Learning-based Robust Model Predictive Control for Sector-bounded Lur'e Systems

For dynamical systems with uncertainty, robust controllers can be designed by assuming that the uncertainty is bounded. The less we know about the uncertainty in the system, the more conservative the bound must be, which in turn may lead to reduced control performance. If measurements of the uncertain term are available, this data may be used to reduce the uncertainty in order to make bounds as tight as possible. In this paper, we consider a linear system with a sector-bounded uncertainty. We develop a model predictive control algorithm to control the system, and use a weighted Bayesian linear regression model to learn the least conservative sector condition using measurements collected in closed-loop. The resulting robust model predictive control algorithm therefore reduces the conservativeness of the controller, and provides probabilistic guarantees of asymptotic stability and constraint satisfaction. The efficacy of the proposed method is shown in simulation.publishedVersio

A Levenberg-Marquardt Algorithm for Sparse Identification of Dynamical Systems

Low complexity of a system model is essential for its use in real-time applications. However, sparse identification methods commonly have stringent requirements that exclude them from being applied in an industrial setting. In this article, we introduce a flexible method for the sparse identification of dynamical systems described by ordinary differential equations. Our method relieves many of the requirements imposed by other methods that relate to the structure of the model and the dataset, such as fixed sampling rates, full state measurements, and linearity of the model. The Levenberg-Marquardt algorithm is used to solve the identification problem. We show that the Levenberg-Marquardt algorithm can be written in a form that enables parallel computing, which greatly diminishes the time required to solve the identification problem. An efficient backward elimination strategy is presented to construct a lean system model.publishedVersio

Design, Synthesis and Characterization of a Highly Effective Inhibitor for Analog-Sensitive (as) Kinases

Highly selective, cell-permeable and fast-acting inhibitors of individual kinases are sought-after as tools for studying the cellular function of kinases in real time. A combination of small molecule synthesis and protein mutagenesis, identified a highly potent inhibitor (1-Isopropyl-3-(phenylethynyl)-1H-pyrazolo[3,4-d]pyrimidin-4-amine) of a rationally engineered Hog1 serine/threonine kinase (Hog1T100G). This inhibitor has been successfully used to study various aspects of Hog1 signaling, including a transient cell cycle arrest and gene expression changes mediated by Hog1 in response to stress. This study also underscores that the general applicability of this approach depends, in part, on the selectivity of the designed the inhibitor with respect to activity versus the engineered and wild type kinases. To explore this specificity in detail, we used a validated chemogenetic assay to assess the effect of this inhibitor on all gene products in yeast in parallel. The results from this screen emphasize the need for caution and for case-by-case assessment when using the Analog-Sensitive Kinase Allele technology to assess the physiological roles of kinases

Synthesis of 3-(1,2,3-triazol-1-yl)- and 3-(1,2,3-triazol-4-yl)-substituted pyrazolo[3,4-d]pyrimidin-4-amines via click chemistry: potential inhibitors of the Plasmodium falciparum PfPK7 protein kinase

Efficient routes to 3-(1,2,3-triazol-1-yl)- and 3-(1,2,3-triazol-4-yl)pyrazolo[3,4-d]pyrimidin-4-amines using a one-pot two-step reaction are presented. The two routes give easy access to two different isomers of 1,4-disubstituted triazoles and the target compounds are obtained from a variety of readily available aromatic and aliphatic halides without isolation of potentially unstable organic azide intermediates. Two compounds show activity towards the PfPK7 kinase (IC50 10–20 µM) of P. falciparum, the organism responsible for the most virulent form of malaria, and can be regarded as hits useful for further development into lead compounds

Learning-based Robust Model Predictive Control for Sector-bounded Lur'e Systems

For dynamical systems with uncertainty, robust controllers can be designed by assuming that the uncertainty is bounded. The less we know about the uncertainty in the system, the more conservative the bound must be, which in turn may lead to reduced control performance. If measurements of the uncertain term are available, this data may be used to reduce the uncertainty in order to make bounds as tight as possible. In this paper, we consider a linear system with a sector-bounded uncertainty. We develop a model predictive control algorithm to control the system, and use a weighted Bayesian linear regression model to learn the least conservative sector condition using measurements collected in closed-loop. The resulting robust model predictive control algorithm therefore reduces the conservativeness of the controller, and provides probabilistic guarantees of asymptotic stability and constraint satisfaction. The efficacy of the proposed method is shown in simulation

Learning-based Robust Model Predictive Control for Sector-bounded Lur'e Systems

For dynamical systems with uncertainty, robust controllers can be designed by assuming that the uncertainty is bounded. The less we know about the uncertainty in the system, the more conservative the bound must be, which in turn may lead to reduced control performance. If measurements of the uncertain term are available, this data may be used to reduce the uncertainty in order to make bounds as tight as possible. In this paper, we consider a linear system with a sector-bounded uncertainty. We develop a model predictive control algorithm to control the system, and use a weighted Bayesian linear regression model to learn the least conservative sector condition using measurements collected in closed-loop. The resulting robust model predictive control algorithm therefore reduces the conservativeness of the controller, and provides probabilistic guarantees of asymptotic stability and constraint satisfaction. The efficacy of the proposed method is shown in simulation

Lighting up DNA with the environment-sensitive bright adenine analogue qAN4

The fluorescent adenine analogue qAN4 was recently shown to possess promising photophysical properties, including a high brightness as a monomer. Here we report the synthesis of the phosphoramidite of qAN4 and its successful incorporation into DNA oligonucleotides using standard solid-phase synthesis. Circular dichroism and thermal melting studies indicate that the qAN4-modification has a stabilizing effect on the B-form of DNA. Moreover, qAN4 base-pairs selectively with thymine with mismatch penalties similar to those of mismatches of adenine. The low energy absorption band of qAN4 inside DNA has its peak around 358 nm and the emission in duplex DNA is partly quenched and blue-shifted (ca. 410 nm), compared to the monomeric form. The spectral properties of the fluorophore also show sensitivity to pH; a property that may find biological applications. Quantum yields in single-stranded DNA range from 1-29 % and in duplex DNA from 1-7 %. In combination with the absorptive properties, this gives an average brightness inside duplex DNA of 275 M-1  cm-1 , more than five times higher than the most used environment-sensitive fluorescent base analogue, 2-aminopurine. Finally, we show that qAN4 can be used to advantage as a donor for interbase FRET applications in combination with adenine analogue qAnitro as an acceptor