Mark Cannon

TL;DR: In this paper, the authors synthesize the authors' wide research in this field to offer a strategy for achieving the objective, which relates technical and social barriers to three key activities, identifying failure, analyzing failure and deliberate experimentation, to develop six recommendations for action.

Abstract: This paper contributes to a growing body of research on shared cognition by examining shared beliefs about failure in organizational work groups. We argue that the popular ideal of organizational learning from failure is likely to be impeded by powerful psychological and organizational barriers to engaging in behaviors through which this can occur. We hypothesize that people hold tacit beliefs about appropriate responses to mistakes, problems and conflict, and that these are shared within and vary between organizational work groups (H1). These shared beliefs vary in the extent to which they take a learning approach to failure – specifically in the extent to which they endorse identifying, discussing, and analysing mistakes, problems, and conflicts. We also hypothesize that effective coaching, clear direction and a supportive work context influence beliefs related to failure (H2), and that beliefs about failure influence group performance (H3). These hypotheses combine to suggest a theoretical model of antecedents and consequences of shared beliefs about failure in work groups. The paper presents empirical evidence from a recent field study to test the model and finds support for Hypotheses 1, 2, and 3. Hypothesis 4 – that shared beliefs about failure mediate between the antecedents and the outcome of group performance – was not supported. Copyright © 2001 John Wiley & Sons, Ltd.

TL;DR: Graduate students pursuing courses in model predictive control or more generally in advanced or process control and senior undergraduates in need of a specialized treatment will find Model Predictive Control an invaluable guide to the state of the art in this important subject.

TL;DR: A model describing the evolution of predicted tube scalings simplifies the computation of stochastic tubes, and the resulting MPC scheme has a low online computational load even for long prediction horizons, thus allowing for performance improvements.

TL;DR: The model predictive control of nonlinear parameter varying systems via receding horizon control Lyapunov functions and nonlinear model-algorithmic control for multivariable nonminimum-phase processes are reviewed.