Bayesian model predictive control: Efficient model exploration and regret bounds using posterior sampling

Tight performance specifications in combination with operational constraints make model predictive control (MPC) the method of choice in various industries. As the performance of an MPC controller depends on a sufficiently accurate objective and prediction model of the process, a significant effort in the MPC design procedure is dedicated to modeling and identification. Driven by the increasing amount of available system data and advances in the field of machine learning, data-driven MPC techniques have been developed to facilitate the MPC controller design. While these methods are able to leverage available data, they typically do not provide principled mechanisms to automatically trade off exploitation of available data and exploration to improve and update the objective and prediction model. To this end, we present a learning-based MPC formulation using posterior sampling techniques, which provides finite-time regret bounds on the learning performance while being simple to implement using off-the-shelf MPC software and algorithms. The performance analysis of the method is based on posterior sampling theory and its practical efficiency is illustrated using a numerical example of a highly nonlinear dynamical car-trailer system

The compounding effects of obesity on the development of Intimal Hyperplasia following vascular intervention : a histopathological analysis

Includes abstract.Includes bibliographical references (leaves 100-111).This study examines the histopathological response to injury following both balloon angioplasty and endovascular stenting in the Zucker rat, a model that allows interpretation of the role of obesity as well as progressive glucose intolerance and hyperinsulimaemia. Lean and obese Zucker fatty rats and Zucker diabetic fatty rat (ZDF) were subjected to balloon injury with or without stenting. The development of IH, along with the histological response to injury was analyzed

Working Length Determination in Palatal Roots of Maxillary Molars

The aim of this study was to determine if a buccal curvature in the palatal roots of maxillary molars affected the clinician’s ability to accurately determine working length. Twenty-seven extracted, human maxillary molars were sorted by palatal root curvatures as J- and C-type and the angle of curvature was determined. Straight-line access was made and a #20 file was placed into the canal until the tip was visible at the apical foramen then withdrawn. The file, tooth and calibration wire were radiographed on one image using the RVG. Actual (file) and radiographic (tooth) lengths were determined using the RVG ruler. Radiographic length appeared shorter on average than the actual length. Canal curvatures larger than 25 degrees had differences greater than 0.5mm. This represents a statistically significant difference between the actual and radiographic lengths as the degree of curvature increases. There was no significant difference between the J- and C- types

Performance and safety of Bayesian model predictive control: Scalable model-based RL with guarantees

Despite the success of reinforcement learning (RL) in various research fields, relatively few algorithms have been applied to industrial control applications. The reason for this unexplored potential is partly related to the significant required tuning effort, large numbers of required learning episodes, i.e. experiments, and the limited availability of RL methods that can address high dimensional and safety-critical dynamical systems with continuous state and action spaces. By building on model predictive control (MPC) concepts, we propose a cautious model-based reinforcement learning algorithm to mitigate these limitations. While the underlying policy of the approach can be efficiently implemented in the form of a standard MPC controller, data-efficient learning is achieved through posterior sampling techniques. We provide a rigorous performance analysis of the resulting `Bayesian MPC' algorithm by establishing Lipschitz continuity of the corresponding future reward function and bound the expected number of unsafe learning episodes using an exact penalty soft-constrained MPC formulation. The efficiency and scalability of the method are illustrated using a 100-dimensional server cooling example and a nonlinear 10-dimensional drone example by comparing the performance against nominal posterior MPC, which is commonly used for data-driven control of constrained dynamical systems

The D.B. Weldon Library\u27s Instruction Portfolio: A Grassroots, Team-Based Approach

In an effort to address ever-shifting staffing levels and evolving service demands, staff in the Research & Instructional Services department of The D.B. Weldon Library at Western University developed and implemented a new and strategic approach to structuring their work. The ‘Portfolio Model’ provides a framework for organizing the primary functions of the department - collections, instruction and reference - while at the same time preserving liaison at its core. Through a close examination of this grassroots effort and in particular, the achievements realized and challenges faced by the team of librarians and library assistants who together comprise the ‘Instruction Portfolio’, this poster presentation provides insight into one academic library’s strategic approach to the development and delivery of instructional services at a time when resources are scarce and accountability more crucial than ever for all Ontario college and university libraries

Learning-based Moving Horizon Estimation through Differentiable Convex Optimization Layers

To control a dynamical system it is essential to obtain an accurate estimate of the current system state based on uncertain sensor measurements and existing system knowledge. An optimization-based moving horizon estimation (MHE) approach uses a dynamical model of the system, and further allows for integration of physical constraints on system states and uncertainties, to obtain a trajectory of state estimates. In this work, we address the problem of state estimation in the case of constrained linear systems with parametric uncertainty. The proposed approach makes use of differentiable convex optimization layers to formulate an MHE state estimator for systems with uncertain parameters. This formulation allows us to obtain the gradient of a squared and regularized output error, based on sensor measurements and state estimates, with respect to the current belief of the unknown system parameters. The parameters within the MHE problem can then be updated online using stochastic gradient descent (SGD) to improve the performance of the MHE. In a numerical example of estimating temperatures of a group of manufacturing machines, we show the performance of tuning the unknown system parameters and the benefits of integrating physical state constraints in the MHE formulation.Comment: This paper was accepted for presentation at the 4th Annual Conference on Learning for Dynamics and Control. The extended version here contains an additional appendix with more details on the numerical exampl