Precipitation nowcasting is critical for meteorological disaster warning, water resource management, and severe rainfall prediction, directly impacting public safety and daily operations. Existing methods based on discriminative modeling tend to produce ambiguous extrapolation maps. While generative models have improved perceptual metrics, they still suffer from limited prediction accuracy (e.g., skills scores such as Critical Success Index (CSI) and Fractions Skill Score (FSS)), high computational costs, and inadequate convective initiation prediction-with the latter stemming from constraints of single-source data. To address these challenges, we propose CastDiffuser, a cascaded latent diffusion framework for high-resolution and high-precision precipitation nowcasting. The framework performs cascaded modeling in the latent space and leverages complementary satellite and radar data, enabling both enhanced predictive accuracy and reduced computational cost. Specifically, CastDiffuser downscales and reconstructs high-resolution radar by variational autocoders. Then we utilize a spatio-temporal translator (ST-Translator) to model the deterministic components of precipitation evolution. Subsequently, a satellite-guided diffusion model is introduced to refine these deterministic features, which are then used to generate high-resolution radar predictions. Experiments on Jiangsu provincial meteorological datasets show CastDiffuser outperforms state-of-the-art methods in prediction accuracy and fine-detail preservation, particularly for heavy rainfall and convective initiation events. • We propose CastDiffuser, a Cascaded Latent Diffusion Framework thatintegrates radar and satellite data in latent space for precipitation forecasting.By modeling in a low-dimensional latent space with a cascaded design, CastDiffuser achieves superior spatio-temporal prediction accuracy while significantly reducing the computational cost of high-resolution forecasting. • We design the Spatio-Temporal Translator composed of hierarchical ST-Inception modules for robust multi-scale feature extraction. This module provides stable and structured representations for subsequent diffusion learning, thereby addressing the issue of unstable input features and improving forecast accuracy. • We introduce FsrFormer, a multi-source fusion denoising network acting as a spatial refinement module. FsrFormer adaptively modulates the influence of satellite-derived features during the diffusion process across different time steps, facilitating efficient and dynamic fusion of multi-source information. • Experimental results on real-world datasets show that the proposed CastDiffuser significantly improves the prediction performance of heavy precipitation and maintains this accuracy over a longer forecast period, especially in the convective incipient prediction task.

In this paper, a novel deep reinforcement learning based navigation policy for mobile robot is proposed to address the challenges in multi-dynamic obstacles environments. Firstly, this method designs a risk perception function to evaluate the collision probability (CP) between robot and dynamic obstacles. Then, the observation space with risk perception is designed for the robot with a sense of the danger level between dynamic obstacles. In order to guide the robot to actively avoid such high-risk obstacles, the velocity obstacles(VO)-based reward is used to find desired direction angle only considering these critical obstacles. In addition, a novel safety constraint is formulated based on control barrier function (CBF) theory, it could adaptively adjust the safety distance according to the risk level of dynamic obstacles. The policy is then optimized within a CBF-guided training framework to enhance safety and adaptability in multi-dynamic obstacles environments. A series of simulation and real-world experiments demonstrate that the proposed policy achieves superior performance in both safety and efficiency compared with state-of-the-art methods, and it is well-suited for safety-critical navigation tasks in dense, dynamic environments such as indoor service and warehouse logistics scenarios.

This article introduces a novel framework for achieving leader-steered (L-S) rigid formations within a multirobot vehicle system subject to nonholonomic constraints, while considering field-of-view (FOV) constraints. In contrast to the conventional separation-bearing leader-follower model, this framework incorporates a virtual leader model, established through topological and local agent connections. To achieve L-S rigid formations and address FOV constraints, a transformative approach is employed. In addition to forming L-S rigid formations, the framework ensures visibility maintenance between topologically connected vehicles using onboard cameras. This is achieved through the introduction of a continuous and continuously differentiable switching function, crucial in balancing visibility maintenance with formation adjustments, particularly when the global leader traverses trajectory segments with large curvature. To implement the framework, the distributed control protocol and the distributed observer are developed. Numerical simulations and real-world experiments demonstrate the framework's capability to achieve L-S rigid formations while accommodating FOV constraints, showcasing its practical utility and effectiveness in real-world applications.

Task scheduling in heterogeneous distributed systems is critical for industrial platforms, where decisions must be made under strict time constraints while resource states evolve dynamically. Existing approaches face significant limitations: classical heuristics yield suboptimal solutions; metaheuristics scale poorly; learning-based methods require extensive training with limited generalization. This article proposes a transformer-initialized dual-population evolution (TIDE), integrating three innovations: first, enhanced graph coloring preprocessing for enriched task representation, second, Transformer-based cross-modal attention for intelligent initialization of feasible solutions without offline pretraining, supported by an online adaptation mechanism, and third, asymmetric dual-population cooperative optimization with adaptive dimensionality reduction. Comprehensive experiments demonstrate that TIDE consistently outperforms state-of-the-art metaheuristics by 8%–13% in makespan while achieving an 80%–85% reduction in algorithm computing time compared to the metaheuristic average. On real scientific workflows, TIDE improves resource utilization by 4%–6% and maintains load balance above 94%, while maintaining response times within industrial deadlines. These results establish TIDE as a scalable solution for real-time scheduling in large-scale industrial systems.

This paper introduces a novel framework for reactive control in dual-arm cooperative robotic systems, addressing the significant challenges posed by high-dimensional, non-convex optimization demands, intricate kinematic, multi-modal distribution, the need for precise, and synchronized coordination. The core of our approach is a two-stage sampling-based model predictive control, which integrates k-means, dual quaternion, and null space into a cohesive system. This integration enhances the system's ability to manage complex coordination tasks, such as obstacle avoidance and holding a water cup, while mitigating risks associated with local optima and reducing control jitter. Our framework not only improves performance and reliability, but also overcomes the traditional computational bottlenecks inherent in dual-arm coordination. These advancements are validated through extensive simulations and experiments, demonstrating the robustness and efficiency of our proposed methodology.

This article considers the problem of output feedback-based decentralized periodic event-triggered control (PETC) for a class of large-scale systems with unknown nonlinear interconnections and measurement delays. When only the delayed sampled-data measurement output is accessible, a novel decentralized high-gain observer is first proposed based on an output predictor for each subsystem. Then, a set of decentralized sampled-data output feedback controllers that are driven by asynchronous periodic event-triggering conditions is developed to globally exponentially stabilize the large-scale systems. With the help of small-gain arguments and feedback domination approach, a rigorous stability analysis shows that there exist some sufficient conditions to ensure the global exponential stability of the overall systems. Different from the sample-and-hold implementation of output information, this article employs the prediction technique to obtain the current output prediction for each subsystem, which in turn effectively compensates for the undesirable effects of measurement delays and information loss. Finally, simulation results are presented to demonstrate the effectiveness of the proposed control method.

Model Predictive Path Integral (MPPI) controllers are drawing increasing attention for their ability to efficiently handle complex systems by leveraging GPU acceleration while with flexible prediction models and cost functions. However, their performance generally degrades with low-quality prediction models and unknown external disturbances. Existing methods that rely solely on feedforward disturbance compensation are limited by the assumption of matched disturbances, which rarely holds in practice due to the complex lumped disturbances. To this end, we propose a novel Disturbance-Aware (DA-) MPPI framework, which seamlessly integrates an Extended high-order Sliding Mode Observer (ESMO) into MPPI. The ESMO provides accurate estimates of uncertainties and external disturbances, which are directly incorporated into the MPPI rolling dynamics to improve prediction and therefore tracking control performance. The proposed algorithm is verified against the baseline MPPI in AirSim simulation environment by stochastic simulation. Comparatively statistical experiments show that incorporating ESMO within the MPPI framework significantly enhances tracking performance, with the RMSE reduction in term of mean by 8.0%, 17.7%, 6.17%, 12.9% and in term of standard variance by 11.5%, 26.0%, 10.4%, and 9.2% in four representative scenarios. The effects of target velocity and prediction horizon on control performance are also systematically evaluated. These results validate the robustness and accuracy of the DA-MPPI controller in complex and uncertain environments.1

Dual Control for Exploitation and Exploration (DCEE) shows promising performance by realizing optimal trade-off between exploitation and exploration under an unknown environment. However, it is computationally intensive and lacks rigorously established properties such as stability and convergence. This paper addresses these two issues by developing the Nesterov Accelerated Gradient Descent (NAGD) based DCEE, i.e. DCEE-NAGD, where the NAGD is applied to both the source term estimation and the path planning in the DCEE framework. It shows that DCEE-NAGD significantly reduces the search time by driving the search agent moving towards the estimated airborne source location (exploitation) and actively searching new data to reduce the current estimation uncertainty (exploration) with the help of NAGD. The convergence of both the source term estimation and the path planning of the DCEE-NAGD algorithm is rigorously established by applying the mean value theorem and mathematical transformation. More specifically, the convergence boundaries and the convergence rates of the source term estimation and the whole DCEE-NAGD algorithm are rigorously established. Both theoretic analysis and simulations confirm the proposed DCEE-NAGD algorithm significantly improves the performance so reduces the autonomous search time.

The quest for optimal operation in environments with unknowns and uncertainties is highly desirable but critically challenging across numerous fields. This paper develops a dual control framework for exploration and exploitation (DCEE) to solve an auto-optimization problem in such complex settings. In general, there is a fundamental conflict between tracking an unknown optimal operational condition and parameter identification. The DCEE framework stands out by eliminating the need for additional perturbation signals, a common requirement in existing adaptive control methods. Instead, it inherently incorporates an exploration mechanism, actively probing the uncertain environment to diminish belief uncertainty. An ensemble based multi-estimator approach is developed to learn the environmental parameters and in the meanwhile quantify the estimation uncertainty in real time. The control action is devised with dual effects, which not only minimizes the tracking error between the current state and the believed unknown optimal operational condition but also reduces belief uncertainty by proactively exploring the environment. Formal properties of the proposed DCEE framework like convergence are established. A numerical example is used to validate the effectiveness of the proposed DCEE. Simulation results for maximum power point tracking are provided to further demonstrate the potential of this new framework in real world applications.Note to Practitioners—In numerous engineering applications, it is highly desirable to operate a system to improve the efficiency, enhance performance or save energy. However, attaining this optimal control is a challenging task, due to the presence of unknown system and/or environment parameters. We develop a principled approach to balance between exploration and exploitation, involving active learning to estimate unknown parameters and tracking the optimal operational condition based on current estimation. This paper provides a unified framework to solve general auto-optimization control problems. The simulation results demonstrate that the proposed method outperforms existing methods in terms of efficiency and optimality for maximum power point tracking problem, and it can be readily implemented for many other engineering problems. Future research include generalizing the proposed method to nonlinear systems, as well as exploring novel applications to facilitate the widespread adoption of our method.

This article proposes a tracking model for non-holonomic constraint robots, enabling the realization of leadersteered rigid geometry formations. By employing cameras and local leader-based approaches, the challenge posed by traditional separation-bearing control methods, which are incapable of establishing rigid formation for both translational and rotational control, is resolved. In addition, to maintain the connectivity of the sensing topology, the field-of-view (FOV) constraints of the on-board cameras are integrated into the controller design. A conversion approach is used to translate the FOV constraints into a rigid geometry formation. Additionally, there is a trade-off between visibility constraints and the leader-steered rigid geometry formation, particularly when the trajectory of the global leader has significant curvature. To address this problem, a continuously smooth transition function is employed. Ultimately, a fixedtime distributed control protocol and distributed observers are developed to realize the formation framework. Experimental results demonstrate that the proposed control protocol effectively achieves rigid geometric formations and satisfies FOV constraints.

This paper focuses on the issue of limited information based emergency response control for a specific category of underwater cyber-physical systems (UCPSs) confronted with multiple emergencies. The focus is on a typical underwater vehicle system (UVS), whose communication with the control center may be compromised by Denial-of-Service (DoS) attacks. The occurrence of dual DoS attacks leads to significant information loss, intensifying the complexity of decision-making and emergency response during critical situations. Moreover, abrupt ocean current disturbances or faults will also seriously affect the performance of UVSs. Drawing upon distinct DoS attack scenarios, this study introduces a novel emergency response decision mechanism. A limited information-based disturbance observer (DO) is then proposed to effectively handle various unknown disturbances, ensuring favorable disturbance estimation performance. Subsequently, as for different attack channels, two innovative emergency response controllers are respectively proposed, considering the constraints of limited information. Furthermore, distinct stability criteria are derived by using Lyapunov stability and stochastic analysis techniques to guarantee the stabilization of UVSs. Finally, a series of numerical results is presented to illstrate the efficiency of the proposed algorithm under various emergency scenarios.Note to Practitioners—This study presents an novel framework for emergency automatic control and decision-making in UVSs suffering from different emergencies. While most of previous research primarily concentrated on emergency situations in the physical layer, this article delves into both the information layer and the physical layer to tackle decision-making and response challenges within information-constrained environments. Specifically, a limited information-based DO is designed to dynamically model disturbances, such as ocean currents and actuator faults. The DO adapts its structure based on valuable information obtained from emergency monitoring, ensuring accurate disturbance estimation upon successful transmission. The proposed strategy holds significant practical applicability in UVSs for handling emergencies, and further experimental validations are planned to be conducted on actual underwater vehicles.

Control Barrier Function (CBF) based quadratic programs (QPs) have become an effective method for enforcing safety in safety-critical systems and robotics. However, these methods often suffer from infeasibility or overly conservative relaxations when handling multiple constraints, potentially compromising safety. In this paper, we propose a hierarchical framework called “Safety-first" for control design, which simultaneously incorporates performance objectives formulated using Control Lyapunov Functions (CLFs), and safety guarantees via CBFs with input constraints. Unlike existing approaches, the proposed method guarantees solution feasibility while achieving improved performance, and it is scalable to an arbitrary number of CBF constraints. This scalability enables more precise and flexible representation of complex safety requirements using multiple simple CBFs. For application to mobile robot navigation, we employ Constrained Delaunay Triangulation (CDT) to construct multiple CBFs that approximate irregularly-shaped obstacles. Real-world experiments in cluttered and dynamic environments demonstrate that the Safety-first algorithm achieves safe navigation, validating both the theoretical guarantee and practical advantages over existing methods.

Achieving precise depth control of autonomous underwater vehicles (AUVs) poses a considerable challenge owing to packet losses and ocean current disturbances. Consequently, there is an urgent necessity to investigate methods for anti-disturbance depth control of AUVs in scenarios that involve packet losses. In contrast to traditional disturbance estimation techniques that depend on complete state information, which rely on complete states, we propose an intermittent information-based disturbance observer, which leverages most recent data packet to compensate for any current data losses. The novel feature of this new anti-disturbance control lies in its consideration of the issue of information intermittency and its dynamic selection of gains with or without packet losses. Through integration of estimated disturbances, a PI recursive controller has been developed to effectively track desired depth of AUVs. In addition, a stability criterion has been established using the Lyapunov stability theory and stochastic analysis techniques, with calculation of the possibility that the Lyapunov function is bounded through Chebyshev’s inequality. Finally, comprehensive simulations have been performed to demonstrate the tracking effectiveness of the proposed algorithm, and its real-world applicability and reliability have also been confirmed through experimental trials.

This study investigates bipartite containment control algorithm for multi-agent systems which are vulnerable to DoS attacks and external disturbances. Disturbances are generated via a series of exogenous nonlinear systems with a one-sided Lipschitz condition. To estimate unknown system states and external disturbances, a set of decentralized state observers and disturbance observers is proposed. Then a fully distributed control protocol is adopted to avoid the utilization of global information. An attack compensator is introduced to mitigate the adverse impacts of DoS attacks. Furthermore, Zeno-free dynamic event-triggered mechanisms that do not require continuous communication between neighboring agents are presented to conserve limited communication resources. In the end, satellite flight systems are introduced to illustrate the feasibility of the designed control scheme.

This article simultaneously addresses the dual-rate and view constraints issues for the image-based visual servoing (IBVS) system of robot manipulators. Considering the low sampling bandwidth of the camera, potentially diminishing the efficiency of the robotic controller in updating low-level servoing control commands, a predictive observer (PO) is initially designed to forecast the system output during the high-level sampling intervals. Moreover, by leveraging a mixture of soft-sensing and real-measured signals, a dual-rate integral-based prescribed performance control (DRIPPC) approach is devised. The benefit lies in that the proposed control method samples the low-frequency state signal while generating a relatively high-frequency control action, ensuring rapid response of the robot manipulator while maintaining strict adherence to field-of-view (FOV) constraints. Finally, the effectiveness of the proposed control approach is validated through a series of experiments conducted on a Universal Robots 5 (UR5) manipulator.

This study discusses the problem of event-triggered (ET) asymptotic tracking control for parametric strict feedback nonlinear systems (SFNSs) with time-varying disturbances and unknown control directions. A unified dynamic threshold method is proposed by combining a unified function with a self-adjustable performance function. In contrast to previous research, the results of this study provide a unified framework in which global or semi-global performance can be achieved through simple parameter selection while excluding the conservativeness of the constraint thresholds owing to the artificial selection of a uniform performance function. The basic lemma based on the Nussbaum function frequently is extended to adapt to the case in which the coefficients are multiple bounded functions. By fusing a first-order differentiator, the reduplicative derivation of the virtual controller in the backstepping process is obviated. Moreover, an ET mechanism with two dynamic variables is constructed to reduce the burden of data transmission. The developed controller can guarantee the boundedness of all signals in the closed-loop system, full-state constraints performance, and asymptotic tracking control performance. Finally, the feasibility of the proposed scheme is attested by two examples.

This article investigates the state estimation and security control problem for discrete-time dual-rate cyber-physical systems (CPSs) under denial-of-service (DoS) attacks. The asynchrony predicament between different signals of dual-rate CPSs, exacerbated by the impact of cyber attacks on the sensor-to-controller channel, substantially increases the complexity of state estimation and control processes. Based on the signal-to-interference-plus-noise ratio and two-channel probability descriptions, an improved sample-and-hold (SAH) estimator is applied to dual-rate CPSs, ensuring favorable state estimates while enduring low-frequency sampling and DoS attacks. Furthermore, to solve the performance degradation problem posed by the SAH algorithm, an alternating-sampling-prediction (ASP)-based estimation method is proposed. At each fast-update moment, the predictor generates virtual outputs. The estimator can reconstruct complete state information by alternately using incomplete sampling data and iterative predictive information. Compared with the SAH method, the proposed ASP-based approach significantly enhances the control performance of dual-rate CPSs. Building on two valid estimation methods, the corresponding security control inputs are designed, guaranteeing both ideal control performance and resilience against attacks. Using convex optimization analysis, both estimator and controller gains are calculated to realize the stochastic stability of closed-loop dual-rate CPSs. Finally, the effectiveness and intercomparisons of the two estimation methods are shown by simulating a satellite yaw-angle control system and a quadrotor landing control experiment.

The high level of autonomy within autonomous systems demands new control strategies to achieve more complex objectives while ensuring both safety and robustness, rather than relying solely on a given reference. To this end, this article addresses the problem of temporal logic disturbance rejection control (TLDRC) for a class of nonlinear systems subject to disturbances. Signal temporal logic (STL) specifications are introduced for the representation of complex tasks. A control barrier function (CBF), composed of a monotonic function characterizing the temporal behavior of the system and a predicate function, is constructed to encode the STL specifications. To guarantee robustness against disturbances, generalized proportional integral observers (GPIOs) are introduced for higher-accuracy disturbance estimation. It is shown that by fully exploiting the constructed CBF and the disturbance estimate, the developed TLDRC strategy is able to ensure the STL specifications and compensate undesirable effects caused by unknown disturbances, even if they are fast-time-varying. A numerical example is presented to illustrate the effectiveness of the proposed strategy.

The development of rigorous theoretical tools, such as feasibility and stability analysis, for finite-control-set model predictive control (FCS-MPC) of electric drives, has lagged behind advancements in engineering practice. To address this gap, this article introduces a unified control method that integrates disturbance estimation and control Lyapunov functions (CLFs) within the FCS-MPC framework. This approach is applied to perturbed electrical drive systems, with inverter-fed permanent magnet synchronous motor systems as a primary example. First, we propose a specific class of CLFs that enables separate design of disturbance estimation, later incorporating it as a constraint in the optimization problem. Theorems and lemmas are then provided to demonstrate that using the disturbance-estimation-based control Lyapunov function constraint in an FCS-MPC setting ensures a nonempty feasible control set, guaranteeing closed-loop stability by design. Experiments conducted on a test bench validate the practicability of the proposed method. A comprehensive performance evaluation is presented under various conditions.

In overhead cranes, the inclination angle of the payload must be limited within an acceptable range to ensure safety, and the trolley reaches a desired position simultaneously. However, the payload can be disturbed by strong winds, which poses a certain threat to the safety of overhead crane operation. In addition, when multiple overhead cranes communicate with each other via a shared network, the communication and computational resources available to each overhead crane become constrained. Taking into account the above factors, a novel event-triggered safety-critical model predictive control (ESMPC) algorithm is proposed for underactuated overhead crane systems to achieve satisfactory performance. In the proposed ESMPC algorithm, disturbances acting on the payload are estimated by a discrete-time disturbance observer. Then, the discrete-time predictive control barrier function is devised to guarantee operational safety. Subsequently, the prediction model is derived and the quadratic programming (QP) problem is formulated. The optimal control sequence can be obtained by solving the QP problem at each event-triggering instant. After that, the predicted control inputs are fully exploited and applied to the trolley one by one chronologically. Finally, the experimental results show that the payload swing angle can be limited within the safe range and the trolley can reach the desired position by using fewer communication and computing resources under the proposed ESMPC method.

Tracking accuracy is critical for practical applications. However, due to the presence of various disturbances and intermittent information caused by packet losses or network attacks, ensuring tracking precision and speed becomes extremely challenging. Consequently, this article proposes a novel control architecture for output tracking with intermittent information, which includes an intermittent information-based disturbance observer (IIBDO) and a flexible antidisturbance control strategy based on conditional disturbance utilization (CDU). The novel IIBDO addresses the issue of information intermittency by compensating for lost information using available signals and their trends. Building on this, a disturbance diagnosis condition (DDC) is introduced to assess whether disturbances are beneficial to system performance. Through integration of DDC and disturbance estimation, the CDU-based flexible antidisturbance control is designed, enabling the system to utilize disturbances rather than merely rejecting them. Sufficient conditions are derived to ensure both tracking and antidisturbance performance, and the potential stabilizing effects of disturbances on the system are also analyzed using Lyapunov stability theory. Finally, comprehensive simulations and experiments confirm the effectiveness of the IIBDO, and the improvement in tracking performance brought about by CDU is also verified.

This article focuses on the private security of multiagent systems with external eavesdroppers. Note that the agent data/states finally reach the same value by using the consensus-based anti-eavesdroppers protocol with Laplace noises, which leads to the leakage possibility of the terminal information. To this end, a novel differential privacy protocol is designed based on directed signed networks, which yields the performance of$\xi$-differential privacy-preserving. To further reveal the effects of eavesdroppers on the system, the privacy disclosure proportion of groups (PDPGs) is proposed in the two cases of a single-channel eavesdropper and a multichannel eavesdropper. It is indicated that the PDPG of the resulting system does not exceed 50% even if the malicious adversary has the ability to crack the privacy-preserving mechanism. The effectiveness of the approach and its superiority over state-of-the-art counterparts are confirmed through numerical simulations.

Lane detection plays a crucial role in automated driving technology. However, current lane detection algorithms primarily focus on daily scenes, such as cars on urban roads and highways, while limited research addresses closed scenes, such as rubber tire gantries (RTGs) in ports. In this study, we propose a novel lane detection method based on explicit feature learning (EFL) to address this gap. Compared to general scenes, RTG lane lines in port exhibit distinctive characteristics, including regular straight-shaped features, relative spatial stability, and severe wear from heavy RTGs. There is also a higher requirement on algorithm precision while with a limited dataset size. To effectively address these challenges, we proposed PortLaneNet, which introduces an EFL network for better feature extraction, coupled with a shape loss function for improved evaluation and supervision. The multiscale feature extraction of the EFL network improves the model's global perception ability to cope with the loss of features caused by the severe wear of the port lane lines. The improved shape loss function strengthens the linearity of the model's inference results to adapt to the shape characteristics of port lane lines. To improve the model's spatial feature extraction capability, an adaptive channel attention mechanism is also introduced, which embeds spatial positional information of rows. The proposed method is evaluated on a self-collected dataset under various challenging environments, where extensively comparative experiments against six state-of-the-art algorithms with various backbones show that the proposed PortLaneNet demonstrates better detection performance in terms of F1 score, precision, and recall while with computation efficiency suitable for real-time applications.

Rain deviates the distribution of rainy images and the clean, rain-free data typically used during perception model training, this kind of out-of-distribution (OOD) issue making it difficult for models to generalize effectively in rainy scenarios, leading the performance degrade of autonomous perception systems in visual tasks such as lane detection and depth estimation, posing serious safety risks. To address this issue, we propose the Ultra-Fast Deraining Plugin (UFDP), a model-efficient deraining solution specifically designed to realign the distribution of rainy images and their rain-free counterparts. UFDP not only effectively removes rain from images but also seamlessly integrates into existing visual perception models, significantly enhancing their robustness and stability under rainy conditions. Through a detailed analysis of single-image color histograms and dataset-level distribution, we demonstrate how UFDP improves the similarity between rainy and non-rainy image distributions. Additionally, qualitative and quantitative results highlight UFDP’s superiority over state-of-the-art (SOTA) methods, showing a 5.4% improvement in SSIM and 8.1% in PSNR. UFDP also excels in terms of efficiency, achieving 7 times higher FPS than the slowest method, reducing FLOPs by 53.7 times, and using 28.8 times fewer MACs, with 6.2 times fewer parameters. This makes UFDP an ideal solution for ensuring reliable performance in autonomous driving visual perception systems, particularly in challenging rainy environments.

In this article, a multi-estimator based computationally efficient algorithm is developed for autonomous search in an unknown environment with an unknown source. Different from the existing approaches that require massive computational power to support nonlinear Bayesian estimation and complex decision-making process, an efficient cooperative active-learning-based dual control for exploration and exploitation (COAL-DCEE) is developed for source estimation and path planning. Multiple cooperative estimators are deployed for environment learning process, which is helpful to improving the search performance and robustness against noisy measurements. The number of estimators used in COAL-DCEE is much smaller than that of the particles required for Bayesian estimation in information-theoretic approaches. Consequently, the computational load is significantly reduced. As an important feature of this study, the convergence and performance of COAL-DCEE are established in relation to the characteristics of sensor noises and turbulence disturbances. Numerical and experimental studies have been carried out to verify the effectiveness of the proposed framework. Compared with the existing approaches, COAL-DCEE not only provides convergence guarantee but also yields comparable search performance using much less computational power.

This paper proposes a radio frequency (RF) down-converter, in which part of the RF transconductance (gm) stage is reused as the bias current source of the transimpedance amplifier (TIA) to realize trans-frequency current-reusing, thereby saving 30% power consumption of the down-converter. Other from a conventional passive down-converter, TIA’s DC bias current flows through the mixing switches in the proposed down-converter. Hence it is so called the sub-passive down-converter. Compared with the passive topology where flicker noise from the TIA’s bias is fully fed to the intermediate frequency (IF) output, the flicker noise of the TIA’s current source supplied by the RF gmstage is up-converted and filtered out. The flicker noise corner frequency is cut-down to 20KHz, applicable for the zero-IF receiver. The linearity is also improved by the completely counteracting of third-order transconductance from PMOS and NMOS gmtransistors in the transconductance amplifier (LNTA), which is contributed by their asymmetrical bias current. Fabricated in TSMC 40nm CMOS technology, the sub-passive RF downconverter achieves 55dB conversion gain (CG) and 4dB noise figure (NF), which is suitable for high sensitivity applications. And it implements 23dBm OIP3 at 2.4GHz with 0.8mW power consumption at 0.65V supply voltage and the chip area of 0.3mm × 0.9mm.

This paper presents a generalized multiple disturbances estimation and compensation based position precision control of maglev systems, especially for the estimation and compensation of the unknown periodic disturbance. In order to accurately track the periodic reference and estimate the periodic disturbance, the internal model of periodic signals is embedded into the design of the generalized disturbance estimator (GDE) and the outer-loop controller, respectively. Meanwhile, the adaptive period estimator (APE) is designed to ensure the good estimation accuracy of the periodic disturbance when the period of the disturbance varies in a small range. Then, the disturbance estimate and its derivatives are introduced into the control law, and the disturbance compensation gain are designed to eliminate the influence of mismatched disturbances on the output. Finally, the stability and anti-disturbance performance of the proposed method are analyzed, and the effectiveness of the proposed method is verified by simulation and experiment.Note to Practitioners—For the maglev systems, there are many types of disturbances, such as constant, sinusoidal and periodic disturbances. If these types of disturbances are simply considered, the good control performance will not be achieved. For this reason, a generalized multiple disturbance estimation and compensation based position precision control is proposed in this paper, with special attention to the estimation and compensation of periodic disturbance, whose period varies in a small range. The internal mode of periodic signals and the proposed APE are employed for the design of disturbance estimation and the modified repetitive control (MRC). Then, the disturbance compensation gain is designed to solve the adverse effect of the mismatched disturbance for the maglev systems. Simulation and experimental results verify the effectiveness of the proposed method. The reported idea of generalized multiple disturbance estimation and compensation can also be extended to control of other types of systems with multiple disturbances.

Considering the multi-frequency-band uncertainties with distinctive forms and perturbed coefficients faced by flexible gimbal servo systems (GSSs) in control moment gyros (CMGs), a robust high-accuracy speed-regulation controller is proposed in this paper. Firstly, to obtain a more precise uncertainties compensation, a generalized model of flexible GSS with refined classification and description upon various types of uncertainties is accomplished. Then a comprehensive disturbance observer is designed based on this generalized model to accurately estimate compound disturbances in multi-frequency-band simultaneously, and a novel quantitative robustness analysis and method against the frequency deviations of faced periodic disturbance is further conducted. Due to the nominal recovery performance guaranteed by this estimation-based feedforward framework, a resonance cancellation-based composite controller is designed for the speed vibration suppression during the transient processes even in the presence of flexibility coefficients perturbation. Rigorous robust stability analysis for the closed-loop system is established. Experimental results with various uncertainties are provided to fully validate the effectiveness of the proposed scheme.

This article investigates the event-triggered model predictive control (MPC) problem for a class of networked nonlinear uncertain systems subject to time-varying disturbances. Different from the traditional MPC, the proposed periodic event-triggered MPC (PETMPC) method does not generate new control sequence unless a predesigned periodic event-triggering mechanism (PETM) is violated. First, a generalized proportional-integral observer (GPIO) is developed to estimate the unknown state and disturbance information by using the sampled-data output of controlled system. Then, the disturbance predictions for future finite steps are obtained based on forward Euler method. After that, with the help of prediction model, the optimal control sequence, including the future finite step predicted control inputs, is generated and dexterously exploited during the interevent interval by storing it in a buffer installed between the control sequence generator and actuator, thereby leading to the further reduction of signal transmission number and the frequency of control sequence computations. Through a rigorous stability analysis, it can be proved that the closed-loop hybrid control system is globally bounded stable under the nominal PETMPC law. Finally, numerical simulations are conducted to substantiate the feasibility and superiority of the proposed PETMPC method.

This article devises a new dynamic double event-triggered anti-disturbance tracking control scheme for a 2-degree of freedom (DOF) laboratory helicopter subject to external time-varying disturbances and load fluctuation by using the generalized proportional-integral observer technique. The helicopter system is separated into two subsystems in the proposed control method, i.e., the pitch subsystem and the yaw subsystem. Each subsystem includes a discrete-time dynamic double event-triggering mechanism (DDETM), and the control laws of the two subsystems are independent of each other. There are two triggering conditions in the designed double triggering mechanism: one is designed based on the system states and the other is based on the lumped disturbance estimation. These two triggering conditions form a competitive relationship such that the controller updates the control signal as long as one of the triggering conditions is satisfied. Theoretical analysis is provided for achieving the better communication and control performance of the proposed DDETM-based robust control method. Through rigorous stability analysis, it is proved that the closed-loop hybrid system is globally ultimately bounded. At last, numerical simulations show that the suggested control strategy not only reduces the event-triggering number, but also improves the initial dynamic performance of the system.

This article investigates the robust control problem for speed regulation of networked permanent magnet synchronous motor subject to the limited communication bandwidth. To handle this, a new sampled-data disturbance rejection control method is developed via a well-designed discrete-time dynamic event-triggered mechanism (DETM). First, a predictor-based generalized proportional integral observer is introduced to estimate the lumped disturbances, when only the sampled-data output is available. Then, a composite proportional feedback controller is formed by fully utilizing disturbance estimation. The composite controller updates only when the designed discrete-time DETM is violated, resulting in remarkable communication and computation resource savings while maintaining the desirable disturbance rejection ability. The designed DETM can be applied to digital computers easily due to the discrete-time detection. Simulations and experiments are carried out to validate the feasibility and effectiveness of the proposed control scheme.

Inspired by the concept of recently proposed dual control for exploration and exploitation, this article presents a multistep dual control for exploration and exploitation with guaranteed convergence in search for autonomous sources. To deal with an unknown source position and environment, the proposed dual control algorithm faces significant challenges in demonstrating its recursive feasibility and convergence. With the help of the properties of Bayesian estimators, we redesign a multistep dual control for exploitation and exploration algorithm with necessary terminal ingredients and show that the recursive feasibility and the convergence of the modified dual control algorithm are guaranteed. Two simulation scenarios are conducted, which demonstrate that the proposed algorithm outperforms the stochastic model-predictive control approach and the informative path planning approach in terms of searching successful rates and efficiency.

This paper proposes an active information-directed reinforcement learning (AID-RL) framework for autonomous source seeking and estimation problem. Source seeking requires the search agent to move towards the true source, and source estimation demands the agent to maintain and update its knowledge regarding the source properties such as release rate and source position. These two objectives give rise to the newly developed framework, namely, dual control for exploration and exploitation. In this paper, the greedy RL forms an exploitation search strategy that navigates the agent to the source position, while the information-directed search commands the agent to explore most informative positions to reduce belief uncertainty. Extensive results are presented using a high-fidelity dataset for autonomous search, which validates the effectiveness of the proposed AID-RL and highlights the importance of active exploration in improving sampling efficiency and search performance.

A novel adaptive fixed-time controller (AFTC) based on disturbance compensation technology is proposed to achieve high performance position precision control for magnetic levitation system in this paper. Firstly, the dynamic model of the magnetic levitation system is established and a fixed-time controller (FTC) is designed to realize the closed-loop control. However, this approach usually requires a large switching gain to suppress interference, resulting in chattering. In view of this, the generalized proportional integral observer (GPIO) is introduced to estimate and compensate the time-varying interference, which can not only improve the anti-interference ability, but also reduce the chattering by choosing a smaller switching gain. Nevertheless, these two performance improvements come at the cost of the dynamic response rate. In order to improve steady state performance without sacrificing dynamic performance, an adaptive fixed-time controller based on GPIO is proposed, which has a significant advantage because of the adjustable switching gain. Specifically, when the system state is far from the sliding mode surface, a larger switching gain is adjusted to improve the convergence rate. When the system state is close to the sliding mode surface, a smaller switching gain is adjusted to reduce chattering. Simulation and experimental results demonstrate the superiority of the proposed AFTC-GPIO method qualitatively and quantitatively. Note to Practitioners–As a highly nonlinear system easily affected by external disturbances and system uncertainty, high precision position control of magnetic levitation system is a great challenge. In this paper, based on the accurate estimation of lumped time-varying interference by GPIO, an adaptive fixed time sliding mode controller is designed to suppress the disturbance and achieve the high precision control. Traditional sliding mode control inevitably has to choose between improving convergence rate and suppressing chattering, and the dynamic and steady performance of the system cannot be considered simultaneously. In view of this issue, this paper combines sliding mode control with adaptive control, and an adaptive and adjustable switching gain is designed, so that the system has the performance of fast convergence and small chattering. Simulation and experimental results verify the effectiveness of the proposed method. The reported AFTC-GPIO idea can also be extended to control of other types of systems.

In this article, we investigate the problem of sampled-data robust output feedback control for a class of nonlinear uncertain systems with time-varying disturbance and measurement delay based on continuous-discrete observer. An augmented system that includes the nonlinear uncertain system and disturbance model is first found, and by using the delayed sampled-data output, we then propose a novel predictor-based continuous-discrete observer to estimate the unknown state and disturbance information. After that, in order to attenuate the undesirable influences of nonlinear uncertainties and disturbance, a sampled-data robust output feedback controller is developed based on disturbance/uncertainty estimation and attenuation technique. It shows that under the proposed control method, the states of overall hybrid nonlinear system can converge to a bounded region centered at the origin. The main benefit of the proposed control method is that in the presence of measurement delay, the influences of time-varying disturbance and nonlinear uncertainties can be effectively attenuated with the help of feedback domination method and prediction technique. Finally, the effectiveness of the proposed control method is demonstrated via the simulation results of a numerical example and a practical example.

The robust filtering problem is tackled for a class of shift-varying two-dimensional systems with uncertain-variance noises under the scheduling of the weighted try-once-discard (WTOD) protocol. The measurements collected from the sensors are transmitted to a remote filter via a shared network. To alleviate the communication burden and obviate the network congestion, the WTOD protocol is adopted to orchestrate the data transmission, where a sensor node is solely permitted to broadcast its information to the remote filter at every transmission step. Moreover, the resilient filter is exploited to regulate the possible gain perturbation. The objective of the addressed problem is to design a robust filter in a recursive structure such that, in the simultaneous presence of the uncertain-variance noises and the WTOD protocol, the minimal upper bounds (UBs) on the filtering error variances (EVs) are developed for the considered system. First, by means of induction and stochastic analysis technique, certain UBs in terms of coupled recursive difference equations are derived for the actual EVs. Then, a proper filter is carefully designed which achieves the minimization of the obtained UBs at each step. Finally, an illustrative example is presented to verify the usefulness of the proposed protocol-based filtering method.

In this paper, the output feedback based periodic event-triggered control problem is considered for a class of nonminimum-phase nonlinear systems via dynamic event-triggering mechanism. When only the sampled-data output is known, a new periodic event-triggered control method is proposed via output feedback and the control input updates based on a discrete-time dynamic event-triggering condition. Despite the unstable zero dynamics, the proposed control method can asymptotically stabilize the hybrid control systems by closing the loop only when it is necessary. In contrast to the continuous-time static one, the proposed dynamic triggering condition has several advantages including the easier digital implementation and the larger average inter-event time interval. The delicate analysis gives the explicit expression of the maximum allowable sampling period, and the global asymptotic stability can be achieved for the hybrid control systems. Finally, the effectiveness of the proposed periodic event-triggered control method is verified by a numerical simulation.

This article considers the problem of periodic event-triggered control design for dual-rate networked control systems subject to nonvanishing disturbance. The plant considered in this article is a kind of dual-rate networked control system, where the sensor samples the measurement output at a slow rate and the actuator updates the control input at a fast rate. Despite the slow-rate sampling of the sensor, a new output predictor-based observer is proposed to accurately estimate system state and disturbance in the intersample time interval, and an active anti-disturbance controller that updates at a fast rate is accordingly proposed, such that the desirable control performance and disturbance rejection performance can be achieved. At each fast-rate updating time instant, we use the prediction technique to generate a data packet, including the computed current control input and the predicted values of the control inputs for the future finite steps, and design a new periodic event-triggered mechanism to determine whether to transmit the data packet via a communication network or not. The proposed control method is easily implemented in digital platform since it has a discrete-time form. To verify the effectiveness of the proposed control method, we finally present the simulation results of a practical speed control system.

This article tackles the recursive filtering problem for an array of 2-D systems over sensor networks with a given topology. Both the measurement degradations of the network outputs and the stochastic perturbations of network couplings are modeled to reflect engineering practice by introducing some random variables with given statistics. The goal of the addressed problem is to devise the distributed recursive filters capable of cooperatively estimating the true state in order to ensure locally minimal upper bound (UB) on the second-order moment of the filtering error (also viewed as the general error variance). For this purpose, the general error variance regarding the underlying target plant is first provided to facilitate the subsequent filter design, and then a certain UB on the error variance is constructed by exploiting the stochastic analysis and the induction approach. Furthermore, in view of the inherent sparsity of the sensor network, the gain parameters of the desired distributed filters are determined, and the proposed recursive filtering algorithm is shown to be scalable. Finally, an illustrative example is given to demonstrate the validity of the established filtering strategy.

This paper develops a new estimate-based dynamic event-triggered output feedback controller for networked control systems subject to nonlinear uncertainties. Specifically, based on the sampled-data, a discrete-time output feedback controller and a discrete-time dynamic event-triggering condition are proposed by the virtue of feedback domination technique. The proposed event-triggered control method is easy to implement in digital computers due to the form of discrete time. Under the proposed dynamic event-triggered control method, the selection regions of the sampling period and the scaling gain are explicitly given to guarantee the global practical/asymptotic stability of the closed-loop system. Finally, two examples are employed to verify the efficiency of the proposed dynamic event-triggered control approach.

This paper proposes a new output-based dynamic event-triggered mechanism (ETM) for disturbance rejection control of a class of networked nonlinear uncertain systems subject to additive time-varying disturbance. In the proposed control method, a new robust output feedback controller is first designed based on a generalized proportional-integral observer to attenuate/compensate the undesirable influence of nonlinear uncertainties and disturbances. Different from the static ETM, two new dynamic variables are defined, and thereafter, two kinds of different discrete-time dynamic ETMs are developed only using the sampled-data output signal, such that a better tradeoff between the communication properties and the control properties can be obtained. It is shown that under the proposed control methods, the global bounded stability of the closed-loop hybrid system can be guaranteed by choosing some appropriate parameters. Finally, the numerical simulations of a single link robot arm are conducted to demonstrate the feasibility and efficacy of the proposed control approach.

As a virtual sensor, disturbance observer provides an alternative approach to reconstruct lumped disturbances (including external disturbances and system uncertainties) based upon system states/outputs measured by physical sensors. Not surprisingly, measurement errors bring adverse effects on the control performance and even the stability of the closed-loop system. Toward this end, this paper investigates the problem of disturbance observer-based control for a class of disturbed uncertain nonlinear systems in the presence of unknown output measurement errors. Instead of inheriting from the estimation-error-driven structure of Luenberger-type observer, the proposed disturbance observer only explicitly uses the control input. It has been proved that the proposed method endows the closed-loop system with strong robustness against output measurement errors and system uncertainties. With rigorous analysis under the semiglobal stability criterion, the guideline of gain choice based upon the proposed structure is provided. To better demonstrate feature and validity of the proposed method, numerical simulation and comparative experiments of a helicopter model are implemented.

It is interesting and challenging to develop advanced controller for industrial boiler-turbine units due to their genuine nonlinearities, serious couplings among state variables, physical constraints imposed on control inputs, and in particular, various types of unknown uncertainties/disturbances. In this paper, a nonlinear disturbance rejection control method is investigated in a composite design manner for an oil-fired drum-type boiler-turbine unit. A baseline exponentially stable feedback controller is first designed, and then a high-order sliding mode observer is utilized to estimate and thus to compensate unknown lumped disturbances. It is shown that the obtained results for the boiler-turbine unit can be extended to a wide class of nonlinear systems. More interestingly, the finite-time stability of the closed-loop systems can be rendered in several cases. Finally, some numerical simulation scenarios are conducted on the boiler-turbine unit to demonstrate the claimed control performance.

This paper investigates a novel nonrecursive tracking control law design for a class of nonlinear systems via dynamic output feedback. As the main contribution of this paper, a global nonrecursive tracking design procedure is first proposed to render a simple construction of a realizable output feedback control law, whose gain selections follow the conventional pole placement approach while the stability margin can be guaranteed via a sufficiently large scaling gain. By introducing a Lyapunov function which neglects the virtual controllers in essence, rigorous analysis is presented to ensure the global stability. In addition, finite-time and asymptotical tracking results can now be achieved within the same design framework whereas the tunable homogeneous degree plays as a key role. As another contribution, by proposing a saturated dynamic compensator, a less ambitious but practical control objective, namely semiglobal stability is achieved of the closed-loop system to relax the requirement of the restrictive growth conditions for global control design. Taking consideration of the case when system is subject to mismatched disturbances, a unified design and stability analysis framework shows that the practical tracking result can also be realized. A numerical example is provided to illustrate the effectiveness of the nonrecursive design and the simplicity of the proposed tracking control algorithm.

The wide utilization of power electronic converters causes the constant power load stability issue in DC microgrids. This paper proposes an offset-free model predictive controller for a DC/DC boost converter feeding constant power loads. First, a baseline nonlinear model predictive controller is designed by solving a receding horizon optimization problem explicitly. Then a higher-order sliding mode observer is utilized to estimate the unknown load variation and system uncertainties. Finally an offset-free controller is integrated by the baseline controller and observer. The proposed controller achieves optimized transient dynamics and accurate tracking with large signal stability. Simulation results are presented to verify the proposed approach.

This paper develops a methodology on sampled-data-based event-triggered active disturbance rejection control (ET-ADRC) for disturbed systems in networked environment when only using measurable outputs. By using disturbance/uncertainty estimation and attenuation technique, an event-based sampled-data composite controller is proposed together with a discrete-time extended state observer. Under the presented new framework, the newest state and disturbance estimates as well as the control signals are not transmitted via the common sensor-controller network, but instead communicated and calculated until a discrete-time event-triggering condition is violated. Compared with the periodic updates in the traditional time-triggered active disturbance rejection control, the proposed ET-ADRC scheme can remarkably reduce the communication frequency while maintaining a satisfactory closed-loop system performance. The proposed discrete-time control scheme provides the engineers with a manner of direct and easier implementation via networked digital computers. It is shown that the bounded stability of the closed-loop system can be guaranteed. Finally, an application design example of a dc-dc buck converter with experimental results is conducted to illustrate the efficiency of the proposed control scheme.

This paper investigates the angular velocity trajectory tracking problem for a DC permanent magnet motor with a DC-DC buck power converter based smooth starter. In practice, the composite control system is inevitably effected by unknown, exogenous, time-varying load torque disturbances. To this end, this paper specifically puts forward a disturbance rejection control approach. Under the proposed method, a novel disturbance observer is constructed to estimate the differences between actual states and the steady-state values of states and control. Therefore, an output feedback composite controller is designed. A key idea in the proposed control strategy is that the unmatched disturbances are transformed into matched ones ingeniously. The proposed control method is tested through simulation studies, where the results obtained validate the superiority of the proposed method.

One weakness of the model predictive control method is that the predicted states/outputs are constructed by an exact nominal model. Its accuracy varies if uncertainties exist, which will ultimately deteriorate the closed-loop control performances. To this end, we propose a generalized dynamic predictive control method for a class of lower-triangular systems subjected to nonparametric uncertainties. Instead of relying on the inherent robustness property of the standard predictive controller or on-/off-line parameter identification, a dual-layer adaptive law is designed to estimate the lumped effect of system uncertainties. As another main contribution, under a less ambitious but more practical control objective, namely semi-global stability, various nonlinearity growth constraints utilized in the existing related methods could be essentially relaxed. Numerical simulation and illustrative experimental tests of a series elastic actuator system are provided to demonstrate both simplicity and effectiveness of the proposed method.

This paper proposes a new continuous terminal sliding mode guidance law for three-dimensional missile guidance system under maneuvering target with consideration of second-order autopilot dynamics. The proposed guidance law not only guarantees the azimuth rate and elevation rate of guidance system converge to zero in finite time but also ensures the continuity of control action. Simulations of a practical interception process under complex maneuvering target are carried and the simulation results demonstrate the effectiveness of the proposed guidance method.

In this paper, the output voltage trajectory tracking for the conventional DC-DC boost power converter in the presence of circuit parameter perturbations is investigated. Based on the property of incremental passivity, a simple feedback controller is designed. Meanwhile, to obtain a better disturbance rejection property, we employ two nonlinear disturbance observers (NDOBs) to attenuate the uncertainties in the output voltage and inductor current channels, respectively. Moreover, global trajectory tracking performance of the system under disturbances is ensured. Finally, simulation and experiment studies are offered to confirm the feasibility and efficiency of the presented approach. The related results reveal the proposed controller delivers a nice antidisturbance performance as well as a superior nominal tracking ability.

With the explosive growth of applications and application traffic, data centers networks are challenged in network utilization and network performance, which is crucial to guarantee the efficiency of computing and storage resources of data center servers. Recently, software defined network has been exploited to manage the network resource in a center controller, which provides opportunities to optimize the network resource utilization. In this paper, we analyze the challenges of the network resource management in data center with software defined network, and point out the opportunities.

An adaptive control scheme based on disturbance observer is proposed in this paper for the bank-to-turn missile autopilot design. In the past, the disturbance observer has been introduced for bank-to-turn missile system to improve system performance in the presence of severe disturbances. However, the disturbance rejection performance is not quite satisfying when considering the fluctuation of the aerodynamic parameters. Therefore, a kind of time-varying disturbance observer is proposed in this paper to enhance the robustness against variations of aerodynamic parameters. The main innovation is that the nominal model of the BTT missile in the disturbance observer is updated over time along with the aerodynamic parameters. And simulation comparisons in the last part show the effectiveness of the proposed adaptive control method.

In this paper, we propose an event-driven output feedback control strategy for a class of nonlinear systems subject to disturbances. Different from the time-driven control, the event-driven control can be regarded as a more reactive approach where the control actions are taken only when an event is triggered, thus the event-driven control has a better balance between the control performance and other system aspects (such as processor load, communication load, and system cost price). Based on the extended state observer (ESO), we propose a composite event-driven controller, which is asynchronously updated only when an intolerable effect on the closed-loop performance is produced. It is proved that the closed-loop system is globally uniformly bounded, and has a good robustness against disturbances. Meanwhile, the closed-loop system considered in this paper is a hybrid system, thus we need to consider the problem of Zeno behavior, which is a phenomenon unique to hybrid systems, and describes the situation where a hybrid system undergoes an unbounded number of discrete transitions in a finite and bounded length of time. Fortunately, it is proved that the system under the event-driven controller can avoids the Zeno behavior of the sampling, and has the significantly reduced sampling frequency compared with the time-driven controller. Finally, a simulation of DC-DC buck converter is conducted to demonstrate the efficiency of the new scheme.

A new continuous dynamic sliding-mode control (CDSMC) method is proposed for high-order mismatched disturbance attenuation in motion control systems using a high-order sliding-mode differentiator. First, a new dynamic sliding surface is developed by incorporating the information of the estimates of disturbances and their high-order derivatives. A CDSMC law is then designed for a general motion control system with both high-order matched and mismatched disturbances, which can attenuate the effects of disturbances from the system output. The proposed control method is finally applied for the airgap control of a MAGnetic LEViation (MAGLEV) suspension vehicle. Simulation results show that the proposed method exhibits promising control performance in the presence of high-order matched and mismatched disturbances.