A metaheuristic particle swarm optimization approach to nonlinear model predictive control

This paper commences with a short review on optimal control for nonlinear systems, emphasizing the Model Predictive approach for this purpose. It then describes the Particle Swarm Optimization algorithm and how it could be applied to nonlinear Model Predictive Control. On the basis of these principles, two novel control approaches are proposed and anal- ysed. One is based on optimization of a numerically linearized perturbation model, whilst the other avoids the linearization step altogether. The controllers are evaluated by simulation of an inverted pendulum on a cart system. The results are compared with a numerical linearization technique exploiting conventional convex optimization methods instead of Particle Swarm Opti- mization. In both approaches, the proposed Swarm Optimization controllers exhibit superior performance. The methodology is then extended to input constrained nonlinear systems, offering a promising new paradigm for nonlinear optimal control design.peer-reviewe

Estimation of temporal and spatio-temporal nonlinear descriptor systems

As advances in the remote sensing of fluid flows forge ahead at an impressive rate, we face an increasingly compelling question of how best to exploit this progress. Light detection and ranging (LIDAR) measurement equipment still presents the problems of having only radial (line-of-sight) wind speed measurements (Cyclops' dilemma). Substantial expanses of unmeasured flow still remain and range weighting errors have a considerable influence on LIDAR measurements. Clearly, more information needs to be extracted from LIDAR data and an estimation problem naturally arises. A key challenge is that most established estimation techniques, such as Kalman filters, cater for systems that are finite-dimensional and described by ordinary differential equations (ODEs). By contrast, many fluid flows are governed by the Navier-Stokes equations, which are nonlinear partial differential-algebraic equations (PDAEs). With this motivation in mind, this thesis proposes a novel statistical signal processing framework for the model-based estimation of a class of spatio-temporal nonlinear partial differential-algebraic equations (PDAEs). The method employs finite-dimensional reduction that converts this formulation to a nonlinear DAE form for which new unscented transform-based filtering and smoothing algorithms are proposed. Gaussian approximations are derived for differential state variables and more importantly, extended to algebraic state variables. A mean-square error lower bound for the nonlinear descriptor filtering problem is obtained based on the posterior Cramér-Rao inequality. The potential of adopting a descriptor systems approach to spatio-temporal estimation is shown for a wind field estimation problem. A basis function decomposition method is used in conjunction with a pressure Poisson equation (PPE) formulation to yield a spatially-continuous, strangeness-free, reduced-order descriptor flow model which is used to estimate unmeasured wind velocities and pressure over the entire spatial region of interest using sparse measurements from wind turbine-mounted LIDAR instruments. The methodology is validated for both synthetic data generated from large eddy simulations of the atmospheric boundary layer and real-world LIDAR measurement data. Results show that a reconstruction of the flow field is achievable, thus presenting a validated estimation framework for potential applications including wind gust prediction systems, the preview control of wind turbines and other spatio-temporal descriptor systems spanning several disciplines

Locked out, but not disconnected: multilingual community engagement in Australia

At the onset of COVID-19, many Local Government Areas (LGAs) indicated they were struggling to communicate effectively with multilingual migrant communities. Communities were isolated from vital LGA support due to factors including the digital divide, barriers to language access, and top-down communication strategies. The pandemic also provided insights into the ways migrant communities mitigate hardship by engaging in placemaking and place-shaping, using existing networks and resources to provide vital support during crisis, which requires significant invisible labour. In this article, we present three case studies from a larger community-based project which began in early 2020 with an LGA in Western Australia. We use case narratives to illustrate and analyse three common actions migrant women used to engage their communities prior to, and during, COVID-19 recovery. These simple, yet profound actions, which include visiting communities, acknowledging challenges, and identifying opportunities further evidence the ways community leaders facilitate culturally sustaining placemaking, even during crisis; they underscore the intense emotional, cultural, and linguistic labour required to enact support in contexts where resources are inaccessible or misaligned with community stories. We argue it is only in partnership with communities that LGAs can learn to address some of the long-standing issues COVID-19 highlights

Patients’ perceived health information needs in inflammatory arthritis: A systematic review

Objectives: To identify the breadth of the literature regarding patients’ perceived health information needs related to inflammatory arthritis care. Methods: A systematic scoping review of MEDLINE, EMBASE, CINAHL and PsycINFO was performed to identify relevant articles (1990 -2016) examining patients’ perceived needs relating to health information in inflammatory arthritis. Data and themes were identified and categorised and risk of bias assessed. Results: Twenty nine studies (11 quantitative, 14 qualitative and 4 mixed methods) from 4121 identified articles were relevant for inclusion. Most focussed on rheumatoid arthritis. Key findings included: (1) Reasons for seeking health information often focussed on gaining ownership over their condition and facilitating self-management. (2) Demographic differences in information needs were inconsistent, but women and younger patients generally reported more needs. (3) Desired information content was broad, and included targeted and practical information covering disease treatment and psychosocial wellbeing. (4) Preferred information delivery method was consultation with a Rheumatologist; however group sessions had advantages for psychosocial issues while written information provided useful supplementation. (5) Barriers to meeting health information needs were around timely access. Conclusions: Patients with inflammatory arthritis have high information needs, desiring practical and individualised information. When developing strategies to meet patients’ information needs, aligning patient expectations with delivery methods that are accessible, cost-effective and flexible may help to optimize patient outcomes