In this article, a prescribed-time output feedback controller is proposed for a class of uncertain nonlinear systems with unknown control coefficients and mismatched nonvanishing disturbances. Both unknown control coefficients and mismatched disturbances are tricky to address by the existing prescribed-time output feedback control frameworks. Differently, a novel prescribed-time control criterion in conjunction with Nussbaum functions is proposed, and prescribed-time stability is achieved. Furthermore, design methods for a state observer and a prescribed-time output feedback controller are developed. With the proposed control design, both the system output and observer errors are rigorously proved to converge to zero within a prescribed time. Moreover, the unified prescribed performance (UPP) of the system output and the satisfaction of output constraints are simultaneously achieved. Numerical simulations and experiments are provided to illustrate the effectiveness of the proposed control design.

Extended Reality (XR), including virtual, augmented, and mixed reality, provides immersive and interactive experiences across diverse applications, from VR-based education to AR-based assistance and MR-based training. However, widespread XR adoption remains limited due to two key challenges: 1) the high cost and complexity of authoring 3D content, especially for large-scale environments or complex interactions; and 2) the steep learning curve associated with non-intuitive interaction methods like handheld controllers or scripted gestures. Generative AI (GenAI) presents a promising solution by enabling intuitive, language-driven interaction and automating content generation. Leveraging vision-language models and diffusion-based generation, GenAI can interpret ambiguous instructions, understand physical scenes, and generate or manipulate 3D content, significantly lowering barriers to XR adoption. This paper explores the integration of XR and GenAI through three concrete use cases, showing how they address key obstacles in scalability and natural interaction, and identifying technical challenges that must be resolved to enable broader adoption.

This article focuses on the adaptive predefined-time control of plug-and-play spacecraft for constrained-attitude reorientation. Employing a singularity-free sliding manifold that simultaneously ensure the attitude constraints and the predefined-time convergence of attitude error, we architect a codesigned control scheme integrating neural network and event-triggered strategies. Compared to existing methods in plug-and-play spacecraft attitude control, the proposed scheme ensures guaranteeing predefined-time stabilization under uncertain dynamics and partial actuator failures, strict compliance with complex attitude constraints during rapid maneuvers, and reduced communication burden via event-triggered control. The simulation results validate the controller's effectiveness within constrained operational scenarios.

We investigate security-reliability tradeoff (SRT) for a non-orthogonal multiple access (NOMA)-aided cooperative network, where an internal untrusted far user (UFU) intercepts transmission from source to trusted near user (TNU), and an external eavesdropper (Eve) overhears transmission from source to UFU and TNU. To prevent eavesdropping, an artificial noise (AN)-aided network coding (ANANC) scheme is presented and exact expressions of outage probabilities (OPs), intercept probabilities (IPs), and transmission secrecy outage probabilities (TSOPs) are derived for NOMA users. For comparison, the simulation results of AN without network coding (ANWNC) scheme and non-AN (NAN) scheme are presented. Numerical results show that: 1) the IPs of TNU by the ANANC scheme is lower than that by the ANWNC and NAN schemes slightly deteriorating OP; 2) UFU by the ANANC, ANWNC, and NAN schemes achieves almost the same OP and NAN scheme performs the worst IP performance; 3) SRT of TNU by the ANANC scheme is superior to ANWNC and NAN schemes without damaging SRT for UFU compared to the ANWNC scheme but being better than that of the NAN scheme; 4) secrecy energy efficiency (SEE) of TNU by the ANANC scheme performs better than that of the ANWNC and NAN schemes but has a negligible impact on UFU in a high transmit power region, while both NOMA users have comparable SEE to ANWNC and NAN schemes in a low transmit power region.

Immersive applications such as virtual and augmented reality impose stringent requirements on frame rate, latency, and synchronization between physical and virtual environments. To meet these requirements, an edge server must render panoramic content, predict user head motion, and transmit a portion of the scene that is large enough to cover the user viewport while remaining within wireless bandwidth constraints. Each portion produces two feedback signals: prediction feedback, indicating whether the selected portion covers the actual viewport, and transmission feedback, indicating whether the corresponding packets are successfully delivered. Prior work models this problem as a multi-armed bandit with two-level bandit feedback, but fails to exploit the fact that prediction feedback can be retrospectively computed for all candidate portions once the user head pose is observed. As a result, prediction feedback constitutes full-information feedback rather than bandit feedback. Motivated by this observation, we introduce a two-level hybrid feedback model that combines full-information and bandit feedback, and formulate the portion selection problem as an online learning task under this setting. We derive an instance-dependent regret lower bound for the hybrid feedback model and propose AdaPort, a hybrid learning algorithm that leverages both feedback types to improve learning efficiency. We further establish an instance-dependent regret upper bound that matches the lower bound asymptotically, and demonstrate through real-world trace driven simulations that AdaPort consistently outperforms state-of-the-art baseline methods.

This brief presents a systematic overview on the development for distributionally robust model predictive control (DRMPC). The existing works on robust model predictive control (RMPC), stochastic model predictive control (SMPC), and DRMPC are introduced and their distinctive features have been summarized. Finally, promising future research directions are discussed.

This paper studies the physical-layer security (PLS) for an uncrewed aerial vehicle (UAV)-aided non-orthogonal multiple access (NOMA) network, where transmissions from a base station (BS) to a trusted near user (TNU) and an untrusted far user (UFU) are assisted by an UAV relay in the face of multiple non-colluding external eavesdroppers (Es). The traditional strategies such as artificial noise-aided friendly jammer (TAN-FJ), artificial noise with non-friendly jammer (TAN-NFJ), and reconfigurable intelligent surface (RIS)-aided schemes fail to simultaneously guarantee the secrecy for both TNU and UFU, thus how to guarantee the PLS for NOMA users becomes an urgent issue to be solved. In this context, we propose an artificial noise (PAN) scheme aided with digital network coding (DNC). Specifically, the artificial noise signals generated by UAV are resorted to encrypt confidential signals with the assistance of DNC, which realizes one-time pad, thus the PLS of TNU and UFU can be ensured. We resort security-reliability tradeoff (SRT) as a metric to evaluate the PLS for our proposed PAN scheme. Hence, the exact and asymptotic expressions of outage probability (OP) and intercept probability (IP) are derived to quantify the reliability and security. Moreover, numerical simulations validate the correctness of our theoretical results and reveal that: 1) the PAN scheme enhances the SRT performance of near user compared to the TAN-FJ, TAN-NFJ, and RIS-aided schemes, while far user by the PAN scheme achieves almost the same SRT performance as the TAN-FJ scheme and significantly better performance than the TAN-NFJ and RIS-aided schemes, which indicates that the PAN scheme realizes the fairness of secrecy transmissions for NOMA users; 2) the SRT performance of TNU benefits from multiple antennas at BS and UAV, which has ignorable effect on the SRT performance of UFU; 3) the antenna number at UAV has more obvious impact on the SRT of TUN than the antenna number at BS.

This paper proposes a discrete-time high-order fully actuated system based robust control method to address the stability and trajectory tracking problem for robotic manipulators with input saturation. The method first transforms the dynamic model of the robotic manipulator into a high-order fully actuated system model, and then designs a robust control law that ensures the asymptotic stability of the closed-loop system using Lyapunov stability theory, without imposing strict constraints on system uncertainties. Compared with existing studies, this work explicitly considers the practical constraint of input saturation and proposes a concise and effective saturation compensation mechanism, enhancing the methods applicability in real-world scenarios. The effectiveness of the proposed method is validated through numerical simulations on a two-degree-of-freedom robotic manipulator. The experimental results demonstrate that the system can achieve fast response and high-precision trajectory tracking. This study provides a new theoretical framework and methodological support for robust control of discrete-time nonlinear systems.

This article studies the global asymptotic neural network (NN) tracking problem for full-state error constrained spacecraft attitude systems with actuator faults, inertia uncertainties, and external disturbances. In the literature, most existing NN control schemes can only achieve semiglobally bounded stability since the approximation capability of NNs is confined to a compact domain called the approximation domain. Differently, an attitude tracking control strategy in conjunction with a modified smooth switching mechanism is proposed to ensure the global asymptotic stability. Specifically, an adaptive NN controller is developed within the approximation domain to address unknown nonlinearities, and a robust controller is activated outside the approximation domain to drive back the system states. With the proposed design, both attitude and angular velocity errors (collectively defined as the full-state errors) are rigorously proven to globally asymptotically converge to zero. Moreover, the full-state errors are preserved within the unified prescribed performance constraints, which are uniform with respect to any initial conditions, thereby eliminating the requirement for offline computation of the performance boundary. In addition, the undesirable feasibility conditions on virtual control laws are completely eliminated. Theoretical analysis and numerical simulations validate the effectiveness of the proposed method.

In this article, we propose a self-triggered distributionally robust model predictive control algorithm for linear discrete systems with state chance constraints and unbounded stochastic disturbances. Assuming that only the first and second moments of the disturbance are accessible, we transform the objective function into a compact quadratic form and reformulate the state chance constraints into linear inequalities, which is more tractable when solving. In order to reduce communication and sampling times of the system, we propose a self-triggered update scheme, in which the state sampling and the control input sequence are updated when the control performance predicted based on the current sampling exceeds that of the periodic sampling scheme. We demonstrate that the optimization problem in the proposed self-triggered model predictive control (MPC) method is recursively feasible and stable. Numerical simulation results verify the effectiveness of the proposed algorithm.

This paper investigates the physical layer security for a rate-splitting multiple access (RSMA)-based hybrid satellite-terrestrial relay network (HSTRN) under the joint impact of hardware impairments (HIs) and channel estimation errors (CEEs). In the proposed RSMA-based HSTRN, a satellite communicates with multiple users via terrestrial relays in the presence of an eavesdropper. In order to improve the secrecy performance, we propose two relay selection schemes, namely, partial relay selection (PRS) and two-stage relay selection (TSRS). We derive exact closed-form expressions of outage probability (OP), intercept probability (IP), and effective secrecy throughput (EST) for the PRS and TSRS schemes. To further enhance the secrecy performance of proposed system, the relay selection schemes are extended to jammer-assisted HSTRN, and we propose the jammer aided PRS (JPRS) and jammer aided TSRS (JTSRS) schemes. The exact closed-form OP, IP, and EST expressions for the JPRS and JTSRS schemes are also obtained. Numerical results show that in the high signal-to-noise ratio (SNR) region, the TSRS scheme outperforms the PRS scheme in terms of EST, while in the low SNR region, the result is opposite. Additionally, in the high SNR region, the secrecy performance of jammer-aided relay selection schemes, i.e., JPRS and JTSRS schemes, outperform relay selection schemes without the support of a jammer, i.e., PRS and TSRS schemes. Moreover, it shows that the RSMA-based HSTRN achieves better secrecy performance than the non-orthogonal multiple access (NOMA)-based HSTRN in high SNR region.

This paper studies the neuroadaptive attitude tracking problem for robot manipulators systems with asymmetric time-varying full-state constraints, model uncertainties, and external disturbances. Most existing robot manipulator attitude control works are constructed through the state-space approach. In this paper, a neuroadaptive attitude control strategy is developed based on the fully actuated system (FAS) approach, which has shown its simplicity and flexibility for controller design of nonlinear systems. We adopt adaptive multilayer neural network (NN) to tackle lumped uncertainties due to the model uncertainties and external disturbances. On this basis, a nonlinear transformation is further integrated into the FAS approach-based control design to achieve exact asymptotic convergence while satisfying full-state constraints. The attitude tracking error is rigorously proven to asymptotically converge to zero, which is a notable improvement over existing NN control works. Moreover, using the FAS approach, stability analysis and controller design are much simpler compared to the conventional backstepping or sliding mode design. Stability analysis and numerical simulations validate the control performance of the proposed control strategy.

This correspondence investigates the secrecy energy efficiency (SEE) issue with the assistance of a reconfigurable intelligent surface (RIS). More particularly, we consider the downlink communications of multiple-input-multiple-output wireless networks, where multiple active colluding and non-colluding eavesdroppers (Es) attempt to wiretap the legitimate transmissions. To evaluate the SEE performance of the system, we introduce two distinct schemes: the SEE maximization based colluding eavesdropping (SEEM-CE) scheme and the SEE maximization based non-colluding eavesdropping (SEEM-NCE) scheme. Then, the proposed optimization problem is decomposed into two sub-problems, namely the base station (BS) precoding vectors optimization sub-problem and the RIS's phase shifters adjustment sub-problem. To be specific, for the first sub-problem, the BS precoding vectors are derived through the Dinkelbach's method and successive convex approximation (SCA) strategy. For the second sub-problem, an SCA-based approach is developed to optimize the RIS phase shifts. Building on these solutions, a block coordinate descent algorithm is proposed to alternately optimize the BS precoding vectors and RIS phase shifts, thereby addressing the original problem. Finally, numerical simulations demonstrate that the proposed SEEM-NCE algorithm achieves superior performance compared to the SEEM-CE scheme and other benchmark methods.

To address the inadequate coverage of fixed-orbit constellations for emergent tasks, this paper proposes a Multi-Stage Integrated Optimization Framework (M-IOF) that couples discrete task assignment with continuous orbital maneuver planning. In this framework, candidate task chains generated by the scheduler are evaluated through inverse orbit design, and the corresponding feasibility information is fed back to the fitness function. Spatial-functional task clustering is first used to reduce the dimensionality of large-scale heterogeneous tasks. Then, a target-driven inverse orbit solving method maps spatio-temporal constraints into feasible resonant phasing conditions. By considering J2 perturbations, a one reconfiguration maneuver plan strategy is formulated, enabling satellites to sequentially observe multiple targets through passive drift after one initial maneuver. The scheduling model is solved using an adaptive genetic algorithm with feasibility penalties. Comparative simulations under three task-structure scenarios show that M-IOF improves target completion and task efficiency while reducing unnecessary maneuvering. The results demonstrate the effectiveness of the proposed framework for responsive constellation reconfiguration.

The constrained combinatorial multi-armed bandit model has been widely employed to solve problems in wireless networking and related areas, including the problem of wireless scheduling for throughput optimization under unknown channel conditions. Most work in this area uses an algorithm design strategy that combines a bandit learning algorithm with the virtual queue technique to track the throughput constraint violation. These algorithms seek to minimize the virtual queue length in their algorithm design. However, in networks where channel conditions change abruptly, the resulting constraints may become infeasible, leading to unbounded growth in virtual queue lengths. In this paper, we make the key observation that the dynamics of the head-of-line age, i.e. the age of the oldest packet in the virtual queue, make it more robust when used in algorithm design compared to the virtual queue length. We therefore design a learning-based scheduling policy that uses the head-of-line age in place of the virtual queue length. We show that our policy matches state-of-the-art performance under i.i.d. network conditions. Crucially, we also show that the system remains stable even under abrupt changes in channel conditions and can rapidly recover from periods of constraint infeasibility.

Multi-unmanned aerial vehicle (UAV) formation cooperation is essential for complex missions where single-UAV systems fall short. To address the inherent challenges of free-final-time optimization and continuous-time state constraints in fixed-wing UAV swarm reconfiguration, this paper proposes a trajectory optimization framework based on an exact penalty function. First, a 3-degree-of-freedom (3-DOF) dynamic model is established, incorporating strict constraints for collision avoidance, communication connectivity, obstacle avoidance, and terminal formation geometries. By integrating control parameterization with time-scale transformation, the infinite-dimensional optimal control problem is transcribed into a finite-dimensional nonlinear programming (NLP) problem. This approach eliminates heuristic weight tuning and efficiently resolves free-final-time variables. Numerical simulations in symmetric and asymmetric obstacle environments demonstrate that the proposed method consistently generates smooth, safe, and time-optimal trajectories while maintaining formation stability, thereby advancing constrained optimal control theory for UAV swarms.

This paper investigates the trajectory tracking problem for robotic manipulators subject to external disturbances and model uncertainties. A control framework is proposed that ensures the tracking error converges within a user-specified timeframe. The approach builds upon the theory of fully-actuated systems (FAS) and integrates a novel sliding manifold endowed with predefined-time stability characteristics. A corresponding sliding mode controller (SMC) is designed, and its closed-loop stability along with the prescribed convergence time is formally established through Lyapunov analysis. The effectiveness of the proposed method is validated via experimental trials conducted on a Franka Emika Panda robot.

This paper investigates the physical layer security for a cognitive satellite-terrestrial Internet-of-Things (IoT) network where a secondary base station (BS) communicates with multiple secondary users (Us) in the presence of an adaptive eavesdropper, which performs eavesdropping when the channel quality of the eavesdropping link is good and performs jamming otherwise. We consider a real situation where mutual interference exists between primary and secondary networks. Considering multiple secondary users available, we propose suboptimal user scheduling and optimal scheduling schemes to improve the secure transmission of BS-Us. Specifically, a user that maximizes the secrecy capacity of the system will be selected in the optimal scheduling scheme, which assumes that the channel state information (CSI) of all links is available. By contrast, the suboptimal scheduling scheme only knows the main links’ CSI, so the user maximizing the CSI of the main links is chosen. We derive the exact and asymptotic closed-form secrecy outage probability expressions for suboptimal and optimal scheduling schemes. For comparison, we also obtain the exact and asymptotic closed-form secrecy outage probability expressions for round-robin scheme. Numerical results show that the suboptimal and optimal user scheduling schemes perform better than round-robin user scheduling scheme in terms of secrecy outage probability. In addition, the secrecy performance of proposed suboptimal and optimal schemes can be improved by increasing the number of secondary users.

This paper proposes a data-driven attitude control method for spacecraft subject to angular velocity constraints, where only a limited set of input-output measurements from the unknown dynamics is available. Traditional model-based approaches often struggle with unmodeled dynamics and state constraints. To address this issue, we develop a stochastic model predictive control (SMPC) framework based on data-driven system identification. First, under the Koopman operator framework, a linear lifted-state model with uncertainty is constructed from data using the extended dynamic mode decomposition (EDMD) method. To capture the residual error of this approximation, Gaussian process regression (GPR) is employed to probabilistically characterize the model mismatch, delivering state- and control-dependent estimation of the mean and covariance over the prediction horizon. These estimations are incorporated into an SMPC optimization that enforces chance constraints on angular velocity and control torques, maintaining the probability of constraint violation below a specified threshold. The numerical simulations validate the utility of the proposed data-driven SMPC algorithm, demonstrating reliable and accurate attitude tracking while handling system uncertainties and constraints.

This article investigates the covertness and reliability of uncrewed aerial vehicle (UAV) relay communications assisted by a friendly jammer in the presence of co-channel interference (CCI). In the considered scenario, communication from a source to a destination is relayed by a UAV, while a malicious node attempts to monitor the transmission behaviors of both the source and UAV. To enhance the system's covertness, we propose a friendly jammer-aided covert communication (FJACC) scheme, which leverages a friendly jammer to degrade the detection capability of the malicious monitoring node by sending the artificial noise. We analyze the covertness and reliability of the FJACC scheme by deriving closed-form expressions of the outage probability (OP) and detection error probability (DEP) under CCI, considering that the wireless channels are characterized by Nakagami-m fading for UAV-to-ground links and Rayleigh fading for terrestrial links. Numerical results confirm the superior covertness of the proposed FJACC scheme over the traditional nonfriendly jammer aided transmission (NFJAT) scheme. Furthermore, increasing the number of co-channel interferers or their transmit power exhibits a dual effect: it enhances covertness at the expense of reliability.