Localization with Noisy Android Raw GNSS Measurements

Android raw Global Navigation Satellite System (GNSS) measurements are expected to bring smartphones power to take on demanding localization tasks that are traditionally performed by specialized GNSS receivers. The hardware constraints, however, make Android raw GNSS measurements much noisier than geodetic-quality ones. This study elucidates the principles of localization using Android raw GNSS measurements and leverages Moving Horizon Estimation (MHE), Extended Kalman Filter (EKF), and Rauch-Tung-Striebel (RTS) smoother for noise suppression. Experimental results show that the RTS smoother achieves the best positioning performance, with horizontal positioning errors significantly reduced by 76.4% and 46.5% in static and dynamic scenarios compared with the baseline weighted least squares (WLS) method. Our codes are available at https://github.com/ailocar/androidGnss

Model-based predictive controller for real-time industrial applications

This report gives the formulation of the state space Generalised Predictive Control as published in (Ling and Lim, 1996). Both the single rate and multirate control schemes are presented. It then presents a detail analysis of the state estimation scheme.RP 30/9

Designing online problem representation engine for conceptual change

Purpose: This paper aims to describe the web-based scaffold dynamic simulation system (PRES-on) designed for pre-service teachers. Design/methodology/approach: The paper describes the initial design of a web-based scaffold dynamic simulation system (PRES-on) as a cognitive tool for learners to represent problems. For the widespread use of the PRES-on, the authors adopt the design experiment methodology to advance pre-service teachers' pedagogical applications of such engine for effective teaching and learning through the integration of PRES-on into the existing pre-service course. Findings: A proto-type of the system has been completed and the authors are in the process of enhancing its features. Ultimately, they will build in all the features stated in the literature review and integrate it into pre-service teachers' curriculum where the aim is to seek to understand its pedagogical applications. The current design only incorporated a quantitative component. The authors are in the process of embedding features that will allow users to build in qualitative representation and different level of question prompts that will facilitate users' cognitive processes. Originality/value: This study contributes to the growing interests among researchers and educators in using web-based technologies to foster deep learning. Being one of the first initiatives to develop a web-based scaffold dynamic simulation system for problem solving purpose, PRES-on will inform the understanding on how new technological developments could enhance learning in environments that involve multidisciplinary problem solving

Robust multiplexed model predictive control

This paper extends the recently developed multiplexed model predictive control (MMPC) concept to ensure satisfaction of hard constraints despite the action of persistent, unknown but bounded disturbances. MMPC uses asynchronous control moves on each input channel instead of synchronised moves on all channels. It offers reduced computation, by dividing the online optimisation into a smaller problem for each channel, and potential performance improvements, as the response to a disturbance is quicker, albeit via only one channel. Robustness to disturbances is introduced using the constraint tightening approach, tailored to suit the asynchronous updates of MMPC and the resulting time-varying optimisations. Numerical results are presented, involving a simple mechanical example and an aircraft control example, showing the potential computational and performance benefits of the new robust MMPC.ASTAR (Agency for Sci., Tech. and Research, S’pore)Published versio