A survey of models, analysis tools and compensation methods for the control of machines with friction

This tutorial/survey paper: (1) provides a concise point of departure for researchers and practitioners alike wishing to assess the current state of the art in the control and monitoring of civil engineering structures; and (2) provides a link between structural control and other fields of control theory, pointing out both differences and similarities, and points out where future research and application efforts are likely to prove fruitful. The paper consists of the following sections: section 1 is an introduction; section 2 deals with passive energy dissipation; section 3 deals with active control; section 4 deals with hybrid and semiactive control systems; section 5 discusses sensors for structural control; section 6 deals with smart material systems; section 7 deals with health monitoring and damage detection; and section 8 deals with research needs. An extensive list of references is provided in the references section.

Structural control: past, present, and future

Reinforcement learning offers to robotics a framework and set of tools for the design of sophisticated and hard-to-engineer behaviors. Conversely, the challenges of robotic problems provide both inspiration, impact, and validation for developments in reinforcement learning. The relationship between disciplines has sufficient promise to be likened to that between physics and mathematics. In this article, we attempt to strengthen the links between the two research communities by providing a survey of work in reinforcement learning for behavior generation in robots. We highlight both key challenges in robot reinforcement learning as well as notable successes. We discuss how contributions tamed the complexity of the domain and study the role of algorithms, representations, and prior knowledge in achieving these successes. As a result, a particular focus of our paper lies on the choice between model-based and model-free as well as between value-function-based and policy-search methods. By analyzing a simple problem in some detail we demonstrate how reinforcement learning approaches may be profitably applied, and we note throughout open questions and the tremendous potential for future research.

Reinforcement Learning in Robotics: A Survey.

The task-switching paradigm offers enormous possibilities to study cognitive control as well as task interference. The current review provides an overview of recent research on both topics. First, we review different experimental approaches to task switching, such as comparing mixed-task blocks with single-task blocks, predictable task-switching and task-cuing paradigms, intermittent instructions, and voluntary task selection. In the 2nd part, we discuss findings on preparatory control mechanisms in task switching and theoretical accounts of task preparation. We consider preparation processes in two-stage models, consider preparation as an all-or-none process, address the question of whether preparation is switch-specific, reflect on preparation as interaction of cue encoding and memory retrieval, and discuss the impact of verbal mediation on preparation. In the 3rd part, we turn to interference phenomena in task switching. We consider proactive interference of tasks and inhibition of recently performed tasks indicated by asymmetrical switch costs and n-2 task-repetition costs. We discuss stimulus-based interference as a result of stimulus-based response activation and stimulus-based task activation, and response-based interference because of applying bivalent rather than univalent responses, response repetition effects, and carryover of response selection and execution. In the 4th and final part, we mention possible future research fields.

/pdf/control-and-interference-in-task-switching-a-review-4jdle9loi9.pdf

Control and interference in task switching--a review.

http://flyingv.ucsd.edu/papers/PDF/35.pdf

Brief Stability of extremum seeking feedback for general nonlinear dynamic systems

This book, written by two major figures in adaptive control, provides a wealth of material for researchers, practitioners, and students. While some researchers in adaptive control may note the absence of a particular topic, the book‘s scope represents a high-gain instrument. It can be used by designers of control systems to enhance their work through the information on many new theoretical developments, and can be used by mathematical control theory specialists to adapt their research to practical needs. The book is strongly recommended to anyone interested in adaptive control.

B. Pasik-Duncan

Papers

Adaptive Control