A cascade MPC control structure for PMSM with speed ripple minimization

This paper addresses the problem of reducing the impact of periodic disturbances arising from the current sensor offset error on the speed control of a PMSM. The new results are based on a cascade model predictive control scheme with embedded disturbance model, where the per unit model is utilized to improve the numerical condition of the scheme. Results from an experimental application are given to support the design

Switched Linear Model Predictive Controllers for Periodic Exogenous Signals

This paper develops linear switched controllers for periodic exogenous signals using the framework of a continuous-time model predictive control. In this framework, the control signal is generated by an algorithm that uses receding horizon control principle with an on-line optimization scheme that permits inclusion of operational constraints. Unlike traditional repetitive controllers, applying this method in the form of switched linear controllers ensures rumpless transfer from one controller to another. Simulation studies are included to demonstrate the efficacy of the design with or without hard constraints

Indirect approach to continuous time system identification of food extruder

A three-stage approach to system identification in the continuous time is presented which is appropriate for day-to-day application by plant engineers in the process industry. The three stages are: data acquisition using relay feedback; non-parametric identification of the system step response; and parametric model fitting of the identified step response. The method is evaluated on a pilot-scale food-cooking extruder

Intermittent predictive control of an inverted pendulum

Intermittent predictive pole-placement control is successfully applied to the constrained-state control of a prestabilised experimental inverted pendulum

Data compression for estimation of the physical parameters of stable and unstable linear systems

A two-stage method for the identification of physical system parameters from experimental data is presented. The first stage compresses the data as an empirical model which encapsulates the data content at frequencies of interest. The second stage then uses data extracted from the empirical model of the first stage within a nonlinear estimation scheme to estimate the unknown physical parameters. Furthermore, the paper proposes use of exponential data weighting in the identification of partially unknown, unstable systems so that they can be treated in the same framework as stable systems. Experimental data are used to demonstrate the efficacy of the proposed approach

"Brilliant AI Doctor" in Rural China: Tensions and Challenges in AI-Powered CDSS Deployment

Artificial intelligence (AI) technology has been increasingly used in the implementation of advanced Clinical Decision Support Systems (CDSS). Research demonstrated the potential usefulness of AI-powered CDSS (AI-CDSS) in clinical decision making scenarios. However, post-adoption user perception and experience remain understudied, especially in developing countries. Through observations and interviews with 22 clinicians from 6 rural clinics in China, this paper reports the various tensions between the design of an AI-CDSS system ("Brilliant Doctor") and the rural clinical context, such as the misalignment with local context and workflow, the technical limitations and usability barriers, as well as issues related to transparency and trustworthiness of AI-CDSS. Despite these tensions, all participants expressed positive attitudes toward the future of AI-CDSS, especially acting as "a doctor's AI assistant" to realize a Human-AI Collaboration future in clinical settings. Finally we draw on our findings to discuss implications for designing AI-CDSS interventions for rural clinical contexts in developing countries

Predictive Repetitive Control Based on Frequency Decomposition

Abstract-This paper develops a predictive repetitive control algorithm based on frequency decomposition. In particular,, the periodic reference signal is first represented using a frequency sampling filter model and then the coefficients of the model are analyzed to determine its dominant frequency components. Using the internal model control principle, the dominant frequency components are embedded in model used to obtain the predictive repetitive control algorithm such that the periodic reference is followed with zero steadystate error. The design framework here is based on predictive control using Laguerre functions and hence plant operational constraints are naturally incorporated in the design and its implementation. keyword Periodic set-point signal, periodic disturbance, predictive control, constrained control, optimization

Human-centered design and evaluation of AI-empowered clinical decision support systems: a systematic review

IntroductionArtificial intelligence (AI) technologies are increasingly applied to empower clinical decision support systems (CDSS), providing patient-specific recommendations to improve clinical work. Equally important to technical advancement is human, social, and contextual factors that impact the successful implementation and user adoption of AI-empowered CDSS (AI-CDSS). With the growing interest in human-centered design and evaluation of such tools, it is critical to synthesize the knowledge and experiences reported in prior work and shed light on future work.MethodsFollowing the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, we conducted a systematic review to gain an in-depth understanding of how AI-empowered CDSS was used, designed, and evaluated, and how clinician users perceived such systems. We performed literature search in five databases for articles published between the years 2011 and 2022. A total of 19874 articles were retrieved and screened, with 20 articles included for in-depth analysis.ResultsThe reviewed studies assessed different aspects of AI-CDSS, including effectiveness (e.g., improved patient evaluation and work efficiency), user needs (e.g., informational and technological needs), user experience (e.g., satisfaction, trust, usability, workload, and understandability), and other dimensions (e.g., the impact of AI-CDSS on workflow and patient-provider relationship). Despite the promising nature of AI-CDSS, our findings highlighted six major challenges of implementing such systems, including technical limitation, workflow misalignment, attitudinal barriers, informational barriers, usability issues, and environmental barriers. These sociotechnical challenges prevent the effective use of AI-based CDSS interventions in clinical settings.DiscussionOur study highlights the paucity of studies examining the user needs, perceptions, and experiences of AI-CDSS. Based on the findings, we discuss design implications and future research directions

Power-constrained intermittent control

In this article, input power, as opposed to the usual input amplitude, constraints are introduced in the context of intermittent control. They are shown to result in a combination of quadratic optimisation and quadratic constraints. The main motivation for considering input power constraints is its similarity with semi-active control. Such methods are commonly used to provide damping in mechanical systems and structures. It is shown that semi-active control can be re-expressed and generalised as control with power constraints and can thus be implemented as power-constrained intermittent control. The method is illustrated using simulations of resonant mechanical systems and the constrained nature of the power flow is represented using power-phase-plane plots. We believe the approach we present will be useful for the control design of both semi-active and low-power vibration suppression systems