Multiparameter Identification of Permanent Magnet Synchronous Motor Based on Model Reference Adaptive System—Simulated Annealing Particle Swarm Optimization Algorithm

The accurate identification of permanent magnet synchronous motor (PMSM) parameters is the basis for high-performance drive control. The traditional PMSM multiparameter identification method experiences problems with the uncertainty of the identification results and low identification accuracy due to the under-ranking of the mathematical model of motor control. A multiparameter identification of PMSM based on a model reference adaptive system and simulated annealing particle swarm optimization (MRAS-SAPSO) is proposed here. The algorithm first identifies the electrical parameters of the PMSM (stator winding resistance R, cross-axis inductance L, and magnetic linkage ψf) by means of the model reference adaptive system method. Second, the result is used as the initial population in particle swarm optimization identification to further optimize and identify the electrical and mechanical parameters (moment of inertia J and damping coefficient B) in the motor control system. Additionally, in order to avoid problems such as premature convergence of the particle swarm in the optimization search process, the results of the adaptive simulated annealing algorithm to optimize multiparameter identification are introduced. The simulation experiment results show that the five identification parameters obtained by the MRAS-SAPSO algorithm are highly accurate and stable, and the errors between them and the real values are below 2%. This also verifies the effectiveness and reliability of this identification method.

DEVELOPMENT OF A MODEL OF THE IRON ORE CONCENTRATE DEWATERING PROCESS

Исследование направлено на разработку адаптивной системы автоматического управления процессом обезвоживания железорудного концентрата, от протекания которого зависит производительность передела, качество и себестоимость выходного продукта данного процесса и дальнейших переделов. The research is aimed at developing an adaptive system for automatic control of the iron ore concentrate dewatering process, on the course of which the processing productivity, quality and cost of the output product of this process and further processing depends.

Phase Transitions and Bifurcation Points in the Initiation Dynamics of Venture Emergence: A Conceptual Framework

Based on isomorphic considerations, this paper attempts to establish an entrepreneur as complex adaptive system, which is one of the concepts that appear prominently in the field of complexity sciences. The attempt to equate the notion of an entrepreneur with the idea of a complex adaptive system, presupposes recognition of the entrepreneur’s role in adaptive agency. Along with this recognition, comes the convenience of contextualizing the concepts of phase transitions and bifurcation points in terms of venture emergence. The dynamics of these concepts are however more commonly explored within the workings of complex or dynamic physical systems. Yet, the broad applicability of the underlying ideas offers the possibility of identifying similar concepts in biological systems and by extension, the field of entrepreneurial cognition and behavior. Thus, the paper adopts an interdisciplinary approach and employs retroductive reasoning in the assemblage of relevant ideas, sought from diverse literary sources. The outcome is a conceptual framework, which presents certain propositions that offer implication for action.

Collaborate as a flock in the organization: how selection and synthesis influence knowledge convergence within a complex adaptive system

Purpose This study aims to investigate how two collaborative methods – selection and synthesis – influence knowledge convergence when people articulate a new strategic direction driving transformation within the organization. Design/methodology/approach The study is based on a longitudinal field experiment developed in four organizations involving 82 employees over a three-month process. Inspired by dynamics governing flocks as complex adaptive systems, selection and synthesis have been separately used in two sets of companies. Primary and secondary data have been largely collected and analyzed throughout the whole process. Findings This study describes how the two alternative methods differently influenced two kinds of knowledge convergence. While selection triggers a general and static knowledge convergence and the propagation of individual knowledge over time, synthesis fosters a local and dynamic knowledge convergence where individuals tend to propagate knowledge generated collectively. Research limitations/implications This research offers insights into understanding the influence of alternative collaborative methods on the creation and propagation of knowledge when people are converging toward a new strategic direction. From a theoretical perspective, it contributes to complex adaptive system theory, highlighting the role of knowledge convergence and emergence through collaboration. Practical implications This research offers insights to managers who deal with the complexity of the engagement of different stakeholders during collaborative processes, offering some actionable takeaways to foster knowledge convergence by alternatively employing selection and synthesis. Originality/value This paper contributes to the management and social information processing literature emphasizing the role of knowledge convergence emerging from the complex interactions among multiple stakeholders.