Simulation Examples Research Articles

Introduction: Swarm debrief is one of the Patient Safety Incident Response Framework (PSIRF) learning response methods [1]. It is a group debrief aimed at fostering collective, system-based learning, used immediately after any event where there is something new to learn. During the implementation of PSIRF in our trust, a gap in Swarm debriefing skills was identified, and the simulation and human factors team was asked to provide educational support. In collaboration with the patient safety team and input from the NHS England PSIRF team, we developed a systems-based Swarm guide and an accessible, engaging audio-visual (AV) Swarm simulation to illustrate a more realistic ‘work-as-done’ example [2]. Methods: A fictional patient incident was scripted, drawing inspiration from recent incident reviews and our own collective clinical experiences as healthcare professionals. The script mirrored the debriefing prompts and system-based questions within the Swarm guide so that viewers could review the guide and video concurrently. The video, featuring a nurse manager, doctor, nurse, and healthcare assistant, depicts a simulated Swarm debrief held in the manager’s office (Figure 1). Filmed on a smartphone and edited using Mac and CapCut software, the 15-minute video was enhanced with subtitles to improve accessibility and engagement. The video has been shown to over 100 learning response leads as part of their formal Swarm debriefing training. The Swarm guide and video link are also hosted on our website and are freely available on YouTube, making these resources accessible to a wider audience. Results: Participants in the Swarm debrief training filled out a post-course survey, where 96.67% rated the video as “very helpful” in enhancing their understanding of a Swarm debrief. Participants described the video as “relatable,” “clear,” and “confidence-building,” noting that it helped clarify the process and provided a relevant and safe example for discussion. Faculty observed that the use of the video within the course enhanced participant engagement and reflective practice. Discussion: This AV simulated example of a Swarm debrief demonstrates how low-cost, low-tech media can be produced to enhance staff education and support PSIRF implementation. Video-based learning offers a powerful modality for teaching these complex skills, allowing learners to observe key interactions directed by the Swarm guide and reflect on the process [3]. This video provides a clear example of how a Swarm debrief should unfold in the real world, making this abstract concept more tangible. Ethics Statement: As the submitting author, I can confirm that all relevant ethical standards of research and dissemination have been met. Additionally, I can confirm that the necessary ethical approval has been obtained, where applicable.

Event-triggered ADP-based tracking controller for partially unknown nonlinear uncertain systems with input and state constraints.

Sports injury surveillance programs have been vital in advancing the understanding of injury epidemiology across various athlete populations. Surveillance-based epidemiological measures of injury occurrence are ubiquitous in the sports medicine literature, and the injury rate is one such commonly used measure. Traditional approaches to calculating injury rates have predominantly relied on frequentist methods, which, while informative, have limitations in addressing certain practical questions. We explore an alternative Bayesian framework for analyzing injury rates, highlighting its potential to enhance sports medicine practice. We delineate the practical implications of adopting a Bayesian approach, contrasting key analytical outputs such as credible intervals with their frequentist counterparts. Through simulated and real-world examples, we demonstrate the types of analyses and inferences that are only possible within this framework. We particularly discuss how Bayesian methods allow for direct calculation of probabilities for specific outcomes and provide intuitive interpretations of uncertainty. We discuss the computational and inferential advantages of the Bayesian approach, illustrating how it can offer more nuanced insights into injury incidence in sport injury epidemiology.

This paper develops the linear-algebraic foundations of the Convex Least Squares Programming (CLSP) estimator and constructs its modular two-step convex optimization framework, capable of addressing ill-posed and underdetermined problems. After reformulating a problem in its canonical form, A(r)z(r)=b, Step 1 yields an iterated (if r>1) minimum-norm least-squares estimate z^(r)=(AZ(r))†b on a constrained subspace defined by a symmetric idempotent Z (reducing to the Moore–Penrose pseudoinverse when Z=I). The optional Step 2 corrects z^(r) by solving a convex program, which penalizes deviations using a Lasso/Ridge/Elastic net-inspired scheme parameterized by α∈[0,1] and yields z^*. The second step guarantees a unique solution for α∈(0,1] and coincides with the Minimum-Norm BLUE (MNBLUE) when α=1. This paper also proposes an analysis of numerical stability and CLSP-specific goodness-of-fit statistics, such as partial R2, normalized RMSE (NRMSE), Monte Carlo t-tests for the mean of NRMSE, and condition-number-based confidence bands. The three special CLSP problem cases are then tested in a 50,000-iteration Monte Carlo experiment and on simulated numerical examples. The estimator has a wide range of applications, including interpolating input–output tables and structural matrices.

This paper focuses on the time-varying distributed optimization problem (DOP) for multi-agent systems (MASs) in predefined time over an undirected graph, where inequality constraints and external disturbances are introduced in system models. First, a novel result with respect to the stability in predefined time is developed for nonlinear systems by adapting hyperbolic function, where the settling time could be preset freely by users. Second, a piecewise distributed sliding-mode control protocol, which consists of hyperbolic function and barrier penalty function, is designed by doing the segmentation for the time interval on the predefined time. Under the proposed control protocol, the states of all agents are driven to the designed sliding-mode surface, and realize the consensus in predefined time. Moreover, the consensus state converges to the optimal solution of DOP in predefined time. Finally, the effectiveness of the proposed optimization control protocol is verified by applying a simulation example.

This paper investigates the state estimation problem for a class of two time-scale cyber-physical systems. A binary symmetric channel (BSC) associated with the binary encoding transmission (BCT) scheme is highlighted to enhance the reliability of data transmission. While binary bit strings are being transmitted through memoryless BSC, random error codes due to channel noise may occur. In light of this, a novel BCT-based state estimator is put forth, guaranteeing that the estimation error exponentially ultimately bounded in the mean square sense. To alleviate the numerical stiffness resulting from the two-time-scale property, a novel parameter-based Lyapunov function is developed, along with corresponding parameter-based criteria. Finally, a simulation example is provided to confirm that the theoretical results are valid.

Event-triggered prescribed-time control for nonlinear systems with time-varying parameters and unknown control directions.

With the continuous advancement of China’s new-type power system construction, wide-range-frequency oscillation accidents in the power grid have become frequent, characterized by multiple modal components and a wide frequency range. Due to the nonlinearity and coupled time-varying characteristic of wide-range-frequency oscillations, it is difficult to accurately identify parameters. Therefore, this paper proposes an improved particle swarm optimization (PSO)–variational mode decomposition (VMD) method for identifying wide-range-frequency oscillations. First, through improved PSO, the number of modal and secondary penalty factors of the VMD are self-optimized. Energy loss is used as the fitness function, and optimum is achieved through dynamic adjustment of the inertial factor of the particle swarm algorithm. Second, the wide-range-frequency oscillation signal undergoes VMD based on the number of obtained modals and the secondary penalty factor. The effective and noisy modal components are separated using the correlation coefficient approach, and signal reconstruction is utilized to reduce noise. Finally, simulation examples were used to verify the feasibility and effectiveness of the method proposed in this paper. Simulation results demonstrate that the proposed method can capture all wide-band-frequency oscillation information of the signal with an identification error below 2%. It provides a theoretical basis and technical support for wide-band-frequency oscillation traceability and mitigation.

This paper presents an adaptive fixed-time fault-tolerant control scheme for nonstrict-feedback nonlinear systems subject to actuator faults, input saturation, and external disturbances. Radial basis function neural networks approximate unknown nonlinear functions within the system. To address the complexity typically associated with the traditional backstepping design, command filtering is introduced, significantly simplifying the control development process. An error compensation technique is further incorporated to reduce the impact of filtering-induced errors on system performance. To effectively handle input saturation, a smooth non-affine approximation is utilized to represent the saturation behavior. The proposed controller is developed using Lyapunov-based analysis and backstepping design, ensuring both transient and steady-state tracking objectives are met. A funnel constraint mechanism is integrated to maintain the tracking error within predefined performance bounds. Theoretical analysis confirms the fixed-time stability of the closed-loop system, and simulation examples are provided to demonstrate the effectiveness and feasibility of the proposed approach.

This paper considers the fixed-time H ∞ stabilization for p -normal form nonlinear systems with external disturbances. A special local smooth nonlinear function (LSNF) is introduced, and a new lemma about the LSNF is given first. Based on the lemma, the fixed-time control and the H ∞ control, a novel improved H ∞ control strategy is proposed, and the robust fixed-time H ∞ controller is obtained, which presets the upper bound of the cost function (UBCF) of the output. It should be emphasized that the preseted UBCF is completely independent of the disturbances and even the system initial values rather than dependent on the disturbances like most general H ∞ control strategies, which greatly enhances the robustness of the H ∞ control. Moreover, the controller results in a globally fixed-time stable closed-loop system when the external disturbances are nonexistent. Finally, to prove the effectiveness and the practicability of the improved H ∞ control strategy, two simulation examples, including a planetary gear-type inverted-pendulum (PGTIP), are given.

Motivated by the emerging mixed autonomous paradigm in cobotic order picking operations, we investigate the optimal information design to navigate human workers (HWs) who cooperate with autonomous mobile robots (AMRs) within an intralogistics system. We incorporate asymmetric information between AMRs and HWs in a routing game where connected AMRs are informed of the congestion state while HWs rely on information provided by the system. The system designs a communication policy aiming to navigate HWs away from congestion. Without strategic communications, we show that the deployment of AMRs cannot mitigate congestion unless the automation level reaches a threshold. Interestingly, we illustrate a substitution effect between automation and strategic communications when information distortion is mild. In contrast, severe information distortion complements automation due to exacerbated congestion. Furthermore, an in‐house AMR fleet is economically more efficient than a third‐party logistics service. Consequently, in‐house automation can be achieved with mild information distortion, while severe information distortion is required to complement the lack of efficiency in the third‐party AMR fleet. With simulated numerical examples to complement the analytical results, we provide managerial insights concerning the optimal information policies under different levels of automation, guiding warehouse managers in their communications with workers to achieve the best performance of the cobotic system.

This article deals with the output regulation problem with output constraint for uncertain nonlinear systems with unknown control directions. An output feedback backstepping control and an internal model integrating root-type barrier Lyapunov function are designed. The root-type barrier Lyapunov function is used for the first time in designing an adaptive controller to deal with the output constraint of the output regulation problem, which ensures that the system output cannot violate the given boundary. Compared with other barrier Lyapunov functions, the key advantage lies in its polynomial-form derivatives, which avoid singularity issues near constraint boundaries. Nussbaum functions are utilised to address the problem of unknown directions. It is proven that under the adaptive controller via a novel algorithm, all signals of the closed-loop system are ultimately uniformly bounded(UUB), and the tracking error remains strictly within pre-given constraint intervals. Finally, the effectiveness of the proposed method is shown by two simulation examples.

ABSTRACT Underactuated wheeled robots (UWRs) are widely used in industrial scenarios. Due to the strong coupling relationship and underactuation of UWRs, it is critical to deal with the coupling terms, as well as disturbances at various locations and the convergence time. This paper proposes a prescribed‐time immersion and invariance‐based tracking control strategy. By designing a fully actuated target system and immersion condition, the underactuated control problem is transformed into an easier‐to‐handle fully actuated issue. The prescribed‐time technique ensures the convergence time of the off‐the‐manifold coordinates and tracking errors. On this basis, a prescribed‐time adaptive anti‐disturbance control strategy based on I&I is proposed via a dual‐terminal self‐triggered mechanism. With theoretical analysis, the effectiveness of the strategy is proven, and its feasibility is demonstrated through a simulation example.

For a class of strict-feedback nonlinear systems with external disturbances, the problem of finite-time prescribed performance tracking control is investigated based on a novel disturbance observer. For the first time, a novel neural network disturbance observer is proposed, which can precisely observe disturbances and limit the observation errors within an extremely small boundary. Second, under the framework of the backstepping method, combined with the finite-time prescribed performance function, a finite-time prescribed performance tracking controller is derived. This controller ensures that the output tracking error converges to the prescribed boundary range within a finite-time and all the signals in the closed-loop system remain bounded. Meanwhile, a command filter and a compensation mechanism are adopted to address the issues of “differential explosion” and filtering errors. Finally, the simulation examples verifiy the effectiveness of the design scheme.

This paper proposes an active disturbance rejection and fuzzy impedance control (ADRFIC) framework for two-degrees-of-freedom (2-DOF) lower limb exoskeleton robots. By utilising the differential flatness theory, the underlying robotic system is decomposed into two subsystems that are easy to control. On this basis, extended state observers (ESOs) are designed individually to realise disturbance estimation for each subsystem. Considering that human-robot interaction can cause obvious observation error, nonlinear state error feedback-based fuzzy impedance controllers (NSEF-FICs) are designed. The proposed ADRFIC framework is composed of tracking differentiators (TDs), ESOs and NSEF-FICs. A simulation example is finally given to demonstrate the excellent trajectory-tracking and impedance performance of the proposed control framework. And this framework can be easily generalised to robotic systems with more DOFs.

This paper mainly deals with the distributed group bipartite consensus control problem for heterogeneous multi-agent systems(MASs) in signed graphs. The systems under consideration consist of first- and second-order agents, and can be divided into multiple groups in which agents can either cooperate or compete. Particularly, competition can exist not only between different groups, but also within the same group. Due to actuator saturation, a control protocol employing hyperbolic tangent function is developed by utilising the nearest neighbour role. Using the graph theory and the Lyapunov stability theory, it is shown that under the structurally balanced and unbalanced topologies, the group bipartite consensus and the group consensus can be reached respectively. Finally, the performance of the proposed protocol is illustrated by a simulation example.

This paper addresses the challenging problem of achieving sampled-data, velocity-free consensus for multiple Euler-Lagrange systems under irregular communication delays. While passivity-based control (PBC) is a powerful framework for such systems, existing works fundamentally require continuous feedback from neighbors, as their stability proofs cannot handle the discontinuous right-hand-side dynamics generated by sampled-data and abrupt delays. This limitation renders conventional PBC methods inapplicable in many realistic networked scenarios. This work bridges that theoretical gap by introducing a novel control and analysis method. Our strategy treats the system dynamics over continuous intervals separately from the discrete instants of discontinuity, allowing us to rigorously prove consensus. The control strategy incorporates a virtual system framework to operate without velocity measurements and successfully relaxes the impractical requirement that delays must have finite derivatives. Finally, simulation examples are provided to demonstrate the effectiveness of the proposed consensus algorithm. Index terms: Euler-Lagrange system, Multi-agent system, Sampled-data control.

This article investigates the secure consensus control problem for multi-unmanned aerial vehicles (uncrewed aerial vehicles (UAVs)) attitude system under channel fading. By decomposing the attitude system, an attitude privacy protection scheme is proposed, enhancing communication security through the transmission of only partial attitude angles. Under UAV channel fading, a distributed observer incorporating a detection factor and compensation mechanism is proposed to ensure both transmission accuracy and observation precision. Furthermore, to enhance tracking performance, a secure prescribed performance control (PPC) strategy based on preset trajectory is proposed. This strategy achieves the PPC without relying on complete information of UAVs. Ultimately, a simulation example involving UAVs demonstrates the feasibility and effectiveness of the proposed control scheme.

3D conventional photoacoustic (PA) imaging often suffers from visibility artifacts caused by the limited bandwidth and constrained viewing angles of ultrasound transducers, as well as the use of sparse arrays. PA fluctuation imaging (PAFI), which leverages signal variations due to blood flow, compensates for these visibility artifacts at the cost of temporal resolution. Deep learning (DL)-based PA image enhancement has previously demonstrated strong potential for improved reconstruction at a high temporal resolution. However, generating an experimental training dataset remains problematic. Herein, we propose creating an experimental training dataset based on single-shot 3D PA images (input) and corresponding PAFI images (ground truth) of chicken embryo vasculature, which is used to train a 3D ResU-Net neural network. The trained DL-PAFI network predictions on new experimental test images reveal effective improvement in visibility and contrast. We observe, however, that the output image resolution is lower than that of PAFI. Importantly, incorporating only experimental data into training already yields a good performance, while pre-training with simulated examples improves the overall accuracy. Additionally, we demonstrate the feasibility of real-time rendering and present preliminaryin vivopredictions in mice, generated by the network trained exclusively on chicken embryo vasculature. These findings suggest the potential for achieving real-time, artifact-free 3D PA imaging with sparse arrays, adaptable to variousin vivoapplications.

ABSTRACTThis paper addresses the problem of controlling time‐varying formation (TVF) in nonlinear multi‐agent systems (MASs) that are subject to false data injection (FDI) attacks. A malicious attacker can inject false data into an actuator, which leads to a deviation in the follower's perception of its own state or the state of its neighbors, and thus disrupts the formation control of multi‐agent systems (MASs). Meanwhile, the nonlinear nature of the system itself brings problems such as modeling uncertainty, control complexity, and difficulty in ensuring stability. To address the above challenges, this paper establishes a dynamic model of nonlinear multi‐agent systems (MASs) under false data injection (FDI) attack, and designs an observation and estimation mechanism with robustness for detecting and compensating the disturbances caused by the attack. On this basis, a distributed formation control strategy is proposed. Specifically, this paper designs a distributed control protocol based on neural networks, utilizes neural networks to approximate and compensate for unknown nonlinear terms, designs an adaptive compensator to defend against false data injection (FDI) attacks, and demonstrates the feasibility of the proposed control scheme with the help of Lyapunov stability theory. Finally, the feasibility of the proposed method is verified by simulation examples.