State-Space Approach for SPMSM Sensorless Passive Algorithm Tuning

Sensorless algorithms for Permanent Magnet Synchronous Motors (PMSM) have achieved increasing interest in the technical literature over the last few years. They can be divided into active methods and passive methods: the first inject high-frequency signals exploiting rotor anisotropy, whereas the second are based on observers. Recently, a sensorless control based on a rotor flux observer has been presented in the technical literature, which gives very accurate results in terms of rotor position estimation and robustness. In this paper, the aforementioned observer is considered and a procedure for choosing stabilizing gains of the observer is proposed. The contribution of the paper is three-fold: the mathematical modelling of the rotor flux observer, the methodology for the definition of the observer gains, and the presentation of the experimental results.

A state-space approach to understand responses of organisms, populations and communities to multiple environmental drivers

Understanding the response of biotic systems to multiple environmental drivers is one of the major concerns in ecology. The most common approach in multiple driver research includes the classification of interactive responses into categories (antagonistic, synergistic). However, there are situations where the use of classification schemes limits our understanding or cannot be applied. Here, we introduce and explore an approach that allows us to better appreciate variability in responses to multiple drivers. We then apply it to a case, comparing effects of heatwaves on performance of a cold-adapted species and a warm-adapted competitor. The heatwaves had a negative effect on the native (but not on the exotic) species and the approach highlighted that the exotic species was less responsive to multivariate environmental variation than the native species. Overall, we show how the proposed approach can enhance our understanding of variation in responses due to different driver intensities, species, genotypes, ontogeny, life-phases or among spatial scales at any level of biological organization.

Deep state-space Gaussian processes

This paper is concerned with a state-space approach to deep Gaussian process (DGP) regression. We construct the DGP by hierarchically putting transformed Gaussian process (GP) priors on the length scales and magnitudes of the next level of Gaussian processes in the hierarchy. The idea of the state-space approach is to represent the DGP as a non-linear hierarchical system of linear stochastic differential equations (SDEs), where each SDE corresponds to a conditional GP. The DGP regression problem then becomes a state estimation problem, and we can estimate the state efficiently with sequential methods by using the Markov property of the state-space DGP. The computational complexity scales linearly with respect to the number of measurements. Based on this, we formulate state-space MAP as well as Bayesian filtering and smoothing solutions to the DGP regression problem. We demonstrate the performance of the proposed models and methods on synthetic non-stationary signals and apply the state-space DGP to detection of the gravitational waves from LIGO measurements.

Towards a functional classification of behaviour: a taxonomy based on outcomes

The classification of behaviour has historically been done using one of the two approaches, either through the hypothetical causes (such as ‘instincts’, ‘drives’ and ‘needs’) or through the cataloguing of the observable form of behaviour using an ethogram. This article offers an alternative framework for classification of behaviour based upon only the behavioural outcomes. The framework is specified from first principles of a state-space approach, allowing us to discuss intermediate outcomes that may have instrumental value. This approach could provide a firmer foundation to consider the hierarchical nature of goals and allows us to address both the ‘how’ and the ‘why’ questions within a single framework. This taxonomy is designed to complement rather than replace existing attempts; the classification of behaviour by outcome is orthogonal to questions of the mechanisms of decision making or of the implementation of actions. This article specifies nine basic classes of behaviour and provides precise definitions for each of these. We then develop a formal language for the description of observed activities, the representation of behavioural hierarchies and for the analysis of possibility sets for achieving future goals. We follow up with some critique and discussion of the problems such a framework poses.

Comparison of time-based versus state–space stand growth models for tropical hybrid Eucalyptus clonal plantations in Sumatera, Indonesia

The pulp and paper industry in Indonesia is the tenth largest producer in the world, with Acacia and Eucalyptus as the main genera used for production; however, limited publications exist related to Eucalyptus growth models in Indonesia compared with other regions. Time-based models have been developed in which height, stand density, and basal area are predicted based on initial conditions and age. In contrast, a state–space approach utilizes the rate of change of these three state variables. Previous direct comparisons of these two approaches are generally limited. Consequently, the objective of this study was to compare two stand-level growth modeling approaches for Eucalyptus hybrid species on Sumatera (Sumatra) Island using both time-based and state–space methods. Our results indicate that dynamic models using either time-based or state–space approaches are adequate for predicting stand parameters to rotation age. A modified Bazukis matrix indicated that the behavior of both methods produced reliable predictions that were biologically reasonable in terms of stand development; however, the time-based approach provided better performance than the state–space approach on a variety of equivalence tests and goodness-of-fit statistics. Overall, the analysis highlights the advantages and disadvantages of these two commonly used, yet highly contrasting, stand-level growth modeling approaches, which need further consideration and evaluation.