Despite growing interest in data-driven analysis and control of linear systems, descriptor systems (or singular systems)—which are essential for modeling complex engineered systems with algebraic constraints like power and water networks— have received comparatively little attention. This paper develops a comprehensive data-driven framework for analyzing and controlling discrete-time descriptor systems without relying on explicit state-space models. We address fundamental challenges posed by non-causality through the construction of forward and backward data matrices, establishing data-based sufficient conditions for controllability and observability in terms of input-output data, where both R-controllability and C-controllability (R-observability and C-observability) have been considered. Building on them, we then extend Willems’ fundamental lemma to incompletely controllable descriptor systems. These methodological advances Data-Enabled Predictive Control (DeePC) for descriptor systems to achieve output tracking and to maintain performance under incomplete controllability conditions, as demonstrated in two case studies: i) Frequency regulation in an IEEE 9-bus power system with 3 generators, where DeePC maintained the frequency stability of the power system despite deliberate violations of R-controllability, and ii) Pressure head control in an EPANET water network with 3 tanks, 2 reservoirs, and 117 pipes, where output tracking was successfully enforced under algebraic constraints.

Magnetic continuum robots offer a promising alternative to conventional guidewires for endovascular interventions, owing to their potential for remote and radiation-free manipulation. However, enabling closed-loop control of magnetic continuum robots within complex vascular environments remains challenging due to the anatomical intricacy, frequent directional transitions, and the need for context-aware magnetic actuation under physiological conditions. In this paper, a structure-aware and energy-minimized actuation framework (SEMAF) that consists of a structure-aware path planning module, an energy-minimized modeling module, and a magnetic actuation module is proposed. The SEMAF establishes a mapping from anatomical perception, such as bifurcation geometry, to actuation-level control parameters, including joint angles and magnetic moments. Extensive experiments, including ex vivo interventions on the right common carotid artery, cerebral aneurysms, and the middle cerebral artery, as well as in vivo studies on rabbits, validate the SEMAF’s adaptability, accuracy, and clinical potential. Experimental results demonstrate that SEMAF reduces operation time by 87.98% compared to manual guidewires and by 56.23% relative to the centerline-based intervention, while achieving an average orientation error of 10.17° and maintaining successful navigation across three representative vascular scenarios and an in vivo rabbit superior mesenteric artery intervention. These results demonstrate the feasibility of closed-loop magnetic robot navigation for next-generation minimally invasive surgery.

This paper proposes a robust composite control strategy for constrained continuous-time nonlinear systems by integrating sliding mode control (SMC) and model predictive control (MPC). SMC enhances disturbance rejection, while MPC handles constraints by solving an optimal control problem (OCP) based on the SMC input. The resulting control input ensures both robustness and constraint satisfaction. To improve computational efficiency, the OCP is solved only at sampling instants. Recursive feasibility and closed-loop stability are rigorously analyzed, and the method’s effectiveness is demonstrated on a cart-damper-spring system.

As more Internet of Things (IoT) applications gradually move toward the cloud-edge collaborative model, the containerized scheduling of workflows extends from the cloud to the edge. However, given the high delay of the communication network, loose coupling of structure, and resource heterogeneity between the cloud and the edge, workflow containerization scheduling in the cloud-edge scenarios faces the difficulty of resource collaboration and application collaboration management. To address these two issues, we propose a KubeEdge-cloud–edge-scheduling scheme named KCES. This workflow containerization scheduling scheme includes a cloud-edge workflow scheduling engine for KubeEdge and incorporates workflow scheduling strategies for tasks’ horizontal roaming and vertical offloading. This article proposes a cloud-edge workflow scheduling model and node model, as well as a workflow scheduling engine designed to maximize cloud-edge resource utilization under the constraint of workflow task delay. A cloud-edge resource hybrid management technology is used to devise the cloud-edge resource evaluation and resource allocation algorithms to achieve cloud-edge resource collaboration. Based on the ideas of distributed functional roles and the hierarchical division of computing power, the horizontal roaming among the edges and cloud-edge vertical offloading strategies for workflow tasks are designed to realize cloud-edge application collaboration. Experimental results using a customized IoT application workflow instance demonstrate that KCES outperforms three comparing algorithms in total workflow time, average workflow time, and resource usage and features horizontal roaming and vertical offloading of workflow tasks.

This work focuses on the issue of fixed-time tracking control for a class of nonlinear systems affected by unknown uncertainties and external disturbances. First, a fixed-time neuroadaptive approximator is proposed to estimate the lumped uncertainties in nonlinear systems. Unlike existing neural network based methods, the estimation solution presented here introduces an adaptive predictor based learning mechanism, which would improve the estimation performance by removing the effect of tracking errors on the estimation process. Then, based on the reconstructed information, a fixed-time command filtered backstepping controller is developed with a fixed-time compensation system. In the compensation system, a novel bounded function is skillfully utilized such that the order and complexity of the compensation system are effectively reduced. Moreover, it is demonstrated that the designed control scheme can drive the tracking error to a small set near zero in a fixed time. Finally, the validity of the proposed control scheme is illustrated by numerical simulationsNote to Practitioners—This paper is motivated by the tracking control problem for nonlinear systems such as robotic, spacecraft, and unmanned aerial vehicle system. Existing tracking control schemes often suffer from issues such as insufficient tracking speed, redundant design process and the explosion of complexity. A fixed time neuroadaptive backstepping control scheme is proposed in this paper to ensure convergence of the error within a fixed time. An adaptive predictor-based fixed-time neuroadaptive estimator is presented to enhance the speed and accuracy of uncertainty estimation. Furthermore, a novel fixed-time compensation system is presented, which effectively addresses the issue of complexity explosion while reducing coupling of the compensation system, making the controller more concise. The effectiveness of the proposed method is validated through a numerical simulation in an uncertain spacecraft pitch motion system. Future work will involve validation of the proposed method on a hardware-in-the-loop simulation system or experiment platform.

In this paper, we propose a novel generalized discrete-time variable gain active disturbance rejection control (DTVGADRC) method for then-th order discrete-time nonlinear systems. The error-driven generalized DTVGADRC can dynamically improve the control performances, including generalized discrete-time variable gain tracking differentiator (DTVGTD), generalized discrete-time variable gain extended state observer (DTVGESO), and generalized discrete-time variable gain controller (DTVGC). Furthermore, the stability analysis of generalized DTVGADRC is performed, and the parameters in the variable gain functions are determined by the theoretical analysis. Finally, the generalized DTVGADRC method is applied to parallel teleoperated manipulators, and the experiment results are presented to illustrate effectiveness of the proposed method.

In this paper, a fixed-time dynamic surface disturbance rejection control (FTDSDRC) method is proposed to achieve desired angle tracking control performances of the pneumatic soft bending actuator. To achieve active disturbance rejection, a fixed-time disturbance observer (FTDOB) is designed to estimate the unknown disturbance in the pneumatic soft bending actuator. Then, a fixed-time dynamic surface disturbance rejection controller is proposed to achieve desired angle tracking control performances by compensating for the unknown disturbance. Finally, angle tracking control experimental results demonstrate that the proposed FTDSDRC method effective in achieving desired angle tracking control performances.

This work studies the attitude coordination tracking problem for multiple spacecraft with consideration of unintended faults (communication link faults and actuator faults), inertial uncertainties, and external disturbances under a directed communication graph. A resilient neuroadaptive distributed fixed-time control scheme is investigated to solve this challenging problem. First, an improved adaptive distributed observer is established for followers to estimate the states of the leader when considering communication link faults. The proposed observer improves the resilience against communication link faults. Subsequently, to further cope with the problem of actuator faults, inertial uncertainties, and external disturbances, based on the proposed observer and the technique of adding a power integrator, a neuroadaptive distributed fixed-time attitude coordination controller is developed. Unlike the existing controllers, the proposed one requires less information when dealing with faults and lumped uncertainties, and has a lower-computational cost. Moreover, the fixed-time stability of the closed-loop system is ensured under the designed resilient neuroadaptive distributed control scheme. Finally, comparative simulations are carried out to manifest the effectiveness of the investigated coordination control method.

The field of 3D medical image segmentation is witnessing a growing trend in the utilization of combined networks that integrate convolutional neural networks and transformers. Nevertheless, prevailing hybrid networks are confronted with limitations in their straightforward serial or parallel combination methods and lack an effective mechanism to fuse channel and spatial feature attention. To address these limitations, we present a robust multi-scale 3D medical image segmentation network, the Transformer-Driven Pyramid Attention Fusion Network, which is denoted as TPAFNet, leveraging a hybrid structure of CNN and transformer. Within this framework, we exploit the characteristics of atrous convolution to extract multi-scale information effectively, thereby enhancing the encoding results of the transformer. Furthermore, we introduce the TPAF block in the encoder to seamlessly fuse channel and spatial feature attention from multi-scale feature inputs. In contrast to conventional skip connections that simply concatenate or add features, our decoder is enriched with a TPAF connection, elevating the integration of feature attention between low-level and high-level features. Additionally, we propose a low-level encoding shortcut from the original input to the decoder output, preserving more original image features and contributing to enhanced results. Finally, the deep supervision is implemented using a novel CNN-based voxel-wise classifier to facilitate better network convergence. Experimental results demonstrate that TPAFNet significantly outperforms other state-of-the-art networks on two public datasets, indicating that our research can effectively improve the accuracy of medical image segmentation, thereby assisting doctors in making more precise diagnoses.

This article proposes a novel reinforcement learning-based model predictive control (RLMPC) scheme for discrete-time systems. The scheme integrates model predictive control (MPC) and reinforcement learning (RL) through policy iteration (PI), where MPC is a policy generator and the RL technique is employed to evaluate the policy. Then the obtained value function is taken as the terminal cost of MPC, thus improving the generated policy. The advantage of doing so is that it rules out the need for the offline design paradigm of the terminal cost, the auxiliary controller, and the terminal constraint in traditional MPC. Moreover, RLMPC proposed in this article enables a more flexible choice of prediction horizon due to the elimination of the terminal constraint, which has great potential in reducing the computational burden. We provide a rigorous analysis of the convergence, feasibility, and stability properties of RLMPC. Simulation results show that RLMPC achieves nearly the same performance as traditional MPC in the control of linear systems and exhibits superiority over traditional MPC for nonlinear ones.

In this paper, a novel generalized variable gain active disturbance rejection control (VGADRC) method is proposed for the$n$-th order nonlinear system. The generalized VGADRC, including generalized variable gain tracking differentiator (VGTD), generalized variable gain extended state observer (VGESO), and generalized variable gain controller (VGC), incorporates error-based variable gains to achieve desired control performance. Moreover, the stability analysis of VGADRC is performed, and the existence of feasible solutions are discussed and expressed explicitly. Finally, the generalized VGADRC method is applied to delta parallel manipulators, and the numerical simulation results and experiment results are presented to illustrate the effectiveness of the proposed method.

In this paper, the trajectory tracking control problem of quadrotor subject to unknown external disturbance and input constraints is addressed. In order to achieve the active disturbance rejection, a composite nonlinear extended state observer (ESO) consisting of both linear and nonlinear functions is developed, and the rigorous convergence analysis is also presented. It is worth mentioning that the proposed composite nonlinear ESO brings together the advantages of both the linear and nonlinear ESOs. Furthermore, the position controller is proposed in the nested saturation control framework, and the disturbance estimate is incorporated in the controller to reject disturbances actively, which provides a composite disturbance rejection control strategy for systems with input constraints and unknown disturbances. The domain of attraction and the rigorous stability analysis of whole system are also presented. Finally, simulation and experiment are provided to show the efficiency of the proposed methods.

In this article, a novel error-driven variable gain active disturbance rejection control (ADRC) approach is developed for pneumatic servo system, and the desired performance of trajectory tracking and the disturbance rejection can be guaranteed. The proposed variable gain ADRC includes three parts: 1) variable gain tracking differentiator (TD); 2) variable gain extended state observer (ESO); and 3) variable gain error feedback controller (EFC). The proposed variable gain TD is noise tolerant and possesses a variable gain, which can be dynamically adjusted according to the tracking error, and the performance for tracking the reference signal and extracting its derivative is improved. The variable gain ESO is also equipped with a variable gain driven by estimation errors to improve the estimation performance. Then, the variable gain EFC is further designed to improve control accuracy. Finally, simulations and experimental results are presented to verify the efficiency of the proposed methods.

This article addresses the finite-time attitude formation-containment control problem for networked uncertain rigid spacecraft under directed topology. A unified distributed finite-time attitude control framework, based on the sliding-mode control (SMC) principle, is developed. Different from the current state of the art, the proposed attitude control method is suitable for not only the leader spacecraft but also the follower spacecraft, and only the neighbor state information among spacecraft is required, allowing the resulting control scheme to be truly distributed. Furthermore, the proposed method is inherently continuous, which eliminates the undesired chattering problem. Such features are deemed favorable in practical spacecraft applications. In addition, upon using the proposed neuro-adaptive control technique, the attitude formation-containment deployment can be achieved in finite time with sufficient accuracy, despite the involvement of both the uncertain inertia matrices and external disturbances. The effectiveness of the developed control scheme is confirmed by numerical simulations.

This article develops a dynamic version of event-triggered model predictive control (MPC) without utilizing any terminal constraint. Such a dynamic event-triggering mechanism takes the advantages of both event- and self-triggering approaches by dealing explicitly with conservatism in the triggering rate and measurement frequency. The prediction horizon shrinks as the system states converge; we prove that the proposed strategy is able to stabilize the system even without any stability-related terminal constraint. Recursive feasibility of the optimization control problem (OCP) is also guaranteed. The simulation results illustrate the effectiveness of the scheme.

In this article, an event-driven output feedback control approach is proposed for discrete-time systems with unknown mismatched disturbances. To estimate the unavailable states and disturbances, a reduced-order extended state functional observer is proposed, and by introducing an event-driven scheduler, the ZOH-based event-driven output feedback disturbance rejection controller is designed, and the stability and disturbance rejection analyses are performed. To further save the network resources, the predictive event-driven output feedback disturbance rejection control approach is proposed, and the stability and disturbance rejection analyses of the systems with predictive control are also conducted. It can be shown that the disturbances are compensated completely in output channels of the systems, and compared with the time-driven control schemes. And event-triggering frequency is greatly reduced with the proposed event-driven control methods. Finally, the effectiveness of the provided control approaches is demonstrated by numerical simulations.

For wireless cyber–physical systems (CPSs) suffering jamming attacks, an optimal resilient control method is proposed through a novel cross-layer dynamic game structure in this article. To confirm to practical conditions of the cyber-layer, incomplete communication information is taken into consideration, and a Bayesian Stackelberg game approach is utilized to model interactions between a smart jammer and a cyber-user. Then, an$H_{\infty }$optimal resilient controller is studied for the closed-loop system with jam-induced packet losses and external disturbance in the sense of physical layer. With assumptions that the smart jammer has abilities of decoding system inputs and states, the changes of the jamming strategy are studied, and a coupled design between cyber and physical layers is presented with an algorithm to depict the dynamic variations of the CPSs. Moreover, the convergence of the proposed algorithm is also discussed. To validate the advantages of the proposed methods, a numerical simulation is performed in the end.

In this paper, event-triggered active model predictive control is investigated for a nonlinear multiagent system (MAS) with packet losses. By designing event-triggered mechanisms which reduce sensing cost, event-triggered conditions are detected at certain sampling instants. The prediction horizons of all agents are selected actively through the event-triggered mechanisms. Selecting a maximal predictive horizon, a common predictive horizon of the nonlinear MAS is obtained to ensure synchronous updating. The Bernoulli distributions are applied to describe the packet losses which are dealt with by model predictive control. Finally, the effectiveness of the proposed algorithm design is verified by a numerical simulation.

In this article, the problem of disturbance rejection control is studied for discrete-time systems within an event-driven control framework. Through a predefined event-driven scheduler, both full- and reduced-order extended state observer (ESO)-based output feedback controllers are designed. With the proposed ESOs, both the disturbance and the system states are estimated, and the controllers are constructed with the estimated disturbance and states. Then, the stability and disturbance rejection analyses are conducted. It can be found that with the established event-driven control approaches, the updating frequency of the controller can be prominently reduced, and the disturbance can also be compensated in the output channels of the systems. Finally, the validity of the established control approaches is illustrated by the numerical simulations.

This paper considers the co-design technique of quantization and event-driven control for reducing the negative influences of network on the system performance of networked control systems (NCSs). Event-driven strategy aims at reducing the frequency of transmission, while signal quantization reduces the amounts of data for each transmission. By combining these two techniques, this paper establishes the quantized event-driven control approaches for both the fixed and relative event threshold cases. The system stability and the existence of the minimum interevent interval (MIEI) are analyzed. Finally, the validity of the proposed approaches is illustrated by the numerical simulations.

In this paper, both a trajectory tracking controller and a position controller are investigated for leader-follower trajectory control of quadrotors. A tracking differentiator is designed to generate speed signals for the quadrotors which are composed of a leader and some followers. Based on state errors between the leader and followers, a nonlinear disturbance observer is used in the trajectory tracking controller. A saturation law with anti-trigonometric functions is adopted in the position controller for the leader and followers. Experiment results are given to show the applicability of the proposed method for quadrotors.

In this paper, a predictive cloud control scheme is investigated for a networked multiagent system (NMAS) with quantized signals under denial-of-service (DoS) attacks. An arbitrary region quantizer is applied to take effective values of control signals. A Stackelberg game is adopted to model the DoS attacks which result in attack-induced packet dropouts. Based on cloud computing and predictive control, the predictive cloud control scheme is designed to compensate for network-induced delays and the attack-induced packet dropouts actively. By a “zoom” strategy, sufficient conditions are given to achieve consensus for the NMAS. Effectiveness and computing superiority of the proposed scheme are demonstrated by simulation results.

This article proposes two kinds of terminal sliding mode control (TSMC) strategies for implementing the finite-time attitude tracking of spacecraft under environmental disturbances and model uncertainties. First, the integral disturbance observer (IDO) is designed to estimate the disturbances and uncertainties. Second, the IDO-based continuous TSMC (CTSMC) method is developed to achieve active disturbance rejection and better tracking performance. Third, to further mitigate the chattering, a modified TSMC (MTSMC) method is constructed by employing an adaptive method and incorporating a piecewise smooth function. Finally, simulations are performed to show the feasibility of the proposed TSMC laws.

In this paper, the security problem for a cloud control system (CCS) is studied. In the CCS, so-called advanced persistent threats (APTs) can be launched by a malicious attacker to reduce the quality of service of cloud and deteriorate the system performance further. To defend against APTs and create a security as a service scheme, a defender needs to allocate defense resource to different units serving to different plants for improving the overall system performance when the CCS accommodates multiple physical plants simultaneously. After observing the defender's action, the attacker decides which serve units to comprise. Considering that both defender and attacker are subject to resource constraints, the interaction of two sides is modeled by a Stackelberg game. The optimal solutions for two players under different types of budget constraints are investigated. Simulation examples and comparison results are provided to verify the main results of this paper.

Mobile crowdsensing systems aim at providing various novel sensing applications by recruiting pervasive users with mobile devices, which are now equipped with enriched built-in sensors (e.g., GPS, microphone, camera, gyroscope, accelerometer, etc.). A key factor to enable such systems is substantial participation of large amount of mobile users. In this paper, we focus on the data collection in mobile opportunistic crowdsensing, where the data can be transferred between mobile users via opportunistic device-to-device communications. The goal is to deliver the sensed data from the collector to the corresponding requester, which can maximize the collector's rewards. Here, we assume that the data collection has time-sensitive characteristics, i.e., the reward is time-sensitive. We consider selfish mobile users with rational behaviors, and propose a credit-based incentive-aware mechanism to stimulate mobile users to participate in data collection for mobile opportunistic crowdsensing. Particularly, we propose an effective mechanism to define the expected rewards for the sensed data, and formulate the sensed data trading as a two-person cooperative game, whose solution is obtained through the Nash bargaining theory. Extensive simulations based on both synthetic and real-world mobility traces are conducted to validate the efficiency of our incentive-aware mechanisms.

In this paper, networked predictive control is investigated for planar nonlinear systems with quantization by an extended state observer (ESO). The ESO is used not only to deal with nonlinear terms but also to generate predictive states for dealing with network-induced delays. Two arbitrary region quantizers are applied to take effective values of signals in forward channel and feedback channel, respectively. Based on a "zoom" strategy, sufficient conditions are given to guarantee stabilization of the closed-loop networked control system with quantization. A simulation example is proposed to exhibit advantages and availability of the results.

In this paper, event-driven is used in a networked control system (NCS) which is subjected to the effect of quantization and packet losses. A discrete event-detector is used to monitor specific events in the NCS. Both an arbitrary region quantizer and Markov jump packet losses are also considered for the NCS. Based on zoom strategy and Lyapunov theory, a complete proof is given to guarantee mean square stability of the closed-loop system. Stabilization of the NCS is ensured by designing a feedback controller. Lastly, an inverted pendulum model is given to show the advantages and effectiveness of the proposed results.

This paper addresses the sliding mode control problem for a class of Takagi-Sugeno fuzzy systems with matched uncertainties. Different from the conventional memoryless sliding surface, a memory-based sliding surface is proposed which consists of not only the current state but also the delayed state. Both robust and adaptive fuzzy sliding mode controllers are designed based on the proposed memory-based sliding surface. It is shown that the sliding surface can be reached and the closed-loop control system is asymptotically stable. Furthermore, to reduce the chattering, some continuous sliding mode controllers are also presented. Finally, the ball and beam system is used to illustrate the advantages and effectiveness of the proposed approaches. It can be seen that, with the proposed control approaches, not only can the stability be guaranteed, but also its transient performance can be improved significantly.

This paper addresses the problem of observer-based output feedback controller designs for discrete-time T–S fuzzy systems based on a relaxed approach in which the fuzzy Lyapunov functions are used. Different from the existing two-step method, a single-step linear matrix inequality method is provided for the observer-based controller design. It is shown that the controller and observer parameters can be obtained by solving a set of strict linear matrix inequalities that are numerically feasible with commercially available software. The new design method not only overcomes the drawback induced by the two-step approach but also provides less conservative results over some existing results. Finally, the effectiveness of the proposed approach is demonstrated by an example.

This paper is concerned with the network delay compensation problem for nonlinear networked control systems (NCSs). By taking full advantage of the characteristics of the packet-based transmission in NCSs, new network delay compensation approaches are proposed to actively compensate the network communication delay under the fuzzy control framework. The nonlinear plant is represented by a Takagi-Sugeno fuzzy model, and the predictive control input packets are constructed based on parallel distributed compensation technique. Both state and output feedback fuzzy delay compensation controllers are designed. Finally, two examples are provided to illustrate the effectiveness and applicability of the developed techniques.

This paper is devoted to design adaptive sliding-mode controllers for the Takagi-Sugeno (T--S) fuzzy system with mismatched uncertainties and exogenous disturbances. The uncertainties in state matrices are mismatched and norm-bounded, while the exogenous disturbances are assumed to be bounded with an unknown bound, which is estimated by a simple and effective adaptive approach. Both state- and static-output-feedback sliding-mode-control problems are considered. In terms of linear-matrix inequalities (LMIs), both sliding surfaces and sliding-mode controllers can be easily obtained via a convex optimization technique. Finally, two simulation examples and a real experiment are utilized to illustrate the applicability and effectiveness of the design procedures proposed in this paper.

This paper is concerned with theHinfinfuzzy filtering problem for a class of continuous-time fuzzy systems with time-varying delays. The objective is to design a stable filter guaranteeing the asymptotic stability and a prescribedHinfinperformance of the filtering error system. Motivated by the parallel distributed compensation technique, we design a new filter model in this paper. Filter parameter matrices can be obtained from the solution of a convex optimization problem in terms of linear matrix inequalities (LMIs). When these LMIs are feasible, an explicit expression of a desiredHinfinfuzzy filter is given. Two numerical examples are provided to demonstrate the effectiveness and less conservativeness of the proposed design approach.