Constrained optimal control theory for differential linear repetitive processes

Differential repetitive processes are a distinct class of continuous-discrete two-dimensional linear systems of both systems theoretic and applications interest. These processes complete a series of sweeps termed passes through a set of dynamics defined over a finite duration known as the pass length, and once the end is reached the process is reset to its starting position before the next pass begins. Moreover the output or pass profile produced on each pass explicitly contributes to the dynamics of the next one. Applications areas include iterative learning control and iterative solution algorithms, for classes of dynamic nonlinear optimal control problems based on the maximum principle, and the modeling of numerous industrial processes such as metal rolling, long-wall cutting, etc. In this paper we develop substantial new results on optimal control of these processes in the presence of constraints where the cost function and constraints are motivated by practical application of iterative learning control to robotic manipulators and other electromechanical systems. The analysis is based on generalizing the well-known maximum and $\epsilon$-maximum principles to the

Abnormal Perceptual Sensitivity in Body-Focused Repetitive Behaviors

Objective Several compulsive grooming habits such as hair pulling, skin picking, and nail biting are collectively known as body-focused repetitive behaviors (BFRBs). Although subclinical BFRBs are common and benign, more severe and damaging manifestations exist that are difficult to manage. Researchers have suggested that BFRBs are maintained by various cognitive, affective, and sensory contingencies. Although the involvement of cognitive and affective processes in BFRBs has been studied, there is a paucity of research on sensory processes. Methods The current study tested whether adults with subclinical or clinical BFRBs would report abnormal patterns of sensory processing as compared to a healthy control sample. Results Adults with clinical BFRBs (n = 26) reported increased sensory sensitivity as compared to persons with subclinical BFRBs (n = 48) and healthy individuals (n = 33). Elevations in sensation avoidance differentiated persons with clinical versus subclinical BFRBs. Sensation seeking patterns were not different between groups. Unexpectedly, BFRB severity was associated with lower registration of sensory stimuli, but this finding may be due to high psychiatric comorbidity rates in the BFRB groups. Conclusions These findings suggest that several sensory abnormalities may underlie BFRBs. Implications for the etiology and treatment of BFRBs are discussed

Learning for Advanced Motion Control

Iterative Learning Control (ILC) can achieve perfect tracking performance for mechatronic systems. The aim of this paper is to present an ILC design tutorial for industrial mechatronic systems. First, a preliminary analysis reveals the potential performance improvement of ILC prior to its actual implementation. Second, a frequency domain approach is presented, where fast learning is achieved through noncausal model inversion, and safe and robust learning is achieved by employing a contraction mapping theorem in conjunction with nonparametric frequency response functions. The approach is demonstrated on a desktop printer. Finally, a detailed analysis of industrial motion systems leads to several shortcomings that obstruct the widespread implementation of ILC algorithms. An overview of recently developed algorithms, including extensions using machine learning algorithms, is outlined that are aimed to facilitate broad industrial deployment.Comment: 8 pages, 15 figures, IEEE 16th International Workshop on Advanced Motion Control, 202

Sensory Processing Patterns and Internalizing Behaviors in the Pediatric and Young Adult General Population: A Scoping Review

Background: While research has largely focused on the relationship between sensory processing patterns and internalizing behaviors (e.g., anxiety, depression) in children with autism spectrum disorder or attention deficit hyperactivity disorder, this relationship is not fully understood among the general population or across development. Method: This scoping review addressed the following research question: How are sensory processing patterns associated with internalizing behaviors (e.g., anxiety, depression) among children with various conditions as well as typically developing children from birth to 22 years of age? Results: Since 2005, n = 97 peer-reviewed articles have addressed this topic and were included in the current review. Overall, findings show a complex interplay between sensory processing patterns, internalizing behaviors, cognitive factors (intolerance of uncertainty, ritualism, cognitive rigidity), and personality characteristics. Discussion: The results of this review showed that research primarily focused on individuals with ASD, and many articles used mediation models to examine complex relationships. Implications for future research are discussed

Noninvasive brain stimulation techniques can modulate cognitive processing

Recent methods that allow a noninvasive modulation of brain activity are able to modulate human cognitive behavior. Among these methods are transcranial electric stimulation and transcranial magnetic stimulation that both come in multiple variants. A property of both types of brain stimulation is that they modulate brain activity and in turn modulate cognitive behavior. Here, we describe the methods with their assumed neural mechanisms for readers from the economic and social sciences and little prior knowledge of these techniques. Our emphasis is on available protocols and experimental parameters to choose from when designing a study. We also review a selection of recent studies that have successfully applied them in the respective field. We provide short pointers to limitations that need to be considered and refer to the relevant papers where appropriate

Input variable selection in time-critical knowledge integration applications: A review, analysis, and recommendation paper

This is the post-print version of the final paper published in Advanced Engineering Informatics. The published article is available from the link below. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. Copyright @ 2013 Elsevier B.V.The purpose of this research is twofold: first, to undertake a thorough appraisal of existing Input Variable Selection (IVS) methods within the context of time-critical and computation resource-limited dimensionality reduction problems; second, to demonstrate improvements to, and the application of, a recently proposed time-critical sensitivity analysis method called EventTracker to an environment science industrial use-case, i.e., sub-surface drilling. Producing time-critical accurate knowledge about the state of a system (effect) under computational and data acquisition (cause) constraints is a major challenge, especially if the knowledge required is critical to the system operation where the safety of operators or integrity of costly equipment is at stake. Understanding and interpreting, a chain of interrelated events, predicted or unpredicted, that may or may not result in a specific state of the system, is the core challenge of this research. The main objective is then to identify which set of input data signals has a significant impact on the set of system state information (i.e. output). Through a cause-effect analysis technique, the proposed technique supports the filtering of unsolicited data that can otherwise clog up the communication and computational capabilities of a standard supervisory control and data acquisition system. The paper analyzes the performance of input variable selection techniques from a series of perspectives. It then expands the categorization and assessment of sensitivity analysis methods in a structured framework that takes into account the relationship between inputs and outputs, the nature of their time series, and the computational effort required. The outcome of this analysis is that established methods have a limited suitability for use by time-critical variable selection applications. By way of a geological drilling monitoring scenario, the suitability of the proposed EventTracker Sensitivity Analysis method for use in high volume and time critical input variable selection problems is demonstrated.E