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Designing the terminal ingredients of direct data-driven predictive control presents challenges due to its reliance on an implicit, non-minimal input-output data-driven representation. By considering the class of constrained LTI systems with unknown time delays, we propose a set-theoretic direct data-driven predictive controller that does not require a terminal cost to provide closed-loop guarantees. In particular, first, starting from input/output data series, we propose a sample-based method to build N-step input output backward reachable sets. Then, we leverage the constructed family of backward reachable sets to derive a data-driven control law. The proposed method guarantees finite-time convergence and recursive feasibility, independent of objective function tuning. It requires neither explicit state estimation nor an explicit prediction model, relying solely on input-output measurements; therefore, unmodeled dynamics can be avoided. Finally, a numerical example highlights the effectiveness of the proposed method in stabilizing the system, whereas direct data-driven predictive control without terminal ingredients fails under the same conditions.

Shaping thermoplastic sheets into three-dimensional products is challenging since overheating results in failed manufactured parts and wasted material. To this end, we propose an indirect data-driven predictive control approach using Model Predictive Control (MPC) capable of handling temperature constraints and heating-power saturation while delivering enhanced precision, overshoot control, and settling times compared to state-of-the-art methods. We employ a Non-linear Auto-Regressive with Exogenous inputs (NARX) model to define a linear control-oriented model at each operating point. Using a high-fidelity simulator, several simulation studies have been conducted to evaluate the proposed method's robustness and performance under parametric uncertainty, indicating overshoot and average steady-state error less than $2^\circ \mathrm{C}$ and $0.7^\circ \mathrm{C}$ ($7^\circ \mathrm{C}$ and $2^\circ \mathrm{C}$) for the nominal (worst-case) scenario. Finally, we applied the proposed method to a lab-scale thermoforming platform, resulting in a close response to the simulation analysis with overshoot and average steady-state error metrics less than $5.3^\circ \mathrm{C}$ and $1^\circ \mathrm{C}$, respectively.

In response to safety concerns associated with learning-based algorithms, safety filters have been proposed as a modular technique. Generally, these filters heavily rely on the system's model, which is contradictory if they are intended to enhance a data-driven or end-to-end learning solution. This paper extends our previous work, a purely Data-Driven Safety Filter (DDSF) based on Willems' lemma, to an extremely short-sighted and non-conservative solution. Specifically, we propose online and offline sample-based methods to expand the safe set of DDSF and reduce its conservatism. Since this method is defined in an input-output framework, it can systematically handle both unknown and time-delay LTI systems using only one single batch of data. To evaluate its performance, we apply the proposed method to a time-delay system under various settings. The simulation results validate the effectiveness of the set expansion algorithm in generating a notably large input-output safe set, resulting in safety filters that are not conservative, even with an extremely short prediction horizon.

Nowadays, many control systems are networked and embed communication and computation capabilities. Such control architectures are prone to cyber attacks on the cyberinfrastructure. Consequently, there is an impellent need to develop solutions to preserve the plant's safety against potential attacks. To ensure safety, this paper introduces a modular safety filter approach that is effective for a variety of cyber-attack types. This solution can be implemented in combination with existing control and detection algorithms, effectively separating safety from performance. The safety filter does not require information on the reliability of the received command or the feature of the used anomaly detector. It can be implemented in conjunction with high-performance, resilient controllers, to achieve both high performance during normal operation and safety during an attack. As an illustrative example, we have shown the effectiveness of the proposed design considering a multi-agent formation task involving 20 mobile robots. The simulation results testify that the safety filter operates effectively during false data injection and intelligent attacks.

Implementation of learning-based control remains challenging due to the absence of safety guarantees. Safe control methods have turned to model-based safety filters to address these challenges, but this is paradoxical when the ultimate goal is a model-free, data-driven control solution. Addressing the core question of "Can we ensure the safety of any learning-based algorithm without explicit prediction models and state estimation?" this paper proposes a Data-Driven Safety Filter (DDSF) grounded in Behavioral System Theory (BST). The proposed method needs only a single system trajectory available in an offline dataset to modify unsafe learning inputs to safe inputs. This contribution addresses safe control in the input-output framework and therefore does not require full state measurements or explicit state estimation. Since no explicit model is required, the proposed safe control solution is not affected by unmodeled dynamics and unstructured uncertainty and can provide a safe solution for systems with unknown time delays. The effectiveness of the proposed DDSF is illustrated in simulation for a high-order six-degree-of-freedom aerial robot and a time-delay adaptive cruise control system.

This work provides a formalized model-invariant safety system for closed-loop anesthesia that uses feedback from measured data for model falsification to reduce conservatism. The safety system maintains predicted propofol plasma concentrations, as well as the patient's blood pressure, within safety bounds despite uncertainty in patient responses to propofol. Model-invariant formal verification is used to formalize the safety system. This technique requires a multi-model description of model-uncertainty. Model-invariant verification considers all possible dynamics of an uncertain system, and the resulting safety system may be conservative for systems that do not exhibit the worst-case dynamical response. In this work, we employ model falsification to reduce conservatism of the model-invariant safety system. Members of a model set that characterizes model- uncertainty are falsified if discrepancy between predictions of those models and measured responses of the uncertain system is established, thereby reducing model uncertainty. We show that including falsification in a model-invariant safety system reduces conservatism of the safety system., Comment: 11 pages, 9 figures, submitted to IEEE TCST

Genetic variation in the tomato clade was explored by sequencing a selection of 84 tomato accessions and related wild species representative for the Lycopersicon, Arcanum, Eriopersicon, and Neolycopersicon groups. We present a reconstruction of three new reference genomes in support of our comparative genome analyses. Sequence diversity in commercial breeding lines appears extremely low, indicating the dramatic genetic erosion of crop tomatoes. This is reflected by the SNP count in wild species which can exceed 10 million i.e. 20 fold higher than in crop accessions. Comparative sequence alignment reveals group, species, and accession specific polymorphisms, which explain characteristic fruit traits and growth habits in tomato accessions. Using gene models from the annotated Heinz reference genome, we observe a bias in dN/dS ratio in fruit and growth diversification genes compared to a random set of genes, which probably is the result of a positive selection. We detected highly divergent segments in wild S. lycopersicum species, and footprints of introgressions in crop accessions originating from a common donor accession. Phylogenetic relationships of fruit diversification and growth specific genes from crop accessions show incomplete resolution and are dependent on the introgression donor. In contrast, whole genome SNP information has sufficient power to resolve the phylogenetic placement of each accession in the four main groups in the Lycopersicon clade using Maximum Likelihood analyses. Phylogenetic relationships appear correlated with habitat and mating type and point to the occurrence of geographical races within these groups and thus are of practical importance for introgressive hybridization breeding. Our study illustrates the need for multiple reference genomes in support of tomato comparative genomics and Solanum genome evolution studies., Comment: 4 Figure, 10 Supplementary Figures, 2 Supplementary Figures This is the pre-peer reviewed version of the following article: The Plant Journal 80.1 (2014): 136-148, which has been published in final form at http://doi.org/10.1111/tpj.12616

Recent Herschel/SPIRE maps of the Small and Large Magellanic Clouds (SMC, LMC) exhibit in each thousands of clouds. Observed at 250 microns, they must be cold, T ~ 15 K, hence the name "Herschel cold clouds" (HCCs). From the observed rotational velocity profile and the assumption of spherical symmetry, the Galactic mass density is modeled in a form close to that of an isothermal sphere. If the HCCs constitute a certain fraction of it, their angular size distribution has a specified shape. A fit to the data deduced from the SMC/LMC maps supports this and yields for their radius 2.5 pc, with a small change when allowing for a spread in HCC radii. There are so many HCCs that they will make up all the missing Halo mass density if there is spherical symmetry and their average mass is of order 15,000 Mo. This compares well with the Jeans mass of circa 40,000 Mo and puts forward that the HCCs are in fact Jeans clusters, constituting all the Galactic dark matter and much of its missing baryons, a conclusion deduced before from a different field of the sky (Nieuwenhuizen, Schild and Gibson 2011). A preliminary analysis of the intensities yields that the Jeans clusters themselves may consist of some billion MACHOs of a few dozen Earth masses. With a size of dozens of solar radii, they would mostly obscure stars in the LMC, SMC and towards the Galactic center, and may thus have been overlooked in microlensing., Comment: Revised and corrected version, matches published version. Conclusions unchanged

This article provides resources for school-community collaboration to support at-risk students and their families in rural settings. Rural schools and their communities must work together efficiently and economically to counteract problems such as geographic isolation, shortage of resources, economic decline, lack of political power and difficulties associated with living in a modern society. The article describes five regional and national resource organizations that support school-community collaboration. It provides information on activities, services, contacts, and phone numbers. The article also outlines state efforts in California and Utah and describes eight collaborative programs within the Far West Laboratory Region. The Far West Laboratory serves Arizona, California, Nevada, and Utah in linking educators to share information, expertise, and innovative practices. It provides technical assistance to build local capacity for continued self-improvement. The program profiles illustrate that school-community collaboration should address both the needs of the children and the needs of the community. The last section contains an annotated bibliography of nine publications related to school-community collaboration. (LP)

The issues of excellence and equity in American education are examined in this paper, which also outlines the effective schools movement behind the competing goals of excellence and equity. The traditional approaches to educational change and the reasons why these approaches have been ineffective are summarized. This paper shows how the need to find a more effective approach to educational improvement has led the two factions--excellence and equity--to realize that in order to raise achievement for all students improvement at the school level, or whole-student improvement, must be addressed. Several innovative whole-school improvement efforts are described, which are classified into three categories: (1) networking programs, such as the National Network for Educational Renewal and Re: Learning; (2) demonstration programs, such as Accelerated Schools, Success for All, and the School Development Program; and (3) regional state department of education programs in Arizona, California, Nevada, and Utah. This paper serves as a starting point for thinking about educational change and the ways in which schools can be improved to more effectively serve the at-risk population. (Contains 29 references.) (Author/LMI)