Dynamic Uncertainties Research Articles

Review on Consensus Control of Multi-Agent Systems

This review provides a comprehensive analysis of consensus control strategies for multi-agent systems (MASs) mainly by researching and analysing the control formula designs of different representative MAS. Key approaches are highlighted ,such as finite-time consensus, pinning control, consensus control under DoS attacks, and other advanced control strategies. Each control strategy is discussed in detail regarding its applicability, advantages, and limitations. Furthermore, the strategies ability to address rapid convergence, robustness, and security in complex network environments is emphasized. Besides, several current research trends and key challenges, such as robustness under dynamic uncertainties and resource-constrained communication, are also explored. Future opportunities for enhancing the stability and security of MASs are explained in the final section of this review

Decomposition-Aware Framework for Probabilistic and Flexible Time Series Forecasting in Aerospace Electronic Systems

Degradation prediction for aerospace electronic systems plays a crucial role in maintenance work. This paper proposes a concise and efficient framework for multivariate time series forecasting that is capable of handling diverse sequence representations through a Channel-Independent (CI) strategy. This framework integrates a decomposition-aware layer to effectively separate and fuse global trends and local variations and a temporal attention module to capture temporal dependencies dynamically. This design enables the model to process multiple distinct sequences independently while maintaining the flexibility to learn shared patterns across channels. Additionally, the framework incorporates probabilistic distribution forecasting using likelihood functions, addressing the dynamic variations and uncertainty in time series data. The experimental results on multiple real-world datasets validate the framework’s effectiveness, demonstrating its robustness and adaptability in handling diverse sequences across various application scenarios.

A Symmetry-Inspired Hierarchical Control Strategy for Preventing Rollover in Articulated Rollers

In off-road environments, the lateral rollover stability of articulated unmanned rollers (URs) is critical to ensure operational safety and efficiency. This paper introduces the concept of a rollover energy barrier (REB), a symmetry-based metric that quantifies the energy margin between the current state and the critical rollover threshold of articulated rollers. URs exhibit dynamic asymmetry due to their hydraulic steering systems, which differ significantly from traditional passenger vehicles. To address these challenges, we propose a hierarchical control framework inspired by the principles of dynamic symmetry. This framework integrates Nonlinear Model Predictive Control (NMPC) and Active Disturbance Rejection Control (ADRC): NMPC is used for trajectory planning by incorporating the REB into the cost function, ensuring rollover stability, while ADRC compensates for dynamic asymmetries, model uncertainties, and external disturbances during trajectory tracking. Simulation and experimental results validate the effectiveness of the proposed control strategy in enhancing the rollover stability and tracking performance of the URs under off-road conditions.

Engineering Management and Modular Design: A Path to Robust Manufacturing Processes

Manufacturing environments, characterized by dynamic changes and uncertainties, demand effective strategies to minimize disruptions. This study introduces an innovative approach that integrates engineering management principles with modular design to prioritize risk mitigation and enhance robustness in manufacturing processes. From a systems engineering perspective, all manufacturing activities are perceived as interconnected components within a unified system. Leveraging the Axiomatic Design (AD) theory and the Design Structure Matrix (DSM) method, the study modularizes manufacturing process architecture to effectively curb risk propagation and manage system complexity. This study identifies the most optimal design as a pivotal architectural configuration, significantly improving the structural robustness and stability of the System of Interest (SOI). Empirical evidence supports this design’s capability to reduce complexities, thereby enhancing robustness within the broader system architecture. Notably, the proposed approach results in a substantial reduction in complexity, with the most optimal design exhibiting an approximately 82.79 percent reduction in work volume compared to the original design. Our research underscores the critical relationship between manufacturing and engineering management. Effective collaboration between these domains optimizes resource allocation, decision-making processes, and overall organizational strategy, leading to improved production processes and increased efficiency. Importantly, the study demonstrates a significant enhancement in modularization, resulting in elevated overall robustness in manufacturing processes. This highlights the proactive involvement of engineering management in the design phase to address production challenges, ultimately optimizing system performance. Thus, this research contributes to both practical applications and academic discourse by offering a novel approach to enhancing the robustness in manufacturing processes. By integrating engineering management principles and modular design strategies, organizations can fortify their processes against disruptions and effectively adapt to evolving circumstances.

Command-filter based predefined-time neural adaptive decentralized control for interconnected systems with dynamic uncertainties

Command-filter based predefined-time neural adaptive decentralized control for interconnected systems with dynamic uncertainties

Robust Trajectory Optimization for Aerogravity-Assist With Dynamic Uncertainty by Riemann–Stieltjes Integral Expansion

Robust Trajectory Optimization for Aerogravity-Assist With Dynamic Uncertainty by Riemann–Stieltjes Integral Expansion

Adaptive Prescribed-Time Distributed Nash Equilibrium Seeking for Networked Games With Heterogeneous Dynamics and Unknown Uncertainties

Adaptive Prescribed-Time Distributed Nash Equilibrium Seeking for Networked Games With Heterogeneous Dynamics and Unknown Uncertainties

Integrating Air-Source Heat Pumps into the Demand-Side Fast Frequency Response Service: A Study Based on Thermal Dynamic Uncertainty

Integrating Air-Source Heat Pumps into the Demand-Side Fast Frequency Response Service: A Study Based on Thermal Dynamic Uncertainty

Modeling and analysis of dengue transmission in fuzzy-fractional framework: a hybrid residual power series approach

The current manuscript presents a mathematical model of dengue fever transmission with an asymptomatic compartment to capture infection dynamics in the presence of uncertainty. The model is fuzzified using triangular fuzzy numbers (TFNs) approach. The obtained fuzzy-fractional dengue model is then solved and analyzed through fuzzy extension of modified residual power series algorithm, which utilizes residual power series along with Laplace transform. Numerical analysis has also been performed in this study and obtained results are shown as solutions and residual errors for each compartment to ensure the validity. Graphical analysis depict the model’s behavior under varying parameters, illustrating contrasting trends for different values of and examining the impacts of transmission and recovery rates on dengue model in uncertain environment. The current findings highlighted the effectiveness of proposed uncertainty in epidemic system dynamics, offering new insights with potential applications in other areas of engineering, science and medicine.

Novel GPID: Grünwald–Letnikov Fractional PID for Enhanced Adaptive Cruise Control

This study demonstrates that the Grünwald–Letnikov fractional proportional–integral–derivative (GPID) controller outperforms traditional PID controllers in adaptive cruise control systems, while conventional PID controllers struggle with nonlinearities, dynamic uncertainties, and stability, the GPID enhances robustness and provides more precise control across various driving conditions. Simulation results show that the GPID improves the accuracy, reducing errors better than the PID controller. Additionally, the GPID maintains a more consistent speed and reaches the target speed faster, demonstrating superior speed control. The GPID’s performance across different fractional orders highlights its adaptability to changing road conditions, which is crucial for ensuring safety and comfort. By leveraging fractional calculus, the GPID also improves acceleration and deceleration profiles. These findings emphasize the GPID’s potential to revolutionize adaptive cruise control, significantly enhancing driving performance and comfort. Numerical results obtained in α=0.99 from the GPID controller have shown better accuracy and speed consistency, adapting to road conditions for improved safety and comfort. The GPID also demonstrated faster stabilization of speed at 60 km/h with smaller errors and reduced the error to 0.59 km/h at 50 s compared to 0.78 km/h for the PID.

Inter‐Model Uncertainty in Projecting Precipitation Changes Over Central Asia Under Global Warming

AbstractThe impact of global warming on Central Asian precipitation suffers from huge uncertainties, yet their origins remain largely unidentified. This study investigates the inter‐model spread in the projection of winter Central Asian precipitation (WCAP) using models from the Coupled Model Inter‐comparison Project Phase 6. The results reveal a homogenous wetting trend in WCAP, with the primary source of uncertainty in its magnitude stemming from the dynamic component of vertical moisture advection. This dynamical uncertainty is further attributed to an out‐of‐phase variation between two Eurasian westerly jets, which can enhance the upward motion over Central Asia through positive relative vorticity advection and warm temperature advection. The opposing variation between two westerly jets can be traced to cooling in the North Pacific, which alters jet intensity through changes in the meridional temperature gradient. This study informs policy and adaptation strategies to better cope with future climate shifts in Central Asia.

Three-Dimensional Structural Analysis of Sea Temperature During Typhoon Transit

This study uses the Finite-Volume Community Ocean Model (FVCOM) to simulate the hydrodynamic processes during typhoon “Saola”. The simulation results closely match observed data. Typhoon “Saola” was a major system in the Pacific typhoon season, highlighting the complexity and uncertainty of tropical cyclone dynamics. By analyzing historical sea surface temperature data and the typhoon’s trajectory, the three-dimensional response of sea temperature during typhoon “Saola” was explored. The key findings are as follows: 1. Typhoon passage affects both coastal and deep-sea warming and cooling. Temperature changes are more pronounced near the coast, with the highest warming and cooling occurring within five days after the typhoon. In deep-sea areas, the highest warming occurs within five days, while the lowest cooling occurs within two days. 2. The nearshore water layers respond quickly to the typhoon, while the deep-sea water layers primarily respond in the middle depths, with a delayed effect. 3. In coastal shallow waters, the response is intense, with the maximum temperature increase and decrease occurring near the bottom, reaching 5.26 °C and −5.17 °C, respectively. In deep-sea areas, the response is weaker, with the maximum temperature change occurring near the surface: an increase of 0.49 °C and a decrease of −0.98 °C. The deepest response in coastal waters reaches about 80 m, while in the deep-sea area, it only reaches 50 m due to the thicker mixed layer.

Neuroadaptive Control of a Continuum Robot for Finger Rehabilitation

This study has designed an easy-to-wear parallel continuum robot-based hand rehabilitation system that supports and enhances the finger’s flexion, extension, abduction, and adduction movements. The primary novelty of the proposed system lies in its ability to focus therapeutic exercises on a single joint, a feature not commonly found in existing rehabilitation robots. A kinematic model of the system was developed, and to perform both kinematic and dynamic analyses, a multibody model was constructed in the MATLAB Simulink environment. Joint angle control was implemented using a nominal controller, and to account for individual uncertainties in joint dynamics, a neuroadaptive controller was integrated with the nominal controller. This approach aims for the neural network architecture to learn these uncertainties during control iterations and incorporate them into the control, resulting in a robust controller. Thus, a model reference control approach was proposed for active and passive rehabilitation processes. The system model was tested in a simulation environment, and then all tests were repeated in the physical system. The simulation and real system results include the real system’s open-loop responses, nominal controller responses for each joint, responses, and the results for active, passive, and assistive control modes.

Complex network control and stability through distributed critic‐based neuro‐fuzzy learning

AbstractInspired by advancements in swarm autonomous vehicles and intelligent control systems, this research addresses the issue of frequency synchronization and phase tracking in oscillator networks. A novel distributed consensus protocol and a reinforcement learning algorithm for a multi‐agent network with a leader–follower topology, considering stability conditions, are developed. The critic‐based neuro‐fuzzy learning (CBNFL) method aims to achieve consensus and minimize local tracking errors. Additionally, an explicit synchronization condition for the network using the Lyapunov theorem is derived. Each vehicle tracks its reference phase and frequency. Employing a fuzzy critic to evaluate the current state and generate a stress signal for the controller, the method prompts adaptive parameter adjustments to minimize this signal. The proposed design's versatility and adaptability to various networks demonstrate robustness against dynamic vehicle properties and network parameter uncertainties, ensuring consistent controller performance. This approach exhibits high scalability, accommodating numerous autonomous agents. To validate the proposed learning method's efficacy, numerical simulations are conducted on a network of five oscillators. The outcomes of implementing CBNFL compared with a conventional PI controller underscore the CBNFL method's superior performance and robustness in maintaining network stability and achieving synchronization.

Complexity Evaluation of Test Scenarios for Autonomous Vehicle Safety Validation Using Information Theory

The validation of autonomous vehicles remains a vexing challenge for the automotive industry’s goal of fully autonomous driving. The systematic hierarchization of the test scenarios would provide valuable insights for the development, testing, and verification of autonomous vehicles, enabling nuanced performance evaluations based on scenario complexity. In this paper, an information entropy-based quantification method is proposed to evaluate the complexity of autonomous vehicle validation scenarios. The proposed method addresses the dynamic uncertainties within driving scenarios in a comprehensive way which includes the unpredictability of dynamic agents such as autonomous vehicles, human-driven vehicles, and pedestrians. The numerical complexity calculation of the approach and the ranking of the scenarios are presented through sample scenarios. To automate processes and assist with the calculations, a novel software tool with a user-friendly interface is developed. The performance of the approach is also evaluated through six example driving scenarios, then through extensive simulation using an open-source microscopic traffic simulator. The performance evaluation results confirm the numerical classification and demonstrate the method’s adaptability to diverse scenarios with a comparison of complexity calculation ranking to the ratio of collision, near collision, and normal operation tests observed during simulation testing. The proposed quantification method contributes to the improvement of autonomous vehicle validation procedures by addressing the multifaceted nature of scenario complexities. Beyond advancing the field of validation, the approach also aligns with the broad and active drive of the industry for the widespread deployment of fully autonomous driving.

Adaptive practical prescribed-time control for uncertain nonlinear systems with time-varying parameters

In this paper, adaptive practical prescribed-time (PPT) control is proposed for a class of uncertain nonlinear systems with time-varying parameters and unmodeled dynamics. By constructing a novel time-varying scaling function and utilizing nonlinear mapping, the PPT control is successfully resolved. The dynamical uncertainties resulting from unmodeled dynamics are estimated by employing an auxiliary available signal, and the unknown continuous terms are handled by the aid of radial basis function neural networks (RBFNNs). A novel adaptive control method is developed by introducing the compensating signals and dynamic surface control as well as practical prescribed-time control. All the signals involved are proved to be semi-globally uniformly ultimately bounded, and the tracking error could enter the pre-specified convergence region within a pre-specified time. The robotic manipulator system is used to demonstrate the effectiveness of the proposed control approach.

Neuro-Adaptive-Based Fixed-Time Composite Learning Control for Manipulators With Given Transient Performance.

This article investigates an adaptive neural network (NN) control technique with fixed-time tracking capabilities, employing composite learning, for manipulators under constrained position error. The first step involves integrating the composite learning method into the NN to address the dynamic uncertainties that inevitably arise in manipulators. A composite adaptive updating law of NN weights is formulated, requiring adherence solely to the relaxed interval excitation (IE) conditions. In addition, for the output error, instead of knowing the initial conditions, this article integrates the error transfer function and asymmetric barrier function to achieve the specific performance for position error in both steady and transient states. Furthermore, the fixed-time control methodology and Lyapunov stability criterion are synergistically employed in order to guarantee the convergence of all signals in the manipulators to a compact neighborhood around the origin within a fixed-time. Finally, numerical simulation and experiments with the Baxter robot results both determine the capability of the NN composite learning technique and fixed-time control strategy.

Event-Triggered Adaptive Preassigned Finite-Time Consensus Control for Multiagent Systems With Nonlinear Faults.

This article investigates a novel neuro-adaptive barrier Lyapunov function (BLF)-based event-triggered preassigned finite-time consensus control with asymptotic tracking for the nonlinear multiagent systems. The proposed approach is designed to broaden the scope of application by considering the high-order nonstrict-feedback dynamics of each agent with dynamic uncertainties subject to external disturbances and nonaffine nonlinear faults. A neural network (NN) is employed to approximate the unknown nonlinear terms. By fusing the NNs and Butterworth low-pass filter technique, the issues arising from the nonaffine nonlinear fault are addressed. To save the communication resources, a novel dynamic event-triggered mechanism based on an enhanced switching threshold is suggested. Additionally, a novel concept called the preassigned finite-time performance function (PFTPF) is defined to improve the transient and steady-state performances as well as providing faster response. The key feature of the proposed adaptive BLF-based control based on the bound estimation method is the introduction of a smooth function with decreasing variable which not only ensures that all the signals remain bounded and the synchronization errors are restricted within the PFTPF but also guarantees that the tracking errors asymptotically converge to zero. Finally, an illustrative example is provided to verify the feasibility of the proposed control approach.

Predefined-time cooperative formation control of heterogeneous unmanned surface vehicle-unmanned aerial vehicle systems with uncertain dynamic estimation

This work addresses the distributed predefined-time cooperative formation of heterogeneous multiagent systems comprising unmanned surface vehicles (USVs) and unmanned aerial vehicles (UAVs) with inherent dynamic uncertainties. By transforming the model coordinates, the underactuated heterogeneous USV-UAV systems can be converted into a fully actuated second-order multiagent framework. Subsequently, a predefined-time dynamic observer is designed to estimate the uncertain dynamics of each agent. Combining the backstepping method and the virtual leader model, a predefined-time distributed cooperative formation control based on uncertain dynamic estimation is designed for the heterogeneous USV-UAV systems. The convergence of the formation errors is rigorously demonstrated by constructing a suitable Lyapunov function under a predefined time framework. Ultimately, the two numerical cases in both the fixed and time-varying formation scenarios confirm the effectiveness of the constructed method.

Multiobjective optimization for sizing and placing electric vehicle charging stations considering comprehensive uncertainties

The rapid growth of electric vehicles (EVs) demands a robust and efficient charging infrastructure. To address this, we propose a particle swarm optimization algorithm designed for optimal placement and sizing of EV charging stations. This study hypothesizes that comprehensive consideration of uncertainties in vehicle types, user behaviors, road dynamics, and environmental impacts will enhance infrastructure effectiveness. Our method integrates data from road networks, driver patterns, station owners, and EV manufacturers to meet diverse charging requirements. Results indicate that 14 fast charging stations are needed along the studied freeway, with a total installation cost of $289,820 and annual operational costs of $4,223,050, leading to annual CO2 emissions of 1,843,572.57 kg. This strategic approach balances technical, environmental, and economic criteria, providing an essential tool for policymakers and urban planners in establishing sustainable EV charging networks.