Considering deferred time-varying tracking error constraint and external disturbance, a disturbance observer based-practical fixed-time formation tracking controller is designed for multiple quadrotor UAVs(QUAVs). To begin with, a shifting function is proposed to ensure that the tracking error of a class of MIMO nonlinear systems can quickly return to the constraint from the outside of constraint. At the same time, the shifting function sets the initial value to an arbitrarily small constant, which can avoid the singular problem. Then, nonlinear transformation is established to transfer the constrained problem into an unconstrained problem. Furthermore, an adaptive gain fixed-time disturbance observer (AGFXDO) is proposed to tackle the external disturbance with relaxed assumption condition. The fixed-time stability of the estimate error is proven. Based on those, a practical fixed-time distributed formation tracking control strategy is designed for the multiple QUAVs. The practical fixed-time stability of the transformed error is rigorously proven and the tracking error is guaranteed to yield the constraint and converges to an arbitrarily small vicinity of zero after prespecified time for arbitrarily given bounded initial values. At the end, the effectiveness of the developed control scheme is illustrated by the compared simulations. Through the comparative analysis of metrics integral of absolute magnitude of error (TAE), integral of time-multiplied absolute magnitude of the error (ITAE), integral of square value of controller signal (ISV), the tracking errors enter the constraint boundaries within 3 seconds and rapidly achieve convergence. Compared with the existing FXDO-based method, the proposed AGFXDO based control approach reduces 21.7% of TAE and 76.9% of ITAE, simultaneously, it maintains lower controller value as reflected in ISV.

In this paper, we address the model reduction problem for linear hybrid systems via the interconnection-based technique called moment matching. We consider two classical interconnections, namely the direct and swapped interconnections, in the hybrid setting, and we present families of reduced-order models for each interconnection via a hybrid characterisation of the steady-state responses. By combining the results for each interconnection, the design of a reduced-order model that achieves moment matching simultaneously for both interconnections is studied. In addition, we show that the presented results have simplified counterparts when the jumps of the hybrid system are periodic. A numerical simulation is finally given to illustrate the results.

It is critical for fire-rescuers to rapidly identify flammable and explosive materials around a burning building and accurately locate trapped individuals within the golden rescue period. To address this need, this paper proposes and develops FireSeer—an automated system for fire detection and unit-level localization. Deploying aerial vehicles, FireSeer assists rescue teams by quickly identifying burning units and their floor levels. Technically, the system determines the specific location of fire-affected floors through a three-stage processing pipeline consisting of a Finder, a Detector, and a Localizer. The Finder extracts key information, such as fire signatures and building corners, from onboard camera images, and isolates the building foreground via perspective transformation. Subsequently, the Detector identifies individual units on fire. In the Localizer stage, a floor classification method categorizes the detected units and outputs the specific unit number where the fire is located. Additionally, a prediction and optimization module corrects missed detections, false positives, and occlusions. Experiments using both synthetic and real-world data confirm the algorithm’s real-time feasibility on embedded devices. Results from typical scenarios show that the FireSeer prototype achieves a row prediction accuracy (RA) of over 97% and a column prediction accuracy (CA) of over 86%, with an average processing time of 2.72 ms—demonstrating the system’s high precision and lightweight efficiency.

To enhance the performance of path planning problems, this paper proposes a Multi-strategy Enhanced Dung Beetle Optimization Algorithm (FEGDBO). This algorithm integrates Tent chaotic mapping for initialization, adaptive candidate search for exploration–exploitation balance, Differential Evolution perturbation, and a Golden Sine position update strategy to enhance global optimization capability and convergence efficiency. Experimental validation shows that FEGDBO outperforms traditional optimization algorithms and various advanced improved algorithms in terms of convergence speed, solution accuracy, and overall optimization performance. As a representative application case, this paper applies FEGDBO to the global path planning problem of warehouse AGVs, and the results confirm the effectiveness and applicability of the algorithm in practical AGV path planning tasks.

This study presents the study of aerodynamic properties of a blended wing body (BWB) unmanned aerial system (UAS) with vertical takeoff and landing (VTOL) capabilities. The UAS's innovative design allows for VTOL and hovering capabilities similar to multi-copter drones, as well as cruise flying efficiency comparable to fixed-wing aircraft. The generation of lift by the complete airframe and the decrease of drag in the wing-body attachment are the major benefits of the BWB concept. Based on the calculated wing parameters, a baseline design was developed using XFLR5, and trade studies were performed to obtain the optimum tradeoff between aerodynamic performance and overall stability. The final CAD model was developed using CATIA and Plane Maker, followed by flight simulations in X-Plane 11. After evaluating performance and stability characteristics, the prototype was fabricated and integrated with electronic subsystems. The flight tests were carried out at an altitude of 2480 meter at Lete village of Mustang district, Nepal, and flight data were analyzed to study the UAS's response to a planned mission. Analysis of flight data confirmed successful VTOL-to-fixed-wing transitions exhibiting satisfactory performance and stability characteristics, demonstrating the potential of the UAS for aerial experiments, reconnaissance, and environmental monitoring missions.

To address the challenges posed by spatial constraints and fluid perturbations in underwater tunnels, this study proposes an efficient autonomous underwater vehicle (AUV) and introduces a tracking control strategy to tunnel conditions. First, the AUV integrates a modular buoyancy adjustment system, effectively mitigating the issues of large turning radii and loss of pitch control at low speeds typically encountered with conventional rudders. The Variable Look-Ahead Distance (VLAD) mechanism is integrated into the Adaptive Line-of-Sight (ALOS) guidance system to facilitate precise estimation of the angle of attack and ensure rapid convergence of tracking errors. In addition, a hyperbolic sliding mode switching controller (HSMC) is proposed, and its stability is rigorously analyzed. This approach dynamically adjusts the sliding mode surface based on the characteristics of the two hyperbolic functions, effectively reducing control buffering and improving the stability of wall tracking. Finally, the experimental results from both planar and curved wall tracking tests demonstrate that the proposed control strategy significantly reduces tracking errors while exhibiting superior resilience to interference.

This paper addresses the event-triggered feedback optimization problem for a class of nonlinear systems in strict-feedback form with dynamic uncertainties. The system has access to real-time gradients of the objective function along its output trajectory, which is a milder requirement than having access to the objective function’s analytical form. By employing backstepping and gain assignment techniques, two event-triggered mechanisms and a feedback optimization protocol are developed that construct the closed-loop system as a feedback interconnection of multiple Input-to-State Stable (ISS) subsystems. The small-gain theorem is applied to guarantee closed-loop stability, ensuring that the proposed protocol drives the system output to the optimal solution while excluding Zeno behavior. We further relax the uncertain system dynamics to allow nonvanishing terms at the equilibrium point and bounds given by nonlinear functions involving unknown parameters. Unlike existing small-gain methods for event-triggered control, the proposed approach avoids constructing set-valued maps and incorporates the optimization objective into the small-gain analysis. The effectiveness of the approach is demonstrated through its application to a source-seeking problem.

This study proposes a novel distributed Model Predictive Control (MPC) framework incorporating virtual tube constraints to address the dual challenges of safe navigation and efficient target attainment for Unmanned Aerial Vehicle (UAV) swarms in complex low-altitude traffic environments. An adaptive neighbor interaction mechanism is proposed that dynamically adjusts based on real-time perception and communication capabilities, enabling effective swarm coordination regardless of communication availability. Then, a multi-objective optimization formulation is constructed integrating maximum velocity pursuit, trajectory tracking precision and lateral deviation minimization within virtual tube boundaries. To ensure the stability of terminal velocity, an adaptive speed weight adjustment mechanism was specifically designed. Thus, a computationally efficient distributed MPC is designed utilizing dynamic local state information on switching topology. Through rigorous Lyapunov-based stability analysis and constraint satisfaction proofs, we establish the theoretical guarantees for both asymptotic stability and recursive feasibility of the proposed algorithm. Extensive numerical simulations demonstrate superior performance in complex traffic scenarios, showing faster target convergence while maintaining safe inter-agent distances and lateral deviation margins across various communication-restricted conditions.

This paper presents the development of a virtual traffic island-based approach for Urban Airspace Management (UAM) system to resolve simultaneous conflicts. Increasing usage of Unmanned Aerial Vehicle (UAV)-based transportation in urban environments requires a high-density conflict management solution that is both safe and implementable in real time. Existing state-of-the-art methods, including Control Barrier Function (CBF)-based approaches, can guarantee safety, but are not scalable when the number of UAVs is larger. In high-density traffic scenario, the simultaneous existence of multiple conflicts makes real-time execution of conflict resolution a challenging problem. This paper presents a scalable, safe, and implementable dynamic conflict resolution approach called the Virtual Traffic Island (VTI) approach for urban airspace management. The novel VTI approach employs an evolving virtual traffic island with changing radii to handle multiple conflicts simultaneously. In the proposed approach, since each UAV in conflict avoids a single virtual obstacle by solving a CBF-based quadratic programming (QP) problem, the computational requirement for each UAV remains the same, resulting in its easy scalability and safety. The proposed method has been validated for dynamic conflict resolution of a growing number of UAVs in the urban airspace. To validate the implementation aspects of the proposed approach in real time, a software-in-the-loop (SITL) simulation has also been presented. Furthermore, a comparison with other well-known existing methods shows that the novel VTI outperforms these in terms of UAVs reaching their goals and reduces computational complexity, resulting in an easier implementation.

In obstetric anesthesia, maintaining the stability of maternal hemodynamic systems is crucial for the safety of both maternal and fetal well-beings. To achieve this goal, manual infusion method is usually used that infuses phenylephrine to patients during surgery. However, traditional manual infusion method may lead to insufficient precision in the dosage. Although automatic control techniques may improve the accuracy of infusion, the implementation of closed-loop pressure control systems usually depends on the full measurement of states, which is hard or impossible to acquire. In this paper, based on auto-regressive with exogenous inputs models, an output-feedback optimal controller is developed that integrates a state reconstruction technique and an optimal state-feedback control policy. The proposed method effectively compensates for hypotension induced by bupivacaine through precise infusion of phenylephrine. Simulation results demonstrate that the proposed control method can efficiently regulate mean arterial pressure of patients.