Heterogeneous Disturbances Research Articles

Stabilizing effects of spatially heterogeneous disturbance via reduced spatial synchrony on a rocky shore community.

Understanding how synchronous species fluctuations affect community stability is a main research topic in ecology. Yet experimental studies evaluating how changes in disturbance regimes affect the synchrony and stability of populations and communities remain rare. We hypothesized that spatially heterogeneous disturbances of moderate intensity would promote metacommunity stability by decreasing the spatial synchrony of species fluctuations. To test this hypothesis, we exposed rocky shore communities of algae and invertebrates to homogeneous and gradient-like spatial patterns of disturbance at two levels of intensity for 4 years and used synchrony networks to characterize community responses to these disturbances. The gradient-like disturbance at low intensity enhanced spatial β diversity compared to the other treatments and produced the most heterogeneous and least synchronized network, which was also the most stable in terms of population and community fluctuations. In contrast, homogeneous disturbance destabilized the community, enhancing spatial synchronization. Intense disturbances always reduced spatial β diversity, indicating that strong perturbations could destabilize communities via biotic homogenization regardless of their spatial structure. Our findings corroborated theoretical predictions, emphasizing the importance of spatially heterogeneous disturbances in promoting stability by amplifying asynchronous spatial and temporal fluctuations in population and community abundance. In contrast to other networks, synchrony networks are vulnerable to the removal of most peripheral nodes, which are less synchronized, but may contribute more to stability than other nodes by dampening large fluctuations in species abundance. Our findings suggest that climate change and direct anthropogenic disturbance can compromise the stability of ecological communities through combined effects on diversity and synchrony, as well as further affecting ecosystems through habitat loss.

Refined sliding mode disturbance observer‐based finite‐time composite control for Euler–Lagrange systems with multiple disturbances

AbstractA wide range of practical control systems such as robot manipulators and spacecrafts can be modeled by Euler–Lagrange systems. On one hand, the requirements for system control accuracy, response speed, and robustness are ever increasing. On the other hand, the control performances are inevitably degraded by multiple disturbances. In this paper, a novel finite‐time composite control law is proposed for a class of Euler–Lagrange systems subject to multiple heterogeneous disturbances including a neutrally stable disturbance and a norm‐bounded disturbance. More specifically, a refined sliding mode disturbance observer (RSMDO) is proposed to estimate and reject the neutrally stable disturbance in the feedforward channel, while an adaptive nonsingular backstepping control (ANBC) is designed in the feedback channel to stabilize the system and attenuate the observer error as well as the norm‐bounded disturbance. The proposed RSMDO can not only fully utilize the disturbance information, but also quantify the convergence time and steady‐state accuracy of the estimation error. The finite‐time stability analysis is carried out for the closed‐loop system. Finally, an example of spacecraft attitude control system is given to demonstrate the effectiveness of the proposed methods.

Comparison of two deep reinforcement learning algorithms towards an optimal policy for smart building thermal control

Heating, Ventilation, and Air Conditioning (HVAC) systems are the main providers of occupant comfort, and at the same time, they represent a significant source of energy consumption. Improving their efficiency is essential for reducing the environmental impact of buildings. However, traditional rule-based and model-based strategies are often inefficient in real-world applications due to the complex building thermal dynamics and the influence of heterogeneous disturbances, such as unpredictable occupant behavior. In order to address this issue, the performance of two state-of-the-art model-free Deep Reinforcement Learning (DRL) algorithms, Proximal Policy Optimization (PPO) and Soft Actor-Critic (SAC), has been compared when the percentage valve opening is managed in a thermally activated building system, modeled in a simulated environment from data collected in an existing office building in Switzerland. Results show that PPO reduced energy costs by 18% and decreased temperature violations by 33%, while SAC achieved a 14% reduction in energy costs and 64% fewer temperature violations compared to the onsite Rule-Based Controller (RBC).

The hidden order of Turing patterns in arid and semi‐arid vegetation ecosystems

Vegetation Turing patterns play a critical role in the ecological functioning of arid and semi-arid ecosystems. However, the long-range spatial features of these patterns have been neglected compared to short-range features like patch shape and spatial wavelength. Drawing inspiration from hyperuniform structures in material science, we find that the arid and semi-arid vegetation Turing pattern exhibits long-range dispersion similar to hyperuniformity. As the degree of hyperuniformity of the vegetation Turing pattern increases, so does the water-use efficiency of the vegetation. This finding supports previous studies that suggest that Turing patterns represent a spatially optimized self-organization of ecosystems for water acquisition. The degree of hyperuniformity of Turing-type ecosystems exhibits significant critical slowing down near the tipping point, indicating that these ecosystems have non-negligible transient dynamical behavior. Reduced rainfall not only decreases the resilience of the steady state of the ecosystem but also slows down the rate of spatial optimization of water-use efficiency in long transient regimes. We propose that the degree of hyperuniformity indicates the spatial resilience of Turing-type ecosystems after strong, short-term disturbances. Spatially heterogeneous disturbances that reduce hyperuniformity lead to longer recovery times than spatially homogeneous disturbances that maintain hyperuniformity.

Resilience assessment framework toward interdependent bus–rail transit network: Structure, critical components, and coupling mechanism

Understanding the interdependent nature of multimodal public transit networks (PTNs) is vital for ensuring the resilience and robustness of transportation systems. However, previous studies have predominantly focused on assessing the vulnerability and characteristics of single-mode PTNs, neglecting the impacts of heterogeneous disturbances and shifts in travel behavior within multimodal PTNs. Therefore, this study introduces a novel resilience assessment framework that comprehensively analyzes the coupling mechanism, structural and functional characteristics of bus–rail transit networks (BRTNs). In this framework, a network performance metric is proposed by considering the passengers’ travel behaviors under various disturbances. Additionally, stations and subnetworks are classified using the k-means algorithm and resilience metric by simulating various disturbances occurring at each station or subnetwork. The proposed framework is validated via a case study of a BRTN in Beijing, China. Results indicate that the rail transit network (RTN) plays a crucial role in maintaining network function and resisting external disturbances in the interdependent BRTN. Furthermore, the coupling interactions between the RTN and bus transit network (BTN) exhibit distinct characteristics under infrastructure component disruption and functional disruption. These findings provide valuable insights into emergency management for PTNs and understanding the coupling relationship between BTN and RTN.

Rewilding soil-disturbing vertebrates to rehabilitate degraded landscapes: benefits and risks.

Soil-disturbing animals are common globally and play important roles in creating and maintaining healthy functional soils and landscapes. Yet many of these animals are threatened or locally extinct due to habitat loss, predation by non-native animals or poaching and poisoning. Some reintroduction and rewilding programmes have as their core aims to increase animal populations and reinstate processes that have been lost due to their extirpation. Here we use a meta-analytical approach to review the effects of soil-disturbing vertebrates on ecosystem processes, and advance the argument that they can be used to rehabilitate degraded ecosystems by altering mainly composition and function, but with fewer positive effects on structure. We describe four examples where the loss or reintroduction of soil-disturbing vertebrates leads to ecosystem state changes and highlight the role of spatial scale, covarying management changes, and species co-occurrence in modulating their effects. We discuss the advantages and disadvantages of using soil-disturbing vertebrates over mechanized engineering approaches such as pitting and furrowing, considering some advantages to include more self-sustainable and heterogeneous disturbances, creation of new habitats and added recreational values. Finally, we identify key knowledge gaps in our understanding of the use of soil-disturbing vertebrates for rehabilitating degraded ecosystems.

Natural Disturbances are Essential Determinants of Tree-Related Microhabitat Availability in Temperate Forests

Assessing the impacts of natural disturbance on the functioning of complex forest systems are imperative in the context of global change. The unprecedented rate of contemporary species extirpations, coupled with widely held expectations that future disturbance intensity will increase with warming, highlights a need to better understand how natural processes structure habitat availability in forest ecosystems. Standardised typologies of tree-related microhabitats (TreMs) have been developed to facilitate assessments of resource availability for multiple taxa. However, natural disturbance effects on TreM diversity have never been assessed. We amassed a comprehensive dataset of TreM occurrences and a concomitant 300-year disturbance history reconstruction that spanned large environmental gradients in temperate primary forests. We used nonlinear analyses to quantify relations between past disturbance parameters and contemporary patterns of TreM occurrence. Our results reveal that natural forest dynamics, characterised by fluctuating disturbance intervals and variable severity levels, maintained structurally complex landscapes rich in TreMs. Different microhabitat types developed over time in response to divergent disturbance histories. The relative abundance of alternate TreMs was maximised by unique interactions between past disturbance severity and elapsed time. Despite an unequal distribution of individual TreMs, total microhabitat diversity was maintained at constant levels, suggesting that spatially heterogeneous disturbances maintained a shifting mosaic of habitat types over the region as a whole. Our findings underscore the fundamental role of natural processes in promoting conditions that maximise biodiversity potential. Strict conservation and management systems that preserve natural disturbance outcomes, including associated biological legacies, may therefore safeguard biodiversity at large scales.

Disturbance observer–based finite-time control for a class of systems with multiple heterogeneous disturbances

The finite-time anti-disturbance control problem is studied for systems with multiple heterogeneous disturbances, which include the disturbance with partially known information and the external disturbance. A disturbance observer is designed to online estimate the disturbance with partially known information. Based on this, a disturbance observer–based finite-time control (DOBFTC) scheme is presented by combining disturbance observer–based control (DOBC) with finite-time control method to guarantee that the composite system is globally finite-time stable. Two simulation examples including a numerical example and a doubly fed induction generators (DFIG) system are given to show the effectiveness of the proposed method compared with the existing schemes.

Robust Formation Control for Multiagent Systems Based on Adaptive Observers

In this article, a distributed robust control strategy is proposed for heterogeneous multiagent systems subject to uncertainties and disturbances to achieve time-varying formations under static and switching topologies. Without prior knowledge of global information on communication graphs, a distributed observer is developed to estimate the state information of a reference system. Internal model-based robust formation controllers for homogeneous disturbances, with and without unknown heterogeneous disturbances, are designed using the Riccati equation approach. Finally, a case study of bushfire edge tracking and patrolling is presented, and the effectiveness and robustness of the formation control developed strategy are verified by simulations.

Arbitrage Pricing with Heterogeneous Spatial Effects and Heteroscedastic Disturbances

Abstract We develop a heterogeneous spatial arbit and regression coefficients, and heteroscedastic variances, and further establish identification of parameters and asymptotic normality of the conditional QML estimators under some mild conditions. We apply the proposed model to study a real data set of 11 eurozone stock index returns and extend the Fama–French five-factor model to regional stock indices, in which heterogeneous spatial effects and heteroscedastic disturbances are highly significant and they both play very important roles in explaining distinct endogenous effects and distinct risks of the 11 eurozone stock markets. Our empirical results reveal unique characteristics of each of 11 eurozone stock markets and their inner connections.

Enhanced distributed state estimation with resilience to multiple disturbances and false data injection attacks

AbstractDisturbances and attacks co‐exist widely in networked systems. Distributed state estimation of networked systems in the simultaneous presence of disturbances and attacks has been widely recognized as a challenging issue. In this article, an enhanced three‐stage approach for the detection and rejection of attacks is established for networked systems subject to multiple disturbances and false data injection (FDI) attacks. First, a multi‐layer distributed estimator with a disturbance observer layer is proposed. Then, an optimal observer‐based attack detection scheme is designed to specify an attack detection logic. After disturbance rejection and attack detection, an attack‐resilient estimator with a dynamic detection‐triggered structure is proposed to actively reject FDI attacks online. This three‐stage approach effectively separates the influence of disturbances and attacks in the distributed estimation process, enabling multiple heterogeneous disturbances and attacks across the sensor network to be rejected. A numeric example and comparisons are provided to illustrate the effectiveness of the proposed approach.

Consensus Control of Leader-Following Multi-Agent Systems in Directed Topology With Heterogeneous Disturbances

This paper investigates the consensus problem for linear multi-agent systems with the heterogeneous disturbances generated by the Brown motion. Its main contribution is that a control scheme is designed to achieve the dynamic consensus for the multi-agent systems in directed topology interfered by stochastic noise. In traditional ways, the coupling weights depending on the communication structure are static. A new distributed controller is designed based on Riccati inequalities, while updating the coupling weights associated with the gain matrix by state errors between adjacent agents. By introducing time-varying coupling weights into this novel control law, the state errors between leader and followers asymptotically converge to the minimum value utilizing the local interaction. Through the Lyapunov directed method and Itô formula, the stability of the closed-loop system with the proposed control law is analyzed. Two simulation results conducted by the new and traditional schemes are presented to demonstrate the effectiveness and advantage of the developed control method.

Elegant anti-disturbance control for stochastic systems with multiple heterogeneous disturbances based on fuzzy logic systems

There are a large number of non-harmonic disturbances generated by nonlinear exogenous systems in realistic engineering. The current disturbance observer is not applicable for estimating the non-harmonic disturbance with unknown nonlinear dynamics, thus greatly reducing the accuracy of the controller. This paper addresses a class of stochastic systems with multiple heterogeneous disturbances including white noise and non-harmonic disturbance with unknown smooth nonlinear function, which can be approximated by fuzzy logic systems. Based on the approximation of the unknown nonlinear function, an adaptive disturbance observer (ADO) is constructed to estimate non-harmonic disturbance. Combining disturbance observer-based control with fuzzy control, an elegant anti-disturbance control (EADC) scheme is proposed such that the composite system achieves asymptotically bounded in mean square. Simulation examples show that the state responses of the system gradually approache [Formula: see text] from divergence, indicating that the effectiveness of the controller is satisfactory. In addition, the anti-disturbance control accuracy of EADC approximately improves [Formula: see text] times compared with [Formula: see text] control. The simulation results demonstrate the feasibility and effectiveness of the proposed scheme.

Adaptive nonlinear disturbance observer-based control for stochastic systems with multiple heterogeneous disturbances

The problem of anti-disturbance control is studied for a class of stochastic systems with multiple heterogeneous disturbances, which include three kinds of disturbance. One is the non-harmonic disturbance coupled with system state and control input. The other one is an unexpected nonlinear signal described as a nonlinear function. The third one is white noise. An adaptive nonlinear disturbance observer (ANDO) is constructed to estimate non-harmonic disturbance. Based on which, a new adaptive nonlinear disturbance observer-based control (ANDOBC) strategy is developed such that the composite system is asymptotically bounded in mean square. Simulation results are given to show its effectiveness of the proposed method.

Elegant antidisturbance fault‐tolerant control for stochastic systems with multiple heterogeneous disturbances

SummaryIn this article, the elegant antidisturbance fault‐tolerant control (EADFTC) problem is studied for a class of stochastic systems in the simultaneous presence of multiple heterogeneous disturbances and time‐varying faults. The multiple heterogeneous disturbances include white noise, norm bounded uncertain disturbances and uncertain modeled disturbances with multiple nonlinearities and unknown amplitudes, frequencies, and phases. The time‐varying fault signals are caused by lose efficacy of actuator. To online estimate uncertain modeled disturbances and time‐varying faults, a novel composite observer structure consisting of the adaptive nonlinear disturbance observer and the fault diagnosis observer is constructed. The novel EADFTC strategy is proposed by integrating composite observer structure with adaptive disturbance observer‐based control theory and H∞ technology. It is proved that all the signals of closed‐loop system are asymptotically bounded in mean square under the circumstances of multiple heterogeneous disturbances and time‐varying faults occur simultaneously. Finally, the effectiveness and availability of proposed strategy are demonstrated by means of the numerical simulation and a doubly fed induction generators system simulation, respectively.

Composite anti-disturbance control for stochastic systems with multiple heterogeneous disturbances and input saturation

This paper develop a novel composite anti-disturbance control (CADC) method for a class of stochastic nonlinear systems in which input saturation and multiple heterogeneous disturbances occur. The disturbances include additive white noise, multiplicative white noise and modeled disturbances with coupling of energy-limited white noise and state signals. The input saturation item of system is approximated by the polytopic bounding method. In order to estimate the modeled disturbances, a novel stochastic saturating disturbance observer (SSDO) is designed. The CADC scheme is presented by integrating SSDO with convex hull expression of saturated linear feedback law, such that the superior anti-disturbance performance to be achieved while overcoming the effects of input saturation. Finally, the validity and usability of the developed scheme are testified by the numerical simulation and practical application example.

Distributed Formal Analysis for Power Networks With Deep Integration of Distributed Energy Resources

A scalable distributed formal analysis (DFA) via reachable set computation is presented to efficiently evaluate the stability of large-scale interconnected power networks under heterogeneous disturbances induced by high penetration of distributed energy resources (DERs). Based on rigorous mathematical derivation, DFA is able to directly compute the boundaries of all possible dynamics and provide stability information, which is unattainable by traditional time-domain simulations or direct methods. An $N+M$ decomposition approach is established to decouple a large-scale networked system and enable distributed reachable set calculations while also preserving the privacy of each subsystem. Numerical examples on a networked microgrid system show that DFA facilitates the efficient calculation and analysis of the impact DER disturbances can have on power network dynamics, which provides a potent means of optimizing the system's operation. Therefore, DFA provides an invaluable tool for designing and operating the interconnected power networks of the future, which will feature the deep integration of DERs.

Adaptive disturbance observer‐based control for stochastic systems with multiple heterogeneous disturbances

SummaryAntidisturbance control problem is discussed for stochastic systems with multiple heterogeneous disturbances, which include the white noise and the disturbance with unknown frequencies and amplitudes. An adaptive disturbance observer is designed to estimate the disturbance with unknown frequencies and amplitudes, based on which, an adaptive disturbance observer‐based control scheme is proposed by combining adaptive technique and linear matrix inequality method. It is proved that the closed‐loop system is asymptotically bounded in mean square when multiple heterogeneous disturbances exist simultaneously and that the equilibrium is globally asymptotically stable in probability as additive disturbance disappears. Finally, two simulation examples, including a wind turbine system, are given to show the effectiveness of the proposed scheme.

Composite hierarchical anti-disturbance control for stochastic systems with multiple heterogeneous disturbances

This paper studies the problem of anti-disturbance control for a class of stochastic systems with multiple heterogeneous disturbances, which include the white noise and the non-harmonic disturbance with unknown nonlinear function. An adaptive disturbance observer is constructed to estimate the non-harmonic disturbances with unknown nonlinear function, which is approximated by neural network. A composite hierarchical anti-disturbance control (CHADC) scheme is designed by integrated Lyapunov function and linear matrix inequality (LMI), such that the expected dynamic performance of the composite system is achieved. Finally, simulations show that the approach is proper and effective.

Tube-Based Discrete Controller Design for Vehicle Platoons Subject to Disturbances and Saturation Constraints

Cooperative adaptive cruise control (CACC) is a promising intelligent vehicle technology for improving traffic flow stability, throughput, and safety. One major control objective of CACC is to guarantee $\mathcal {L}_{p}$ string stability, i.e., $\mathcal {L}_{p}$ -norm measured disturbance is uniformly bounded along the vehicle string. Most existing methods for string stability are laborious for implementation without considering either heterogeneous disturbances (e.g., tracking errors and unmodeled dynamics) or saturation constraints (e.g., input saturation). The decentralized model predictive control (MPC) method, which is a widely used feedforward control for string stability, suffers the burdens of computation cost and intervehicular communication. To fill these gaps, we distinguish different types of disturbances and use different ways to handle them. We use feedforward control for large yet infrequent disturbances and feedback control for small yet frequent disturbances. Different from MPC, our feedforward control is event-triggered so that the intervehicle communication and planning costs can be significantly reduced. Different from pure robust feedback control, our combination of feedback and feedforward control could reduce the conservation of the controller. Theoretical analysis and simulations show that the proposed method guarantees $\mathcal {L}_{p}$ string stability of vehicle platoons considering heterogeneous disturbances and saturation constraints.