The servo drive system serves as the core power unit in high-end equipment such as industrial robots and computerized numerical control (CNC) machine tools, where mechanical resonance and shaft torque ripple induced by elastic deformation and backlash severely degrade motion accuracy and system stability. Conventional resonance suppression approaches, predominantly based on PI control and notch-filter-augmented PI control, suffer from critical limitations: high sensitivity to resonant frequency variations, inability to systematically enforce physical shaft torque constraints, poor robustness against parameter uncertainties and external disturbances, and significant degradation of dynamic performance when resonance is aggressively suppressed. This paper establishes a two-inertia elastic system model to investigate the effects of elastic deformation and backlash nonlinearities, revealing the mechanisms of mechanical resonance and torque ripple, and proposes control strategies for resonance suppression and shaft torque ripple limitation. A novel hierarchical control architecture is designed, consisting of a Luenberger-observer-based model predictive control (MPC) speed controller, and a super-twisting sliding mode controller (ST-SMC) for the current loop. Luenberger observer-based MPC with ST-SMC strategy is to simultaneously obtain: (a) enhanced robustness via state estimation, (b) superior dynamic performance via SMC, and (c) guaranteed shaft torque constraint satisfaction via MPC. Compared with conventional PI control and notch-filter-based PI control, simulation results demonstrate that Luenberger observer-based MPC with ST-SMC strategy effectively suppresses resonance, limits shaft torque ripple, and enhances the system’s disturbance rejection capability.

Electrostatic energy harvesters (EEHs), especially those employing electroactive polymers, show significant potential to be scaled up into real‐world technologies for ambient energy harvesting. Conventional EEH cycles involve rapidly charging and discharging a variable capacitor at specific instants corresponding to maximum and minimum capacitance values. This approach poses two major challenges in practical operation: 1) the precise detection of these extrema is difficult under stochastic inputs and 2) instantaneous charging demands high peak currents and power, creating significant cost and complexity burdens for large‐scale applications. This work proposes control strategies that enable charging and discharging under stochastic excitations while also evaluating the impact of limiting the peak current on the maximum convertible energy. Traditional peak‐triggered controls are compared with newly proposed smooth current control methods that use continuous AC‐like driving voltages, whose phase is bound to the capacitance. Modeling and results on elastomeric generators show that smooth voltage controls can be implemented in a real‐time prediction‐free fashion, while competing in performance with peak‐triggered controls even under realistic stochastic ambient energy harvesting conditions.

The polymer electrolyte membrane fuel cells (PEMFCs) systems with unmeasurable variables, output constraints, time-varying disturbances, and uncertain dynamics can hardly be modelled well, such that model-based control strategies become infeasible. Therefore, an ultra-local model (ULM) only utilizing input and output signals of the PEMFC system is innovatively presented. Together with a fixed-time disturbance observer (FxTDO), fixed-time prescribed performance control, and an adaptive technique-based compensator, a novel internal state estimator-based model-free adaptive fixed-time prescribed performance control (MF-AFxTPPC) is naturally developed to estimate and regulate the oxygen excess ratio (OER). Main advantages are presented as follows: (1) The proposed internal state estimator is designed to estimate OER via FxTDO, which is known for its fixed-time convergence and low computational time. (2) The presented ULM algorithm not only reduces the complexity of controller design but also significantly enhances its flexibility and adaptability. (3) The FxTPPC scheme constrains the tracking error and stabilizes the closed-loop system, which is more favorable to practical implementations. (4) The adaptive technique-based compensator can not only compensate for estimation error and mitigate the input chattering but also guarantee the global robustness. Furthermore, the Lyapunov theorem is utilized to analyze the stability of the designed MF-AFxTPPC scheme. Finally, the numerical simulations on a nonlinear PEMFC model with the proposed controller strategy are achieved in a MATLAB/SIMULINK environment, and findings are given to exhibit the robustness and superiority of the presented method.

Based on equivalent consumption minimization strategy (ECMS), the approaches by genetic algorithm (GA), learning vector quantization neural networks (LVQ NNs) and fuzzy logic algorithm (FLA) are integrated to adjust/tune the power split ratio between internal combustion engines (ICE) and belt-driven starter generators (BSG). The proposed bi-object equivalent consumption minimization strategy (BOECMS) possesses three key features: being real-time, causal and capable of fulfilling two objects, namely, (i) minimizing fuel consumption, and (ii) ensuring a stable battery state of charge (SOC) within a relatively narrow range. A hybrid electric vehicle (HEV) model and its corresponding power split strategy are developed and verified by using the vehicle simulator ADVISOR (advanced vehicle simulator) and Simulink at the design stage. For practicality, the proposed control strategy, BOECMS, is converted into C code and then written into the embedded micro-processor to conduct the necessary hardware-in-the-loop (HIL) experiments at the verification stage. According to computer simulation results, fuel economy improved by 40.39 % over pure ICE vehicles for the “MANHATTAN” drive cycle. In addition, the SOC can be retained within a relatively narrow range: [0.4, 0.6]. Finally and significantly, the experimental results by HIL converge well with computer simulation results using Simulink, implying BOECMS can potentially be applied to the real-world driving in the future.

Abstract This paper proposes an observer‐based dynamic event‐triggered control strategy for a class of continuous networked nonlinear systems with actuator saturation and periodic denial‐of‐service (DoS) attacks. Initially, by segmenting DoS attacks into active and dormant periods, an attack‐correlated observer is proposed. Then, the system is modeled as a switched system. To further reduce the amount of communication and improve the robustness of the system, the dynamic event‐triggered mechanism (ETM) is designed. Next, the conditions for local exponential stability are derived by using a segmented Lyapunov functional. Finally, the codesign parameters for the state observer, controller, and dynamic ETM are determined by solving LMIs. The effectiveness of the proposed controller strategy is validated through simulation. These results show that system states can converge to the origin with overshoot and undershoot kept under 2%, while the dynamic ETM optimizes the use of limited connectivity resources.

Measles, a highly contagious viral infection, remains a significant public health challenge despite the availability of an effective vaccine. This cross-sectional study, conducted at City Hospital in Jalalabad, Kyrgyzstan, from December 10, 2023, to March 30, 2024, investigated a measles resurgence among 50 hospitalized children aged 1 month to 14 years. The study aimed to identify risk factors, evaluate vaccine effectiveness, assess complications, and propose control strategies. Findings revealed that 60% of cases were infants under 2 years, with 86% unvaccinated due to religious beliefs and misinformation. Malnutrition was prevalent, with 56% of children underweight and 60% stunted. Pneumonia (80%), anemia (34%), and parasitic infections (18%) emerged as significant co-morbidities. Transmission was associated with low herd immunity and direct contact (40%). These results underscore the urgent need for enhanced vaccination campaigns, nutritional interventions, and community education to reduce measles morbidity in Jalalabad.

Purpose Propose guidance and control schemes for the autonomous docking of quadrotors. Design/methodology/approach Theoretical and simulation (matlab) studies. Findings A guidance and control scheme for the autonomous docking of quadrotors has been developed. The guidance scheme proposed for the approach phase adopts artificial potential function method to achieve both the translational and rotational requirements simultaneously. A nonlinear port dynamics controller on SE(3), which can control the port position precisely, meets the docking phase requirements. Research limitations/implications Practical constraints on quadrotor flight and docking, such as the influence of disturbances, actuator limitations, and the presence of reaction forces between ports are not considered. Practical implications Docking, a relatively new concept for quadrotors, can be used for various applications such as powering a quadrotor while in flight, assembling multiple quadrotors to increase payload carrying capacity, and aerial robotics. Co-operative aerial manipulation and transportation using quadrotors can be achieved once establishing a physical connection between quadrotors through docking. Originality/value The concept of autonomous docking of multiple quadrotors, where both the quadrotors cooperatively move toward each other and dock has not been studied as per the best of authors' knowledge. Also, geometric nonlinear port dynamics controller on SE(3) has not been developed/studied.

System stability control in resource allocation is a critical issue in group robot systems. Against this backdrop, this study investigates the nonlinear dynamics and chaotic phenomena that arise during pricing games among finitely rational group robots and proposes control strategies to mitigate chaotic behaviors. A system model and a business model for group robots are developed based on market mechanism mapping, and the dynamics of resource allocation are formulated as a second-order discrete nonlinear system using game theory. Numerical simulations reveal that small perturbations in system parameters, such as pricing adjustment speed, product demand coefficients, and resource substitution coefficients, can induce chaotic behaviors. To address these chaotic phenomena, a control method combining state feedback and parameter adjustment is proposed. This approach dynamically tunes the state feedback intensity of the system via a control parameter M, thereby delaying bifurcations and suppressing chaotic behaviors. It ensures that the distribution of system eigenvalues satisfies stability conditions, allowing control over unstable periodic orbits and period-doubling bifurcations. Simulation results demonstrate that the proposed control method effectively delays period-doubling bifurcations and stabilizes unstable periodic orbits in chaotic attractors. The stability of the system’s Nash equilibrium is significantly improved, and the parameter range for equilibrium pricing is expanded. These findings provide essential theoretical foundations and practical guidance for the design and application of group robot systems.

This article addresses the distributed formation control issue of cooperative unmanned surface vessels (USVs) under interleaved periodic event-triggered communications. First, an adaptive event-based control protocol is designed, where the event-based neural network (NN) scheme is developed to compensate for uncertain model dynamics. Upon the designed control protocol, an interleaved periodic event-triggered mechanism (IPETM) is subsequently proposed to achieve the communication objective. Unlike the common continuous event-triggered methods and periodic event-triggered methods, in which multiple nodes are allowed to trigger their events at the same time, the proposed IPETM ensures that USVs detect their events at different times to avoid the simultaneous event triggering of different nodes. By this virtue, traffic jamming in common wireless environments can be prevented, such that potential communication delays and faults are naturally avoided. In addition, the event detecting instants of the presented IPETM are also discrete and periodic, such that it can be performed under low-computational frequencies. Through Lyapunov-based analysis, it is verified that all closed-loop signals can converge to an arbitrary small compact set with exponential convergence rates. Simulation results demonstrate the effectiveness and superiority of the proposed control scheme.

In this study, a shape memory alloy (SMA) actuating system is constructed to achieve flexible actuation ability. To improve the actuating performance, an effective control scheme needs to be developed to address the negative effects caused by the nonlinear nature of the internal SMA material and accommodate complex operating conditions. A modified generalised Prandtl-Ishlinskii (MGPI) hysteresis model is proposed to accurately characterise the strong saturated asymmetric hysteresis nonlinearity in the SMA wires. The modification of the input shape function allows for greater flexibility in controller design. Using this hysteresis modelling approach, an adaptive neural network (NN) control with a command filter is designed to achieve superior control performance. Experimental results validate the effectiveness of the proposed controller strategy.

In this article, the adaptive neural control is studied for multiple-input-multiple-output (MIMO) nonlinear systems with asymmetric input saturation, dead zone, and full state-function constraints. A suitable transformation is introduced to overcome the dead zone and saturation nonlinearity, and radial basis function (RBF) neural networks (NNs) are used to approximate the unknown nonlinear functions. What is more, we apply the Nussbaum function and time-varying barrier Lyapunov function (BLF) to deal with the unknown control gains and full state-function constraints, respectively. Based on the backstepping method, a universal adaptive neural control scheme is presented such that not only the state-function constraints of the closed-loop system cannot be violated and all signals of the closed-loop systems are bounded, but also the tracking error converges to a small neighborhood containing the origin. The effectiveness of the proposed control scheme is verified by an application to the mass-spring-damper system and a numerical example.

The [formula omitted]-Hilfer fractional order model for the optimal control of the dynamics of Hepatitis B virus transmission

Background: The recent trend in the all-electric ship (AES) electrical systems, especially in military vessels, is to move towards medium voltage direct current (MVDC) distribution. Bus voltage instability is a major issue in direct current (DC) distribution systems. Nowadays, direct current electric springs (DCES) are extensively used in low-voltage direct current (LVDC) microgrids to address voltage instability issues. This paper extends the use of a shunt DCES to stabilize the bus voltage in an MVDC grid. The work proposes an addition to the MVDC onboard ship distribution system architecture, described in IEEE 1709, by integrating a shunt DCES with a novel control strategy to stabilize the bus voltage under various loading conditions, including propulsion motor (PM) and online pulsed power load (PPL). Methods: The shunt DCES is designed to provide current into the MVDC bus, which reduces the bus current ripple to attain a stable bus voltage with reduced ripple. A dual loop control with a battery management system (BMS) is proposed for the shunt DCES and simulated in MATLAB/Simulink. BMS is designed based on the state of charge (SOC) of the battery and bus current ripple extracted from the system's source and load side currents. The current supplied by the shunt DCES and the extracted ripple current validate the effectiveness of the proposed control. Total harmonic distortions (THDs) as a measure of voltage ripple of the MVDC bus voltage at different intervals are measured and compared for both systems, with and without shunt DCES. Results: It was observed that the shunt DCES could reduce the voltage ripple well below the permissible limit, which is 5% as per IEEE 1709. Conclusion: The proposed control strategy could attain a reduction of 68-85% in THD under peak to off-peak loading conditions with the addition of shunt DCES.

Abstract As the DC transmission system of modular multilevel converters for offshore wind power suffers problems like large volume, heavy weight, difficulty in platform transportation, and high cost, a DC transmission scheme for offshore wind power based on diode rectifier units and modular multilevel converters is put forth in the present work; Meanwhile, the features of active-power coordination, the DC harmonic current, and the AC harmonic current of the hybrid converter in this proposed scheme are analyzed to propose control strategies for active power coordination control, DC harmonic current suppression, and AC harmonic current suppression of the hybrid converter, respectively. The performance and feasibility of the proposed strategies are verified by testing the hardware in the loop real-time simulation system for transmission of offshore wind power through hybrid converters based on RTDS. It is found that the proposed strategies could improve the stability, flexibility, and reliability of the system.

Modeling and control strategy of small unmanned helicopter rotation based on deep learning

Multiple batteries in uninterruptible power supply (UPS)-microgrid systems based on multiagents composed of multiple electric vehicles (EVs) can encounter state of charge (SoC) consistency problems. To solve this differential expansion and controller saturation problem, an adaptive command filter sliding-mode control strategy based on virtual synchronous generators (VSGs) and considering the power allocation principle is proposed. First, based on directed graph theory, an SoC consistency algorithm and power allocation strategy for multiple EVs were proposed, forming a dc power system with a fixed communication topology. Second, the rotor motion equation of synchronous generator (SG) is introduced into the inverter control algorithm to form the mathematical model of VSG. Third, a low-pass filter (LFP) was introduced in the voltage control process to simulate the excitation attenuation characteristics of the SG. Based on the above, a backstepping control strategy, including a command filter and sliding mode controller is proposed, which improves the operating stability of the system based on the system errors of angle, frequency, and power output. Finally, the UPS-microgrid system based on multiagents is simulated to demonstrate the stability of the system and the effectiveness of the proposed control strategy.

Under the condition of grid voltage imbalance, the circulation of the bridge arm inside the modular multilevel converter (MMC) increases significantly, which leads to the aggravation of the distortion of the bridge arm current, and, thus, increases the system loss and reduces the power quality. To address this problem, this paper analyzes the mechanism of circulating current generation and proposes a circulating current suppression strategy based on a reduced-order generalized integrator (ROGI), which firstly uses the ROGI system to separate the second-harmonic positive- and negative-sequence components in the circulating current from the DC, and then converts the rotating coordinates of the circulating current’s second octave component into the DC to be fed into the proportional–integral quasi-resonance (PIR) controller for suppression. A simulation model of a 23-level MMC inverter is built in MATLAB/Simulink, and the control strategy proposed in this paper is compared with the classical proportional–integral (PI) control in simulation experiments. The simulation results show that the amplitude of the circulating current fluctuation of the classical PI control is reduced from 90 A to 22 A, and the harmonic distortion rate of the bridge arm current is reduced from 32.56% to 5.57%; the amplitude of the circulating current fluctuation of the control strategy proposed in this paper is reduced from 90 A to 5.7 A, and the harmonic distortion rate of the bridge arm current is reduced from 20.2% to 1.13%, which verifies the effectiveness of the pro-posed control strategy.

In this article, the human-in-the-loop leader-follower consensus control problem is addressed for multiagent systems (MASs) with unknown external disturbances. A human operator is deployed to monitor the MASs' team by transmitting an execution signal to a nonautonomous leader in response to any hazard detected, with the control input of the leader unknown to all followers. For each follower, a full-order observer, in which the observer error dynamic system decouples the unknown disturbance input, is designed for asymptotic state estimation. Then, an interval observer is constructed for the consensus error dynamic system, where the unknown disturbances and control inputs of its neighbors and its disturbance are treated as unknown inputs (UIs). To process the UIs, a new asymptotic algebraic UI reconstruction (UIR) scheme is proposed based on the interval observer, and one of the significant features of the UIR is the capacity to decouple the control input of the follower. The subsequent human-in-the-loop asymptotic convergence consensus protocol is developed by applying an observer-based distributed control strategy. Finally, the proposed control scheme is validated through two simulation examples.

This article studies the distributed adaptive event-triggered consensus control problem of linear multiagent systems. A strong non-Zeno mixed adaptive dynamic event-triggering scheme is proposed, which guarantees a strictly positive minimum interevent time (MIET) between any two consecutive events. A model-based event-triggered fully distributed adaptive control law is presented without using prior global information about the communication topology. Moreover, a hybrid system model is constructed to facilitate the stability analysis of the closed-loop system. It is shown that the proposed control strategy can achieve asymptotic consensus of all agents via intermittent communication in a fully distributed way, while guaranteeing the strictly positive MIET property. Finally, the effectiveness of the designed control method is illustrated by a simulation example.

Electric-powered wheelchairs play a vital role in ensuring accessibility for individuals with mobility impairments. The design of controllers for tracking tasks must prioritize the safety of wheelchair operation across various scenarios and for a diverse range of users. In this study, we propose a safety-oriented speed tracking control algorithm for wheelchair systems that accounts for external disturbances and uncertain parameters at the dynamic level. We employ a set-membership approach to estimate uncertain parameters online in deterministic sets. Additionally, we present a model predictive control scheme with real-time adaptation of the system model and controller parameters to ensure safety-related constraint satisfaction during the tracking process. This proposed controller effectively guides the wheelchair speed toward the desired reference while maintaining safety constraints. In cases where the reference is inadmissible and violates constraints, the controller can navigate the system to the vicinity of the nearest admissible reference. The efficiency of the proposed control scheme is demonstrated through high-fidelity speed tracking results from two tasks involving both admissible and inadmissible references.