N-2(+)((2)Sigma(g)) and Rb(S-2) in a hybrid trap: modeling ion losses from radiative association paths

By employing ab initio computed intermolecular potential energy surfaces we calculate the radiative association probabilities and rates for two different associative mechanisms involving trapped molecular ions N-2(+)((2)sigma(g)) interacting either directly with ultracold Rb atoms or undergoing charge-exchange (CE) processes leading to the formation of complexes of the strongly exothermic products N-2(X-1 sigma(g)) plus Rb+(S-1(0)). The two processes are expected to provide possible paths to ion losses in the trap within the timescale of experiments. The present calculations suggest that the associative rates for the vibrational' direct process are too small to be of any significant importance at the millikelvin temperatures considered in the experiments, while the vibronic' path into radiatively associating the CE products has a probability of occurring which is several orders of magnitude larger. However the reaction rate constants attributed to non-adiabatic CE [F. H. J. Hall and S. Willist, Phys. Rev. Lett., 2012, 109, 233202] are in turn several orders of magnitude larger than the radiative ones calculated here, thereby making the primary experimental process substantially unaffected by the radiative losses channel

Synchronization of coupled limit cycles

A unified approach for analyzing synchronization in coupled systems of autonomous differential equations is presented in this work. Through a careful analysis of the variational equation of the coupled system we establish a sufficient condition for synchronization in terms of the geometric properties of the local limit cycles and the coupling operator. This result applies to a large class of differential equation models in physics and biology. The stability analysis is complemented with a discussion of numerical simulations of a compartmental model of a neuron.Comment: Journal of Nonlinear Science, accepte

Subject specific demands of teaching: Implications for out-of-field teachers

This chapter provides a framework for thinking about the subject-specific nature of teaching in terms of the knowledge, modes of inquiry and discursive practices that delineate one subject from another in the traditional school curriculum. The chapter will explore how these disciplinary traits are translated into teaching as curriculum, knowledge and pedagogy, and how this subject-specificity of teaching is juxtaposed against the more generic aspects of teaching. The chapter explores the idea that if a teacher’s expertise can be situated within a field, then they can also be positioned out-of-field. Implications for teaching out-of-field are discussed in terms of the subject-specific knowledge, processes and skills, and the difficulties associated with teacher practice. English and Australian illustrations of teacher practices from in-field and out-of-field situations are provided, in particular highlighting the demands of moving across subject boundaries. Cross-fertilisation is especially evident when subjects are integrated, therefore, the issues associated with integrated curriculum are discussed where the traditional subject boundaries are being challenged as schools are reorganised to integrate subjects through, for example, STEM teaching, or holistic curriculum designs

Data-driven predictive current control for synchronous motor drives

Data-driven control techniques have become increasingly popular in recent years due to the availability of massive amounts of data and several advances in data science. These control design methods bypass the system identification step and directly exploit collected data to construct the controller. In this paper, we investigate the application of data-driven methods to the control of electric motor drives, and specifically to the design of current controllers for three-phase synchronous permanent magnet motor drives. Two of the most promising data-driven algorithms are presented, namely the Subspace Predictive Control algorithm and the Data-Enabled Predictive Control algorithm. The theory behind these techniques is first reviewed in the optimization-based control framework. Standard algorithms are slightly modified to fulfill the requirements of the specific application, and then simulated in the MATLAB Simulink environment. Some key aspects of real-time implementation are studied, providing a proof-of-concept demonstration of the applicability of these algorithms. The data-driven design is proposed for three different topologies of synchronous motors, proving the flexibility of the approach

Data-Driven Continuous-Set Predictive Current Control for Synchronous Motor Drives

Optimization-based control strategies are an affirmed research topic in the area of electric motors drives. These methods typically rely on an accurate parametric representation of the motor equations. In this paper, we present the transition from model-based towards data-driven optimal control strategies. We start from the model predictive control paradigm which uses the voltage balance model of the motor. Second, we discuss the prediction error method, where a state-space model is identified from data, without a parametrization. Moving toward data-driven controls, we present the Subspace Predictive Control, where a reduced model is constructed based on the singular value decomposition of raw data. The final step is represented by a complete data-driven approach, named data-enabled predictive control, in which raw data is not encoded into a model but directly used in the controller. The theory behind these techniques is reviewed and applied for the first time to the design of the current controller of synchronous permanent magnet motor drives. Design guidelines are provided to practitioners for the proposed application and a way to address offset-free tracking is discussed. Experimental results demonstrate the feasibility of the real-time implementation and provide comparisons between model-based and data-driven controls

Real-Time Feasibility of Data-Driven Predictive Control for Synchronous Motor Drives

The data-driven control paradigm allows overcoming conventional troubles in the controller design related to model identifications procedures. Raw data are directly exploited in the control input selection by forcing the future plant dynamics to be coherent with previously collected samples. This paper focuses, in particular, on the data-enabled predictive control algorithm. A relevant disadvantage of this algorithm is the fact that the complexity of the online control program grows with the dimension of the data-set. This issue becomes particularly relevant when considering embedded applications such as the control of synchronous motor drives, characterized by challenging real-time constraints. This work proposes a systematic approach for dramatically reducing the complexity of such algorithms. Such methodology enables real-time feasibility of the constrained version of this control structure, which was previously precluded. Simulations and experimental results are provided to validate the method, considering the current control of an interior permanent magnet motor as test-case