as lighter weight, greater flexibility, and adaptability to confined spaces. To solve the problems of backlash and improve the accuracy of motion in specific application environments, this paper proposes a novel single-tendon-driven design for each joint of the manipulator. Kinematic modeling of the manipulator is systematically derived. Then, a decoupling algorithm is designed to mitigate motion coupling effects and enable accurate mapping between motor inputs and joint motions. Moreover, to improve the accuracy of trajectory tracking control for the tendon-driven manipulator, this paper proposes a nonsingular terminal sliding mode (NTSM) control scheme based on time-delay estimation (TDE). TDE is used to estimate unknown disturbances. An adjustable parameter was introduced based on TDE technology, which can enhance the system’s robustness against uncertainties and external disturbances. The stability of the closed-loop control system is verified through Lyapunov stability theory. Finally, decoupling experiments are conducted to validate the kinematic model and the feasibility of the proposed design. And comparative experiments are performed to prove the advantages of the proposed control scheme. Full article

With the gradual development of science and technology, increasingly complex application environments impose higher requirements on the control performance of quadrotor unmanned aerial vehicles (UAVs). This requires UAVs to achieve high-performance tracking control under various challenging conditions, such as model uncertainties, external disturbances, actuator saturation, and actuator faults. Considering these issues, this paper proposes a novel fixed-time controller. First, to address the external disturbances and model uncertainties that UAVs may encounter during flight, a non-singular fixed-time terminal sliding mode control method is proposed, and a variable exponential fixed-time adaptive sliding mode disturbance observer is introduced to improve the estimation accuracy of the lumped disturbances. Secondly, considering the impact of actuator input saturation, an auxiliary system is constructed to mitigate the actuator saturation problem. Finally, a fixed-time fault-tolerant control scheme with actuator saturation and prescribed performance constraints is investigated for quadrotor UAVs. The convergence performance of the controller is rigorously established based on Lyapunov stability theory. Comparative simulation results are provided to demonstrate the effectiveness of the proposed control strategy. Full article