Everybody wants to get better at what they do or at least they should want to. The problem is, not everyone knows exactly how to do this, to achieve measurable and lasting performance improvement. This book will help people and organizations achieve those desired results by helping them focus on a positive approach to what makes the performer tick.

"Presenting a simple yet effective new way to improve performance, Positive Performance Improvement veers away from the prevailing focus on fixing what is wrong with workers and the workplace today - performance gaps, personal deficiencies, weaknesses in core strengths. Instead, it zeros in on what can make the most dramatic impact and ensure long-lasting improvement." "Drawing on the latest research in positive psychology, research in positive psychology, appreciative inquiry, and sport psychology, authors Gerson and Gerson introduce important building blocks for creating strong relationships; questionnaires and assessments to help uncover clues to what drives an individual's success; and a Talent Optimization Performance System that makes their Positive CORE model both tactical and practical in any coaching, performance appraisal, or ongoing performance management situation."--Jacket

Fundamentals of Performance Improvement, 3rd Edition Fundamentals of Performance Improvement is a substantially new version of the down-to-earth, how-to guide designed to help business leaders, practitioners, and students understand the science and art of performance technology and successfully implement organizational and societal change. Using the Performance Improvement / Human Performance Technology (HPT) model, the expert authors explain step-by-step how to spot performance indicators, analyze problems, identify underlying causes, describe desired results, and create workable solutions. “It does not matter what function you align yourself to in your organization, this book allows you to tap into the secrets that drive organizational success. Several books work to define what is performance improvement and performance technology. This one also provides insights into the Why? And How?” —CEDRIC T. COCO, CPT, SVP, Learning and Organizational Effectiveness, Lowe’s Companies “Fundamentals of Performance Improvement is full of practical models and tools for improving the world by partnering with customers, clients, constituents, and colleagues. It provides a path forward for successful transformation and performance improvement at personal, group and collective levels. It is a must read for leaders and consultants seeking to advance opportunities in new and emerging situations.” —DIANA WHITNEY, PhD, president, Corporation for Positive Change “If you have an interest in performance improvement, this is simply the best available book on the topic. It addresses the science and craft as well as the intricacies of how to improve workplace performance. Van Tiem, Moseley, and Dessinger have incorporated into this work the best available research on the Certified Performance Technology (CPT) standards and process.” —JAMES A. PERSHING, Ph.D., CPT, professor emeritus, Workplace Learning and Performance Improvement, Indiana University “Its international flavor, with practitioner comments and examples drawn from across the world, enhances its appeal as more and more professionals operate in an increasingly global context.” —DALJIT SINGH, Asia Pacific Director of Talent Management, Baker & McKenzie, Sydney, Australia

This book reports on a set of advances relating to nonlinear observer design, with a special emphasis on high-gain observers. First, it covers the design of filters and their addition to the observer for reducing noise, a topic that has been so far neglected in the literature. Further, it describes the adaptive re-design of nonlinear observers to reduce the effect of parametric uncertainty. It discusses several limitations of classical methods, presenting a set of successfull solutions, which are mathematically formalised through Lyapunov stability analysis, and in turn validated via numerical simulations. In the second part of the book, two applications of the adaptive nonlinear observers are described, such in the estimation of the liquid water in a hydrogen fuel cell and in the solution of a common cybersecurity problem, i.e. false data injection attacks in DC microgrids. All in all, this book offers a comprehensive report on the state-of-the-art in nonlinear observer design for energy systems, including mathematical demonstrations, and numerical and and experimental validations.

The performance of model predictive control (MPC) largely depends on the accuracy of the prediction model and of the constraints the system is subject to. However, obtaining an accurate knowledge of these elements might be expensive in terms of money and resources, if at all possible. In this thesis, we develop novel learning-based MPC frameworks that actively incentivize learning of the underlying system dynamics and of the constraints, while ensuring recursive feasibility, constraint satisfaction, and performance bounds for the closed-loop. In the first part, we focus on the case of inaccurate models, and analyze learning-based MPC schemes that include, in addition to the primary cost, a learning cost that aims at generating informative data by inducing excitation in the system. In particular, we first propose a nonlinear MPC framework that ensures desired performance bounds for the resulting closed-loop, and then we focus on linear systems subject to uncertain parameters and noisy output measurements. In order to ensure that the desired learning phase occurs in closed-loop operations, we then propose an MPC framework that is able to guarantee closed-loop learning of the controlled system. In the last part of the thesis, we investigate the scenario where the system is known but evolves in a partially unknown environment. In such a setup, we focus on a learning-based MPC scheme that incentivizes safe exploration if and only if this might yield to a performance improvement.