Unexpected Disturbances Research Articles

How honeybees respond to heat stress from the individual to colony level.

A honey bee colony functions as an integrated collective, with individuals coordinating their behaviour to adapt and respond to unexpected disturbances. Nest homeostasis is critical for colony function; when ambient temperatures increase, individuals switch to thermoregulatory roles to cool the nest, such as fanning and water collection. While prior work has focused on bees engaged in specific behaviours, less is known about how responses are coordinated at the colony level, and how previous tasks predict behavioural changes during a heat stress. Using BeesBook automated tracking, we follow thousands of individuals during an experimentally induced heat stress, and analyse their behavioural changes from the individual to colony level. We show that heat stress causes an overall increase in activity levels and a spatial reorganization of bees away from the brood area. Using a generalized framework to analyse individual behaviour, we find that individuals differ in their response to heat stress, which depends on their prior behaviour and correlates with age. Examining the correlation of behavioural metrics over time suggests that heat stress perturbation does not have a long-lasting effect on an individual's future behaviour. These results demonstrate how thousands of individuals within a colony change their behaviour to achieve a coordinated response to an environmental disturbance.

Sustained liveable cities: the interface of liveability and resiliency

ABSTRACT Cities across the world are challenged by acute and chronic problems. Liveability policies and plans have generally disregarded the impacts of unexpected shocks and disturbances on cities and the quality of life of communities. This paper argues that the resiliency of a city is a necessary condition for its liveability. Applying qualitative methodologies, the paper builds on in-depth semi-structured interviews to explore the interface between the concepts of liveability and resiliency. Findings suggested that urban planning and policy needs to address the institutionalised inequities built into our cities through ‘local living’. Planning sustained liveable cities was also perceived to be ‘people centred’. ‘Integrated long-term planning’, ‘responsiveness and adaptability’, ‘future-proofing’ and ‘sustainable development’ were also underlined as key principles of developing cities that are both liveable and resilient. These principles are, however, challenging to implement. Future studies need to investigate the mechanisms through which these can be translated into practice.

Mediating Effect of Job Resources on Psychological Wellbeing and Employee Engagement- Centric to Public Sector Telecom in Pandemic, India

The current pandemic brought about by COVID-19 has a reason for unexpected disturbance in all human beings. As the nationwide economy and organizations got an extreme blow with this situation, to handle the present pandemic situation Organizations changed employee’s avatar by shifting workforce from office to home. In these new avatar employees, working conditions with limited job resources like Intra/Internet and phone calls may impact engagement levels compared to earlier. Similarly increased working hours, spending more time with office calls at home, and sometimes near-end family emotions may disturb the psychological conditions of employees. So this research focused on employee’s psychological wellbeing and Job resources and its importance in promoting Engagement. In general, for IT employees work from home is a regular practice whereas coming to Telecom employees the scenario is completely different because of the versatile in the job nature so this research focused on the importance of psychological well-being and job resources in promoting Telco’s employee engagement in Indian pandemic time. From the strong support of Theoretical evidence from the Job Demands-Resources model and theory of Conservation resources, Objectives are framed as to identify the Job Resources that mediates the relation between psychological well-being and employee engagement. The study was centric about Public Sector Telecom employees of India of sample 382 for 65,000 Population and a purposive sampling method of sampling technique is used. By distributing a questionnaire with the help of Google forms primary data was collected. Mediation analysis is carried out with the help of AMOS and conformed the Job Recourses fully mediates the relationship between psychological well-being and employee engagement.

Fuzzy Event-Triggered Super Twisting Sliding Mode Control for Position Tracking of Permanent Magnet Synchronous Motors Under Unknown Disturbances

In this article, we propose the fuzzy event-triggered super twisting sliding mode control (STSMC) approach for improving the position tracking performance and reducing the communication resource under various unknown disturbances in the control system of permanent magnet synchronous motors (PMSMs). The event-triggered strategy is designed to minimize the unnecessary use of computation/communication resources while achieving acceptable control performances. However, when the unexpected disturbances are injected, the tracking performance could be degraded since the conventional event-triggered strategy using previous tracking error information cannot immediately update the control input according to rapid unexpected disturbances. To resolve this problem, we design the fuzzy rule, which is applied in the event-triggered strategy to adjust the updating time based on the tracking error at the present time to achieve robustness against unexpected disturbances. Thus, a fuzzy event-triggered control input can be transmitted immediately to the PMSM when an unexpected disturbance occurs. The STSMC method is designed to enhance the position tracking performance under disturbances. The stability of the closed-loop is shown using the modified Lyapunov theory. In addition, the nonexistence of Zeno execution is guaranteed by analyzing the minimum bounded event-triggered interval time. The effectiveness of the proposed method was verified through simulations and experiments, where the experiment was conducted using a PMSM testbed.

Adaptive neural control of microgravity active vibration isolation system subject to output constraint and unexpected disturbance

The objective of this investigation is to explore the issue of microgravity vibration isolation control within a space station, while considering constraints on output and unexpected disturbances. Drawing upon an analysis of the environment and operating conditions within the space station, the uncertainties resulting from variable payloads and unforeseen disturbances are depicted as combined disturbances. On this basis, an actor–critic neural network with energy consumption evaluation is used to estimate the combined disturbances and devise strategies for compensatory control. Furthermore, an adaptive robust term is devised to handle errors generated by the estimation process of the neural network. Additionally, to ensure that the output of the isolation system remains within specified bounds, an improved predefined performance function is introduced, thereby ensuring adherence to constraints regardless of initial conditions. Simulations conducted under different operational scenarios demonstrate the effectiveness of the proposed controller in achieving system stability. The results indicate a remarkable suppression of onboard vibration amplitude, amounting to approximately 60 dB within the frequency range of 2 to 100 Hz.

Enhancing EV charger resilience with reinforcement learning aided control

This study aims to improve the performance of sustainable electric vehicle chargers in the face of unpblackictable/unpreventable disturbances. Over the past few years, Dual Active Bridge (DAB) DC-DC Converters are procuring substantial recognition for electric vehicle charging applications due to their superior characteristics such as higher power density, bidirectional mode of operation, and higher efficiency. Unexpected disturbances and fault scenarios at both source and load sides can deteriorate DAB converters’ performance. In this study, the performance of a single-phase shifted DAB converter is enhanced to achieve desiblack output current under several disturbance conditions for electric vehicle (EV) charging applications. A Reinforcement Learning (RL) based Deep Deterministic Policy Gradient (DDPG) algorithm is deployed to proactively tune control parameters when the DAB undergoes certain unexpected disturbances including short circuit faults at the source and battery sides. Results show that the RL-tuned PI controller improves the rate of current overshoot significantly compablack with the manually-tuned PI controller. The method and results are validated through simulations in MATLAB/Simulink environment.

RST Switching Bi-controller Based Flatness for an Electronic Throttle Valve

In this paper, an RST switching bi-controller, based on flatness and on Luenberger observers, is designed to control the opening angle change of an Electronic Throttle Valve (ETV), to compensate unexpected external disturbances and to detect sensor faults. Two identified mathematical linear models are established to simulate the ETV for two different positions of the throttle plate. The use of robust RST switching bi-controller based-flatness approach by the development of closedloop control is proposed, in order to obtain a stable system tracking a desired flat trajectory. The switching between the two models using stateflow tool is based on residual values generated by using the Luenberger observers in order to detect and to localize sensor faults occurrence. The observer’s gains are determined using Linear Matrix Inequalities (LMIs) taking into account the stability of the system based on Lyapunov theory. The simulation results show the efficiency of the developed robust switching RST bi-controller based-flatness in terms of tracking the desired angle’s reference trajectory, rejecting disturbances and detecting sensor faults.

Management strategies can buffer the effect of mass mortality in early life stages of fish

Abstract Mass mortality (MM) events affecting early life stages of fish can have strong and long‐term consequences for population abundance and demography as well as the economic activity supported by exploited stocks. Adaptive fishery management may help mitigate economic impacts and ensure sustainable resource use following a MM event. Using a state‐space life cycle model, we simulated ‘what‐if’ scenarios of MM on Northeast Arctic cod (Gadus morhua) eggs and larvae. We compared the expected catches, total stock biomass (TSB), and interannual variability in catches over a period of 10 years after the simulated disturbance. We further evaluated a range of management mitigation strategies, namely reductions in fishing mortality of varying duration (1–10 years) and intensity (no fishing reduction to full ban). A large range of reductions in fishing led to an increase in expected catches over 10 years compared to no reductions, especially when applied immediately after the perturbation and when the cod population was characterized by a high mean age and high TSB. Severe fishing reductions can increase catches substantially but are associated with high interannual variability. Fishing reductions of moderate intensity applied between 1 and 4 years would allow to increase catches with only a slight increase in interannual variability. Our findings demonstrate the potential benefits of an adaptive approach to fisheries management and highlight that mitigation actions may ensure the sustainable exploitation of fish stocks in the wake of unexpected disturbances. Synthesis and application. Mass mortality events during early life stages of fish can potentially have substantial and long‐term effects on the population. Mitigation is more efficient when the affected population has a diverse age structure and when the mitigation strategy is applied immediately after the perturbation. Severe reduction in fishing mortality is an efficient measure to increase the expected average catch but is associated with high interannual variability. Fishing reduction of moderate intensity applied during 1–4 years after the event also increases the expected average catch with only slightly higher interannual variability in catches.

Force-Fighting Phenomena and Disturbance Rejection in Aircraft Dual-Redundant Electro-Mechanical Actuation Systems

This paper presents a robust control system that addresses two key challenges in redundant actuators using Permanent Magnet Synchronous Motors (PMSM) for an aircraft nose wheel steering system: the elimination of force-fighting phenomena and the ability to respond effectively to unexpected disturbances. In detail, a control method was devised to enhance the mitigation of force-fighting phenomena and disturbances by accurately observing and compensating for the torque-induced load applied to the PMSM. This was achieved through the utilization of a Q-filter-based Disturbance Observer (DOB). The proposed control approach was implemented and evaluated on a redundant system consisting of the PMSM and the nose wheel steering system. The performance of the proposed method was verified through extensive simulation studies. The simulation results confirmed the effectiveness and reliability of the method in accurately observing and responding to the force-fighting phenomenon that occurs in the redundant driving device. By subjecting the system to various scenarios and disturbances, the simulation provided a comprehensive evaluation of the proposed method’s ability to handle force-fighting phenomena. The results demonstrated that the method successfully observed and responded to the force-fighting phenomenon, thereby mitigating its adverse effects on the system’s performance. Therefore, these outcomes serve as empirical evidence supporting the validity and efficiency of the proposed method in addressing the force-fighting phenomenon encountered in the redundant driving device. These findings substantiate the effectiveness of the proposed approach and its potential for practical implementation in real-world systems.

DOC: Differentiable Optimal Control for Retargeting Motions onto Legged Robots

Legged robots are designed to perform highly dynamic motions. However, it remains challenging for users to retarget expressive motions onto these complex systems. In this paper, we present a Differentiable Optimal Control (DOC) framework that facilitates the transfer of rich motions from either animals or animations onto these robots. Interfacing with either motion capture or animation data, we formulate retargeting objectives whose parameters make them agnostic to differences in proportions and numbers of degrees of freedom between input and robot. Optimizing these parameters over the manifold spanned by optimal state and control trajectories, we minimize the retargeting error. We demonstrate the utility and efficacy of our modeling by applying DOC to a Model-Predictive Control (MPC) formulation, showing retargeting results for a family of robots of varying proportions and mass distribution. With a hardware deployment, we further show that the retargeted motions are physically feasible, while MPC ensures that the robots retain their capability to react to unexpected disturbances.

Non-linear properties of the Achilles tendon determine ankle impedance over a broad range of activations in humans.

Regulating ankle mechanics is essential for controlled interactions with the environment and rejecting unexpected disturbances. Ankle mechanics can be quantified by impedance, the dynamic relationship between an imposed displacement and the torque generated in response. Ankle impedance in the sagittal plane depends strongly on the triceps surae and Achilles tendon, but their relative contributions remain unknown. It is commonly assumed that ankle impedance is controlled by changing muscle activation and, thereby, muscle impedance, but this ignores that tendon impedance also changes with activation-induced loading. Thus, we sought to determine the relative contributions from the triceps surae and Achilles tendon during conditions relevant to postural control. We used a novel technique that combines B-mode ultrasound imaging with joint-level perturbations to quantify ankle, muscle, and tendon impedance simultaneously across activation levels from 0 - 30% of maximum voluntary contraction. We found that muscle and tendon stiffness, the static component of impedance, increased with voluntary plantarflexion contractions, but that muscle stiffness exceeded tendon stiffness at very low loads (21±7 N). Above these loads, corresponding to 1.3% of maximal strength for an average participant in our study, ankle stiffness was determined predominately by Achilles tendon stiffness. At approximately 20% MVC for an average participant, ankle stiffness was four times more sensitive to changes in tendon stiffness than muscle stiffness. We provide the first empirical evidence demonstrating that the nervous system, through changes in muscle activations, leverages the non-linear properties of the Achilles tendon to increase ankle stiffness during postural conditions.

Quadratically Constrained Linear Programming-based energy-efficient driving for High-speed Trains with neutral zone and time window

Among the numerous challenges that are faced during the daily operation of high-speed rail, the management of the neutral zones in the catenary during unexpected disturbances remains underinvestigated. This study proposes a computerised method for managing the operations of a high-speed train with regenerative braking for passing neutral zones under disturbance to minimise energy consumption. For the first time, different mechanism-based Automatic Passing Neutral Zone systems, including the Magnetic Induction System and Automatic Train Protection, are analysed and modelled as location-based and time-based constraints, respectively. Motion constraints caused by disturbances are described by time windows. Forced coasting and air brake-allowed passing neutral zone rules are considered in these models. The original nonlinear model with location-based constraints is transcribed as a quadratically constrained linear model and then solved. The optimality consistency and its establishment condition between the original and transcribed models are analysed based on the Karush–Kuhn–Tucker conditions. A high-quality solution is obtained when the establishment condition holds. The model with time-based constraints is novelly transformed into an optimal switching point problem. A series of sub-problems are iteratively and efficiently solved. Comprehensive experiments are conducted based on practical data from a high-speed rail system in China. The proposed method is significantly beneficial when compared to Mixed-integer Linear Programming and Artificial Driving Algorithms. Moreover, the impacts of different mechanism-based automatic passing neutral zone systems, operation rules, and a combination of time window settings are extensively analysed.

Model‐based risk analysis for system design

AbstractDespite being the dominant risk analysis paradigm, event guessing is useless for systems design. In management, no event guessing has ever preempted the launch of policies that are decided, not designed. In engineering, events are not guessed; rather, they are created for testing purposes. Events provide inputs to which systems respond according to their structure, as described by state‐space or equivalent System Dynamics models. A new risk analysis framework draws design support information from model attributes. Risk‐informed dynamic models help design physical architectures or organizational policies that capably respond to arbitrary events. The approach builds on the notion that all inputs carry energy. Physical or policy systems change states by trading energy with the surroundings, through expected transactions and unexpected disturbances. A non‐probabilistic risk framework supports the design by showing that the system exhibits intended functionality when responding to arbitrary inputs. Instead of guessing countless hypothetical events, the framework systematically and comprehensively analyzes weaknesses in the system model using a programmed algorithm. It applies to any state‐space dynamic model by defining risk as a function of the energy needed to move the system from an acceptable to a faulty state. Robust systems dissipate excess energy, whereas vulnerable systems lose functionality. Fuses and cushions are generic classes of protections. Placing them into system models at identified weak points helps improve design. Two published simple models of business policies illustrate the framework, which is extended to define measures for consequence and uncertainty as functions of acceptable and faulty states.

Building robustness for rural revitalization: A social-ecological system perspective

Collaborative governance has the potential to foster co-creative solutions to rural issues. Many rural areas have deteriorated due to rapid and encroaching urbanization and globalization. By building institutions to support and convey collaborative governance processes, rural areas can be revitalized and equipped with the ability to cope and evolve in the face of unexpected shocks and disturbances, they can be made robust. Concepts and arguments developed in a social-ecological systems framework are drawn on to flesh out a theoretical logic to aid understandings of how institutional design can enhance the robustness of a rural social-ecological system through fostering collaboration. A critical instance case study of a rural revitalization project undertaken at a traditional village in Hong Kong, Lai Chi Wo, allows for in-depth analysis of the relationship between intuitional design, collaborative governance and robustness. Through this case study, we show how institutions can be designed and built to foster collaborative processes and, hence, enhance the system's robustness. The demonstration of this logic extends theoretical understandings regarding the identification of mechanisms through which institutional variables can impact and condition collaborative processes and so outcomes. While demonstrated here in the case of rural revitalization, these findings contribute to wider understandings of how collaborative endeavors can be fostered and enhanced and how robustness in social-ecological systems can be better supported.

Ionic wind generation through coplanar discharge: A plasma actuator without exposed electrodes

This study demonstrates the successful induction of a unidirectional ionic wind by adding an embedded electrode in a coplanar discharge, thus breaking the generation of a symmetrical electric field. The strategy for the ionic wind generation is based on separating the ionization process and the charged-particle acceleration process. Conventional plasma actuators used to induce an ionic wind typically incorporate exposed electrodes that pose a risk of unexpected airflow disturbance and reduce durability due to oxidation. However, the coplanar discharge-based, exposed-electrodeless plasma actuator developed in this study overcomes these issues. The coplanar discharge generates a diffused and uniform surface discharge, a desirable attribute for plasma actuators. The ionic wind velocity generated by this coplanar discharge plasma actuator is comparable to that generated by conventional plasma actuators when applying a square-waveform bias voltage to the additional electrode. Furthermore, this study emphasizes the significance of the phase difference between the repetitive pulses for generating coplanar discharge and the square-waveform voltage for accelerating the charged particles.

An Adaptive Control Method with Disturbance Estimation for Underwater Manipulation

In this paper, an adaptive control scheme with disturbance estimation is proposed for the station keeping and trajectory tracking of an underwater vehicle-manipulator system (UVMS) in a task space. The control of UVMSs encounters time-varying and lumped disturbances introduced by the motion of the manipulator and currents. The proposed control scheme includes an observer to identify disturbances without introducing additional sensors. Using state-of-the-art UVMS control technologies, it alleviates the restriction of the feedforward control applied to the UVMS. In addition, the proposed control scheme is not designed based on the worst case. In other words, the robustness of the controller is not achieved at the price of performance, and the performance of the controller can be recovered in the absence of disturbance. Meanwhile, the controller can also realize quick responses to attenuating disturbance and closed-loop stability when encountering an unexpected disturbance. The effectiveness of the proposed control scheme was demonstrated by a series of spatial trajectory tracking experiments under various disturbances. A limitation of this study is that the proposed control scheme is specialized for the underwater manipulation of UVMSs.

Review on Modeling and Control Strategies of DC–DC LLC Converters for Bidirectional Electric Vehicle Charger Applications

Bidirectional DC–DC converters are frequently chosen for applications requiring high power density such as in bidirectional electric vehicle (EV) chargers. Vehicle to Everything (V2X) technology makes the EV battery an electrical energy source. In this article, the use of a DC–DC LLC converter used in a bidirectional EV charger is reviewed. Different modeling approaches of the DC–DC LLC converter, such as small and large signal modeling, are discussed. Common modulation strategies applied to the DC–DC LLC converter in V2X mode, such as Pulse Frequency Modulation (PFM), Pulse Width Modulation (PWM) and Phase-Shift Modulation (PSM), are presented. The new challenge is to present the main characteristics and limitations of each modulation strategy in order to cover the whole operating zone of the EV charger in V2X mode. Furthermore, different control strategies based on a small or large signal model combined with different modulation strategies are highlighted. Linear and nonlinear controllers applied to the DC–DC LLC converter are discussed. Robust controllers are mainly highlighted regarding their advantage in ensuring the control robustness with respect to unexpected disturbances. A comparative study among modulation strategies as well as different control algorithms is conducted in terms of control performance and converter efficiency in V2X mode.

Dynamic event-triggered composite anti-disturbance fault-tolerant tracking control for ships with disturbances and actuator faults

This paper investigates the dynamic event-triggered composite anti-disturbance and fault-tolerant tracking problem of ships with external ocean disturbances and actuator fault effects. The dynamic event-triggered composite anti-disturbance and fault-disturbance tracking is achieved by combining the disturbance observer and the fault observer with the dynamic event-triggering and the vectorial backstepping control method. The nonlinear disturbance observer is constructed to obtain the online external disturbance estimations in the ocean time-varying environment and thus overcome the unexpected disturbances. The actuator fault observer is exploited to achieve the online fault estimations of actuator faults. The dynamic event-triggered mechanism reduces execution frequencies of control signals to mitigate the tear and wear of ship actuators. The dynamic event-triggered composite disturbance compensation fault-tolerant control ensures the globally uniformly ultimately stability of tracking control system and enables the ship to track the expected trajectory with the small errors. Simulations and comparisons with static event-triggered control on a full-scaled ship validate the dynamic event-triggered control.

A novel robust maximum power extraction framework for sustainable PV system using incremental conductance based MRAC technique

AbstractThis study proposes a maximum power point tracking (MPPT) approach with two components: an Incremental Conductance (INC) MPPT for reference voltage regulation and a Model Reference Adaptive Controller (MRAC) for adjusting the duty cycle of the DC‐DC converter switch. A robustness test is performed on the system considering real‐world situations that involve an abrupt change in atmospheric conditions with load uncertainties. The probabilistic load distribution analysis is accomplished through levels of uncertainty (i.e., Probabilistic DOWN and UP) to guarantee the operation of the proposed INC‐MRAC controller while generating unexpected disturbances in the system. Using MATLAB/Simulink, the performances of the novel INC‐MRAC MPPT are comparatively analyzed with PO, INC, VSSPO, and ANN under realistic case studies in seven states. After comparative analysis, it is evident that the proposed MPPT offers less tracking time, i.e., 3.8 ms, to track maximum power point (MPP) with negligible steady‐state oscillation and ripples. It is about 3, 7, 9, and 10 times faster than ANN, VSSPO, INC and PO, respectively. Moreover, the tracking efficiency of the proposed controller is up to 99.63% as well as overall efficiency of the system is more than 98%. The tracking power loss and error rate in finding MPP for the proposed controller is the lowest among all state‐of‐the‐art MPPT approaches. Finally, the effectiveness of the proposed INC‐MRAC approach is experimentally validated by employing a real‐time simulator OPAL‐RT (OP4510). The study helps in environmental protection and participation in sustainable development.

DAGA: Dynamics Aware Reinforcement Learning With Graph-Based Rapid Adaptation

Reinforcement learning (RL) has achieved some notable accomplishments in robotics. However, one of the major challenges when applying RL in the real world is that unexpected disturbances in local dynamics could lead to failures of policies at test time. It is necessary to learn a model capable of adapting to various environments with different dynamics. In this paper, a Dynamic-Aware reinforcement learning model with graph-based rapid adaptation (DAGA) is proposed to address these challenges. DAGA encodes the dynamic features from a few interactions and guides the policy with an environment embedding. To encourage the embedding to capture variations in dynamics, we present an objective function based on forward prediction and environment similarity. The proposed model enables the robot to generalize with a wide range of transition dynamics resulted from different hardware parameters. Experiments in robot locomotion and manipulation tasks show that DAGA outperforms existing baselines in both better sample efficiency and generalization. DAGA has the potential to deploy the RL policy in realistic and changing environments.