Distributed model predictive control of linear systems with coupled constraints based on collective neurodynamic optimization

© Springer Nature Switzerland AG 2018. Distributed model predictive control explores an array of local predictive controllers that synthesize the control of subsystems independently yet they communicate to efficiently cooperate in achieving the closed-loop control performance. Distributed model predictive control problems naturally result in sequential distributed optimization problems that require real-time solution. This paper presents a collective neurodynamic approach to design and implement the distributed model predictive control of linear systems in the presence of globally coupled constraints. For each subsystem, a neurodynamic model minimizes its cost function using local information only. According to the communication topology of the network, neurodynamic models share information to their neighbours to reach consensus on the optimal control actions to be carried out. The collective neurodynamic models are proven to guarantee the global optimality of the model predictive control system

Choice-Disability and HIV Infection: A Cross Sectional Study of HIV Status in Botswana, Namibia and Swaziland

Interpersonal power gradients may prevent people implementing HIV prevention decisions. Among 7,464 youth aged 15–29 years in Botswana, Namibia and Swaziland we documented indicators of choice-disability (low education, educational disparity with partner, experience of sexual violence, experience of intimate partner violence (IPV), poverty, partner income disparity, willingness to have sex without a condom despite believing partner at risk of HIV), and risk behaviours like inconsistent use of condoms and multiple partners. In Botswana, Namibia and Swaziland, 22.9, 9.1, and 26.1% women, and 8.3, 2.8, and 9.3% men, were HIV positive. Among both women and men, experience of IPV, IPV interacted with age, and partner income disparity interacted with age were associated with HIV positivity in multivariate analysis. Additional factors were low education (for women) and poverty (for men). Choice disability may be an important driver of the AIDS epidemic. New strategies are needed that favour the choice-disabled

Distributed Model Predictive Control

Distributed Model Predictive Control refers to a class of predictive control architectures in which a number of local controllers manipulate a subset of inputs to control a subset of outputs (states) composing the overall system. Different levels of communication and (non)cooperation exist, although in general the most compelling properties can be established only for cooperative schemes, those in which all local controllers optimize local inputs to minimize the same plant-wide objective function. Starting from state-feedback algorithms for constrained linear systems, extensions are discussed to cover output feedback, reference target tracking and nonlinear systems. An outlook of future directions is finally presented

Distributed Robust Model Predictive Control of Interconnected Polytopic Systems

International audienceA suboptimal approach to distributed robust MPC for uncertain systems consisting of polytopic subsystems with coupled dynamics subject to both state and input constraints is proposed. The robustness is defined in terms of the optimization of a cost function accumulated over the uncertainty and satisfying state constraints for a finite subset of uncertainties. The approach reformulates the original centralized robust MPC problem into a quadratic programming problem, which is solved by distributed iterations of the dual accelerated gradient method. A stopping condition is used that allows the iterations to stop when the desired performance, stability, and feasibility can be guaranteed. This allows for the approach to be used in an embedded robust MPC implementation. The developed method is illustrated on a simulation example of an uncertain system consisting of two interconnected polytopic subsystems

MODS-Wayne, a Colorimetric Adaptation of the Microscopic-Observation Drug Susceptibility (MODS) Assay for Detection of Mycobacterium tuberculosis Pyrazinamide Resistance from Sputum Samples.

Although pyrazinamide (PZA) is a key component of first- and second-line tuberculosis treatment regimens, there is no gold standard to determine PZA resistance. Approximately 50% of multidrug-resistant tuberculosis (MDR-TB) and over 90% of extensively drug-resistant tuberculosis (XDR-TB) strains are also PZA resistant. pncA sequencing is the endorsed test to evaluate PZA susceptibility. However, molecular methods have limitations for their wide application. In this study, we standardized and evaluated a new method, MODS-Wayne, to determine PZA resistance. MODS-Wayne is based on the detection of pyrazinoic acid, the hydrolysis product of PZA, directly in the supernatant of sputum cultures by detecting a color change following the addition of 10% ferrous ammonium sulfate. Using a PZA concentration of 800 µg/ml, sensitivity and specificity were evaluated at three different periods of incubation (reading 1, reading 2, and reading 3) using a composite reference standard (MGIT-PZA, pncA sequencing, and the classic Wayne test). MODS-Wayne was able to detect PZA resistance, with a sensitivity and specificity of 92.7% and 99.3%, respectively, at reading 3. MODS-Wayne had an agreement of 93.8% and a kappa index of 0.79 compared to the classic Wayne test, an agreement of 95.3% and kappa index of 0.86 compared to MGIT-PZA, and an agreement of 96.9% and kappa index of 0.90 compared to pncA sequencing. In conclusion, MODS-Wayne is a simple, fast, accurate, and inexpensive approach to detect PZA resistance, making this an attractive assay especially for low-resource countries, where TB is a major public health problem