Search Results (2,175)

With the rapid development of modern industry, robotic manipulators are required to achieve increasingly high trajectory-tracking accuracy and robustness in practical applications. To enhance tracking performance under complex operating conditions, this paper proposes an Adaptive Model Predictive Control with Sliding-Mode Robust Compensation (AMPC–SMC) scheme that integrates an adaptive mechanism with sliding-mode control theory. First, a dynamic model of the manipulator is established, and parameter linearization is employed to transform the nonlinear dynamics into a linearly parameterized form with unknown parameters. Second, an adaptive law is derived based on Lyapunov stability theory to update the model parameters online, thereby mitigating the adverse effects of parametric perturbations and external disturbances on tracking accuracy. Building on this, a receding-horizon optimization strategy is introduced by formulating a quadratic cost function that penalizes both tracking errors and control effort, and the optimal control input is obtained by solving the resulting optimization problem. Meanwhile, a sliding-mode term is incorporated as a robust compensator to eliminate residual tracking errors. Finally, the desired trajectory is generated via point-to-point path planning in Cartesian space, and the proposed method is validated on a real six-degree-of-freedom robotic manipulator. Comparative experiments against conventional model predictive control (MPC) and traditional sliding-mode control (SMC) demonstrate that the proposed AMPC-SMC controller achieves remarkably superior tracking performance compared with the conventional MPC and SMC controllers. In terms of tracking accuracy, the mean absolute errors (MAE) of Joint 2, Joint 4 and Joint 5 under AMPC-SMC are reduced by $91.7\%$, $92.1\%$ and $86.1\%$ respectively relative to MPC, and decreased by $76.1\%$, $77.3\%$ and $85.1\%$ compared with the standalone SMC controller. Full article

Distributed Denial-of-Service (DDoS) attacks are a major security threat to blockchain systems, especially in Internet-of-Things (IoT)-oriented and edge-assisted deployments where mining services, gateway nodes, and communication resources are more vulnerable to disruption. In Proof-of-Work (PoW) blockchain networks, such attacks can degrade mining pool connectivity, reduce effective revenue, and undermine both system security and resilient infrastructure design. Existing studies have mainly focused on DDoS detection or static mining games, while paying limited attention to dynamic strategy evolution and convergence under varying network conditions. To address this problem, this paper proposes a dynamic evolutionary game (DEG)-based revenue model for mining pools under DDoS attacks. Unlike static game methods that only identify equilibrium points, this work analyzes both the convergence dynamics and the sensitivity of equilibrium outcomes to key parameters, including attack scale, penalty, reward, and network quality. The proposed method characterizes the adaptive interaction between honest mining and attack behaviors through replicator dynamics, and analyzes strategy stability and convergence using equilibrium and Lyapunov-based methods. In addition, we investigate how key system parameters, including attack scale, reward, penalty, and network quality, affect the convergence speed of mining pool strategies in different environments. MATLAB results show that the proposed DEG model better captures the dynamic evolution of mining pool behaviors than conventional static-game formulations. The results further indicate that improved network conditions may unintentionally incentivize DDoS attacks, while proper parameter tuning can accelerate convergence toward security-favorable strategies. These findings provide useful insights for secure blockchain design and resilient mining infrastructure in IoT-oriented systems. Full article

This paper presents a self-tuning minimal-rule fuzzy logic controller for high-performance induction motor drives operating under field-oriented control. Unlike conventional full-rule fuzzy controllers and reduced-rule designs with fixed post-design scaling, the proposed method combines a fixed nine-rule Mamdani inference structure with a bounded online output gain adaptation mechanism. The nominal gain and adaptation sensitivity are determined offline using Particle Swarm Optimization, thereby retaining operating-condition responsiveness without requiring online optimization, rule reconstruction, or membership-function retuning. The closed-loop behavior is analyzed using a discrete-time Lyapunov framework derived from the induction motor mechanical dynamics under bounded disturbances. The controller is evaluated through fixed-step simulations incorporating measurement noise, 12-bit signal quantization, and a one-sample computational delay. Comparative results against a conventional PI controller and a classical 49-rule fuzzy controller show that the proposed scheme achieves a rise time of 0.15 s, a settling time of 0.26 s, a post-transient mean absolute tracking error of 4 RPM, negligible overshoot, and a torque ripple of approximately 0.44 Nm. Relative to the classical 49-rule FLC, the proposed design reduces the maximum number of fuzzy-rule evaluations per control update from 49 to 9, corresponding to an 81.6% reduction in structural fuzzy-inference complexity. The results indicate a favorable simulation-level trade-off between dynamic performance, disturbance rejection, and structural algorithmic simplicity. Generated-code SIL, Hardware-in-the-Loop testing, target-processor timing measurements, and experimental implementation remain necessary to establish practical embedded feasibility. Full article

With the widespread application of motor drive systems in fields such as industrial automation and new energy vehicles, the impact of their nonlinear dynamical behavior on control accuracy and stability has become increasingly significant. Chaos theory provides new insights for revealing and regulating complex nonlinear phenomena in motor systems. Based on chaos theory, this paper takes the synchronous reluctance motor as the research object and proposes, for the first time, a fractional-order mathematical model of the synchronous reluctance motor based on chaotic neurons. Then, the chaotic dynamical behaviors of the fractional-order mathematical model at orders of 0.99 and 0.97 were analyzed. Through bifurcation analysis, Lyapunov exponents, Poincare sections, and attraction domains reveal the mechanism of chaotic oscillation induced by external excitation current and multiple parameter modulation factors. Subsequently, a closed-loop control system based on the Zeroing Neural Network (ZNN) algorithm was designed, which effectively suppressed the chaotic behavior in the motor system’s mechanical rotor angular velocity, phase current, and rotor electrical angular velocity, thereby significantly enhancing the system’s stability. Finally, the effectiveness of the proposed method was validated through simulation experiments, providing theoretical support for the design of motor drive systems. Full article

In this work, we present a systematic framework to optimize robust 1-D chaotic maps, focusing on expanding the uninterrupted chaotic region and enhancing chaotic properties throughout the entire parameter space. To achieve these objectives, we propose three distinct techniques, each involving systematic manipulations of chaotic seed maps. These manipulations include shifts and operations such as multiplication and division, which result in significant improvements in their chaotic characteristics. The effectiveness of the proposed methods is demonstrated through a comprehensive analysis using bifurcation plots, the maximum Lyapunov exponent, the correlation coefficient, Shannon entropy, the average Lyapunov exponent, and the chaotic ratio. The results illustrate the attainment of an extensive and uninterrupted chaotic range, alongside enhanced chaotic behavior due to the application of shifted maps. Additionally, in this work we also investigate the impact of combining general shifted maps with halfway-shifted maps, showing that their product leads to further improvements in chaotic properties and their division widens the chaotic ratio. In the last proposed method, the combined product division (CPD) maps achieve the highest overall performance, attaining a maximum Lyapunov exponent of 1.3567 and an average Lyapunov exponent of 1.3498, while maintaining a perfect chaotic ratio (CR = 1) across the entire parameter space. To demonstrate hardware feasibility, some of the proposed maps were also implemented on an FPGA, and the results were compared with MATLAB R2022b simulations. The close match between the two validates the practicality of implementing these chaotic systems in hardware. The proposed techniques have potential applications in areas such as random number generation, chaos-based cryptography, and secure communication, among others. To prove this, the optimized maps are leveraged to design a chaos-based pseudo-random number generator (PRNG) that passes statistical tests including NIST SP 800-22 (all 15 sub-tests passed) and TestU01 (38/38 Rabbit, 17/17 Alphabit, 102/102 BlockAlphabit), validating cryptographic-grade randomness. Full article

This paper addresses the analysis and control of continuous-time linear switched systems within the linear time-varying (LTV) framework. A correspondence between switched and LTV system representations is first established, enabling the use of LTV tools for stability and controllability analysis under arbitrary switching sequences. Two state-feedback synthesis approaches are considered. The first is a switched LQR design formulated via Linear Matrix Inequalities using Multiple Lyapunov Functions. The second is a continuous-time LTV-LQR obtained by solving the differential Riccati equation. To mitigate the discontinuities associated with hard switching, a matrix smoothing strategy based on the partition of unity is introduced. This approach generates continuous approximations of the plant matrices utilized exclusively for control synthesis in the time-varying Riccati framework, while the actual plant retains its hard-switched dynamics. Numerical simulations for representative switched systems illustrate that the proposed smoothed LTV formulation leads to improved closed-loop behavior compared to the switched LQR baseline, including reduced control discontinuities and lower values of the quadratic performance index for the considered scenarios. The results indicate that the method is suitable for applications where abrupt control variations are undesirable. Full article

This paper presents a distributed closed-form control strategy for multi-unmanned aerial vehicle (multi-UAV) formation flight in cluttered environments. We propose a virtual-centroid-based architecture that reduces leader dependency while maintaining precise geometric configuration. A virtual-centroid navigation strategy generates reference trajectories for formation coordination. For collision avoidance, a generalized p-norm distance function accurately assesses collision risks for diverse obstacle geometries. An event-triggered mechanism incorporating velocity-dependent conditions produces amplitude-modulated, continuity-preserving avoidance vectors. A virtual navigation trajectory dynamically fuses avoidance information with reference tracking, enabling seamless coordination of tracking and collision avoidance. Closed-form controllers are derived for three-degree-of-freedom (3-DoF) dynamic formation control. Safety guarantees and asymptotic convergence in the nominal case are established via generalized Lyapunov analysis for non-smooth dynamical systems. Comparative simulations demonstrate that the proposed method achieves trajectory smoothness comparable to artificial potential field (APF). Full article

Diesel generator sets are key frequency-supporting units in islanded microgrids and shipboard power systems, where rapid speed recovery under abrupt load variations is essential for maintaining power quality. However, conventional linear active disturbance rejection control (LADRC) is limited by the disturbance-estimation and noise-amplification trade-off of a single observer, while fixed parameters restrict its adaptability under varying operating conditions. To address these limitations, this paper proposes an RBF neural-network-optimized cascaded LADRC method, termed RBF-CLADRC. A mechanism-based torque balance model is first established, with uncertain mechanical coupling, friction losses, and load variations lumped into the total disturbance. A residual-disturbance cascaded observer is then constructed, in which the first linear extended state observer estimates the total disturbance and the second further reconstructs the residual estimation error. Unlike conventional ML-based ADRC methods that directly tune multiple gains, the proposed RBFNN adjusts only a common controller bandwidth within a prescribed interval, while all observer and feedback gains are generated through predefined analytical relationships. This low-dimensional adaptation preserves coordinated gain variation, reduces online computational complexity, and facilitates real-time implementation. Lyapunov analysis shows that the observer and tracking errors are uniformly ultimately bounded under bounded disturbance rates and converge exponentially for constant disturbances. Finally, comparative simulations in MATLAB/Simulink demonstrate that the proposed method achieves better dynamic response and disturbance-rejection performance than conventional LADRC and other benchmark controllers. Full article

Bolstered by emerging sixth-generation (6G) communication technology, space–air–ground– sea integrated networks (SAGSINs) are reshaping edge computing through the synergistic use of space, aerial, terrestrial, and maritime platforms. However, in such highly dynamic and heterogeneous network environments, long-term energy-efficient computation offloading in drone-enabled SAGSINs has not yet been thoroughly explored, particularly when dynamic task demands from user equipment (UE) are served under the constraints of energy-limited drones. To fill this gap, the problem of service node association and computing-frequency allocation under dynamic computation offloading demands at the edge of the networks is studied in this paper. However, the related problem turns out to be a stochastic optimization problem. To this end, through in-depth mathematical analysis based on the Lyapunov optimization method, it is found that the multi-time-slot long-term optimization problem can be transformed into several single-time-slot optimization problems, which enables an efficient solution to the original problem. The single time-slot problem is solved using graph theory, convex optimization, and optimization theory, where Lyapunov optimization is utilized to achieve queue stability and energy efficiency. Simulation results demonstrate that the proposed strategy effectively reduces energy consumption, ensures low delay, and maintains long-term queue stability in drone-enabled SAGSINs under dynamic task demands from UE. Full article

In this paper, the collision-avoidance formation control problem for underactuated surface vessels (USVs) subject to input constraints is investigated. The input constraints include both input amplitude saturation and input rate saturation. A controller based on barrier Lyapunov functions (BLFs) is developed for the considered system. First, a disturbance observer is designed to compensate for environmental disturbances and model uncertainties that degrade system performance. Second, an auxiliary dynamic system is introduced to handle input amplitude saturation and input rate saturation. To guarantee connectivity preservation and collision avoidance within the formation, the distance errors and angle errors are transformed using BLFs. Based on the transformed errors and the disturbance observer, a BLF-based anti-saturation controller is then constructed. Lyapunov stability analysis proves that all signals in the closed-loop system are bounded. Finally, simulation results demonstrate that the proposed method can achieve collision-free formation control under both input amplitude saturation and input rate saturation, and verify that the control system achieves fast convergence, small tracking errors, and collision avoidance while satisfying the input magnitude and rate constraints. Full article

This paper investigates cooperative tracking control for virtually coupled multi-train systems subject to nonlinear running resistance, bounded time-varying resistance parameters, state constraints, and actuator saturation. The theoretical contribution is not the separate use of barrier Lyapunov functions, adaptive control, anti-windup compensation, or distributed cooperative control. Instead, the revised analysis establishes a coupled safety-and-boundedness certificate for the actual saturated closed-loop vector field. The closing-speed-aware spacing variable and actuator-authority condition support a first-exit proof of forward invariance, after which a composite Lyapunov analysis couples the saturation residual, anti-windup state, cooperative tracking error, and time-varying parameter-estimation error to establish uniform ultimate boundedness without persistent excitation. This proof architecture distinguishes the proposed controller from recent constrained train-control methods focused separately on velocity/input bounds, distance-oriented full-state barriers, or iteration-indexed learning. Numerical studies with heterogeneous trains, stronger time-varying aerodynamic perturbations, normalized actuator limits, tracking-bound verification, constrained baselines, a near-boundary safety-allocation case, and a quantitative one-factor-at-a-time parameter-sensitivity study are provided. Full article

Regression methods are widely used to investigate macroeconomic relationships; however, they are generally estimated without first examining whether the underlying variables exhibit fractal structures, persistence, and chaotic dynamics. Although nonlinear regression models relax the assumption of linearity, they rarely account for the complex geometric, long-memory, and dynamical properties that characterize macroeconomic time series. Motivated by this limitation, this study proposes a fractal-oriented Lie regression framework that integrates fractional persistence and Lie algebra to model nonlinear macroeconomic interactions within a unified analytical structure. For Türkiye, the empirical analysis employs monthly data on inflation, interest rates, exchange rates and oil prices covering the period 2000M1–2026M1, encompassing major economic crises and structural breaks. Prior to model estimation, the dynamical characteristics of the variables are examined using entropy measures, long-range dependency analysis, Lyapunov exponents and attractors. The results reveal persistent fractal structures, significant fractional dependence and chaotic behavior, indicating that macroeconomic variables evolve within a complex nonlinear dynamical system rather than around a conventional equilibrium. Based on these results, the variables are represented within a Lie algebra framework in which nonlinear transformation matrices preserve the underlying geometric structure while simultaneously capturing both self-dynamics and cross-variable interactions. The proposed Lie regression model demonstrates substantial improvements over standard regression methods in both model adequacy and forecasting performance. Oil prices emerge as the dominant transmitter of shocks by generating pronounced asymmetric effects on inflation, exchange rates and overall macroeconomic stability. The model achieves remarkable forecasting accuracy by reducing RMSE, MAE, and MAPE from 18.58, 13.61, and 69.92 under a standard regression model to 0.27, 0.22 and 16.4, respectively. Finally, the estimated Lie transformation matrix is employed as a policy-simulation mechanism to evaluate the transmission of alternative oil-price shocks. Scenarios based on 5%, 10%, and 20% increases in oil prices quantify the resulting adjustments in inflation, interest rates, and exchange rates by providing forward-looking assessments of macroeconomic vulnerability. The proposed framework extends standard regression analysis by explicitly incorporating fractional persistence and chaotic dynamics into a Lie algebra representation, thereby offering a more accurate and theoretically consistent approach for modeling complex macroeconomic systems. Full article

This work deals with the boundary stabilization problem of a flexible beam system subject to actuator saturation and external disturbances. Firstly, for the case of actuator input saturation, by introducing a dead-zone function and constructing an auxiliary system, the influence of saturation nonlinearity is compensated. Secondly, a boundary saturated controller is proposed. Meanwhile, for an actuator-saturated beam system subject to unknown bounded disturbances, the control objective is to attenuate their effects. Then, a sliding-mode saturated controller is constructed based on the boundary saturated control strategy. The uniform bounded stability of the system is proven in the sense of Lyapunov. The simulation results verify the effectiveness of the proposed methods. Full article

In this paper, we investigate a one-dimensional tree-structured Timoshenko beam system composed of three beams with boundary feedback controls and derive its explicit exponential decay rate. We first formulate a dynamic model incorporating end loads, boundary feedback, and multi-beam coupling relationships. Then, by applying Lyapunov’s method, we construct an energy functional suitable for this coupled system. This study not only provides rigorous proof of the exponential stability of the coupled Timoshenko beam system, but also yields an explicit decay rate under prescribed conditions. Our results offer theoretical support and a quantitative foundation for vibration suppression of multi-beam-coupled flexible structures. Full article

This paper addresses the finite-time sliding mode control problem for fractional Brownian motion (fBm)-driven Itô stochastic systems with actuator efficiency degradation, unknown matched nonlinearities, and partially unknown transition probabilities. A projection-based adaptive sliding mode observer is proposed to estimate unmeasurable states and unknown actuator fault factors. A non-switching sliding surface is constructed to avoid chattering caused by mode switching. Using a composite Lyapunov function and the fractional Itô formula, linear matrix inequality conditions are derived to ensure stochastic stability. A finite-time sliding mode controller is designed that drives the system onto the sliding surface within a prescribed time, with an explicit upper bound on the reaching time. Numerical simulations verify that the proposed method achieves finite-time convergence and yields bounded estimation errors, which demonstrates its effectiveness and robustness. Full article