Design method of tuned mass damper by LMI based robust control theory for seismic excitation

This paper presents a new design method based on a robust-control strategy in the form of a linear matrix inequality (LMI) approach for a passive tuned mass damper (TMD), which is one of the common passive-control devices for structural vibration control. To apply the robust control theory, we first present an equivalent expression that describes a passive TMD as an active TMD. Then, some LMI-based condition is derived that not only guarantees robust stability but also allows us to adjust the robust H¥ performance. In particular, this paper considers the transfer function from a seismic-wave input to structural responses. Unlike other methods, this method formulates the problem to be a convex optimization problem that ensures a global optimal solution and considers uncertainties of mass, damping, and stiffness of a structure for designing a TMD. Numerical example uses both a single-degree-of-freedom (SDOF) and 10DOF models, and seismic waves. The simulation results demonstrated that the TMD that is designed by the presented method has good control performance even if the structural model includes uncertainties, which are the modeling errors.

Harnessing the power of gate control: interventions for procedural pain and anxiety

Medical and dental procedures present a minefield of opportunities for pain and anxiety. Many procedures for diagnosis, treatment, and palliation are performed either without comfort measures at all or with sedation/anesthesia. Yet, there are many ways of decreasing patients’ procedural pain and anxiety and of increasing physical and psychological comfort. Gate control theory explains how we can close the gate on pain transmission (and minimize opening the gate) through non-pharmacological means. An exploration of several bottom-up and top-down interventions will be discussed including breathing, mindfulness, gradual exposure, non-pain stimuli, distraction, touch, and postoperative communications. Interventions will be illustrated with pictures and short videos in the dental setting.

Age-associated network controllability changes in first episode drug-naïve schizophrenia

Background Recent neuroimaging studies revealed dysregulated neurodevelopmental, or/and neurodegenerative trajectories of both structural and functional connections in schizophrenia. However, how the alterations in the brain’s structural connectivity lead to dynamic function changes in schizophrenia with age remains poorly understood. Methods Combining structural magnetic resonance imaging and a network control theory approach, the white matter network controllability metric (average controllability) was mapped from age 16 to 60 years in 175 drug-naïve schizophrenia patients and 155 matched healthy controls. Results Compared with controls, the schizophrenia patients demonstrated the lack of age-related decrease on average controllability of default mode network (DMN), as well as the right precuneus (a hub region of DMN), suggesting abnormal maturational development process in schizophrenia. Interestingly, the schizophrenia patients demonstrated an accelerated age-related decline of average controllability in the subcortical network, supporting the neurodegenerative model. In addition, compared with controls, the lack of age-related increase on average controllability of the left inferior parietal gyrus in schizophrenia patients also suggested a different pathway of brain development. Conclusions By applying the control theory approach, the present study revealed age-related changes in the ability of white matter pathways to control functional activity states in schizophrenia. The findings supported both the developmental and degenerative hypotheses of schizophrenia, and suggested a particularly high vulnerability of the DMN and subcortical network possibly reflecting an illness-related early marker for the disorder.

Bereavement Adaptation as Deflection Reduction: Bereaved Caregivers Define the Event of Dying

Affect control theory (ACT) has the potential to extend dominant understandings of adaptation to bereavement. Using narratives from bereaved caregivers, we assessed attributions they made about the death of a loved one from cancer. We transformed these attributions into actor-behavior-object events along the evaluation, potency, and activity dimensions of ACT. After creating hypothetical baseline deflections for events, we simulated the attributions as events in INTERACT. We found eight emergent categories of resolutions that caregivers used to make sense of the death: caregivers redefined the event to align with their sentiments about the deceased or the death. We also found racial differences in the attributions. White caregivers were more likely to blame themselves or others for the death of their loved one, while black caregivers were more willing to admit their deceased loved one’s faults. These findings demonstrate how caregivers make sense of their grief in a framework of cultural sentiments and underscore the utility of affect control theory in qualitative and theory-generating research.

Affect Control Theories: A Double Special Issue in Honor of David R. Heise

We introduce this two-part special issue that celebrates David Heise and his pathbreaking theories: affect control theory (ACT), affect control theory of the self (ACTS), and affect control theory of institutions (ACTI). These interlocking, multi-level, mathematically based theories explain a range of social processes, including impression formation, social interaction, trait and mood attributions, emotional experiences, emotion management, and identity adoption, and they do so in multiple languages and cultures. The 15 articles in this two-part issue test, apply, and develop the theories in new and innovative ways. After briefly summarizing each theory and Bayesian affect control theory (BayesACT), we highlight the key findings from each of the articles that follow.

An analytical approach for LQR design for improving damping performance of multi-machine power system

In a multi-machine environment, the inter-area low-frequency oscillations induced due to small perturbation(s) has a significant adverse effect on the maximum limit of power transfer capacity of power system. Conventionally, to address this issue, power systems were equipped with lead-lag power system stabilizers (CPSS) for damping oscillations of low-frequency. In recent years the research was directed towards optimal control theory to design an optimal linear-quadratic-regultor (LQR) for stabilizing power system against the small perturbation(s). The optimal control theory provides a systematic way to design an optimal LQR with sufficient stability margins. Hence, LQR provides an improved level of performance than CPSS over broad-range of operating conditions. The process of designing of optimal LQR involves optimization of associated state (Q) and control (R) weights. This paper presents an analytical approach (AA) to design an optimal LQR by deriving algebraic equations for evaluating optimal elements for weight matrix ‘Q’. The performance of the proposed LQR is studied on an IEEE test system comprising 4-generators and 10-busbars.

Modeling student engagement using optimal control theory

<p style='text-indent:20px;'>Student engagement in learning a prescribed body of knowledge can be modeled using optimal control theory, with a scalar state variable representing mastery, or self-perceived mastery, of the material and control representing the instantaneous cognitive effort devoted to the learning task. The relevant costs include emotional and external penalties for incomplete mastery, reduced availability of cognitive resources for other activities, and psychological stresses related to engagement with the learning task. Application of Pontryagin's maximum principle to some simple models of engagement yields solutions of the synthesis problem mimicking familiar behaviors including avoidance, procrastination, and increasing commitment in response to increasing mastery.</p>