Similarity quantification for linear stochastic systems as a set-theoretic control problem

For the formal verification and design of control systems, abstractions with quantified accuracy are crucial. Such similarity quantification is hindered by the challenging computation of approximate stochastic simulation relations. This is especially the case when considering accurate deviation bounds between a stochastic continuous-state model and its finite-state abstraction. In this work, we give a comprehensive computational approach and analysis for linear stochastic systems. More precisely, we develop a computational method that characterizes the set of possible simulation relations and optimally trades off the error contributions on the system's output with deviations in the transition probability. To this end, we establish an optimal coupling between the models and simultaneously solve the approximate simulation relation problem as a set-theoretic control problem using the concept of invariant sets. We show the variation of the guaranteed satisfaction probability as a function of the error trade-off in a case study where a formal specification is given as a temporal logic formula.Comment: 16 pages, 9 figures, submitted to Automatic

Combining High Yields and Blast Resistance in Rice (Oryza spp.): A Screening under Upland and Lowland Conditions in Benin

The future security of the supply of rice for food in Africa depends on improving the level of local production to achieve self-sufficiency. In order to cope with the existing gap between production and actual demand, combining a high level of rice blast tolerance and a high-yield potential is necessary. The current study was conducted under upland and lowland conditions in Benin to gain insight into the performance of selected blast-resistant accessions along with some currently grown varieties. This study revealed a high phenotypic variability among these accessions. Furthermore, differences in the performance of these accessions under lowland and upland conditions were observed. Principal component analysis showed their grouping in three clusters. The analysis also demonstrated a high yield potential among the blast-resistant rice accessions whether they were Oryza sativa or O. glaberrima. Furthermore, there was a significant correlation between yield and both spikelet fertility and growth cycle duration. In conclusion, the present study identified promising rice accessions for future breeding. High phenotypic variability in combination with interesting traits can help to develop new resilient varieties. Finally, when the traits correlate with yield, they can be used as markers for an early screening method for identifying promising accessions at an early stage

A Linear Parameter-Varying Approach to Data Predictive Control

By means of the linear parameter-varying (LPV) Fundamental Lemma, we derive novel data-driven predictive control (DPC) methods for LPV systems. In particular, we present output-feedback and state-feedback-based LPV-DPC methods with terminal ingredients, which guarantee exponential stability and recursive feasibility. We provide methods for the data-based computation of these terminal ingredients. Furthermore, an in-depth analysis of the properties and implementation aspects of the LPV-DPC schemes is given, including alternative recursive formulations, application for nonlinear systems and handling noise-disturbed data. We demonstrate the performance of the proposed methods on a simulation example involving a nonlinear unbalanced disc system.Comment: Submitted to IEEE-TAC. Extended version. 17 page

Data-Driven Predictive Control for Linear Parameter-Varying Systems

Based on the extension of the behavioral theory and the Fundamental Lemma for Linear Parameter-Varying (LPV) systems, this paper introduces a Data-driven Predictive Control (DPC) scheme capable to ensure reference tracking and satisfaction of Input-Output (IO) constraints for an unknown system under the conditions that (i) the system can be represented in an LPV form and (ii) an informative data-set containing measured IO and scheduling trajectories of the system is available. It is shown that if the data set satisfies a persistence of excitation condition, then a data-driven LPV predictor of future trajectories of the system can be constructed from the IO data set and online measured data. The approach represents the first step towards a DPC solution for nonlinear and time-varying systems due to the potential of the LPV framework to represent them. Two illustrative examples, including reference tracking control of a nonlinear system, are provided to demonstrate that the data-based LPV-DPC scheme, achieves similar performance as LPV model-based predictive control.Comment: Accepted to 4th IFAC Workshop on Linear Parameter-Varying System

Fundamental Lemma for Data-Driven Analysis of Linear Parameter-Varying Systems

Based on the Fundamental Lemma by Willems et al., the entire behaviour of a Linear Time-Invariant (LTI) system can be characterised by a single data sequence of the system as long the input is persistently exciting. This is an essential result for data-driven analysis and control. In this work, we aim to generalise this LTI result to Linear Parameter-Varying (LPV) systems. Based on the behavioural framework for LPV systems, we prove that one can obtain a result similar to Willems'. Based on an LPV representation, i.e., embedding, of nonlinear systems, this allows the application of the Fundamental Lemma for systems beyond the linear class.Comment: Accepted to the 60th Conference on Decision and Control 2021 (CDC2021

Temporal Logic Control of POMDPs via Label-based Stochastic Simulation Relations

The synthesis of controllers guaranteeing linear temporal logic specifications on partially observable Markov decision processes (POMDP) via their belief models causes computational issues due to the continuous spaces. In this work, we construct a finite-state abstraction on which a control policy is synthesized and refined back to the original belief model. We introduce a new notion of label-based approximate stochastic simulation to quantify the deviation between belief models. We develop a robust synthesis methodology that yields a lower bound on the satisfaction probability, by compensating for deviations a priori, and that utilizes a less conservative control refinement

Deoxynivalenol content in wheat dust versus wheat grain: a comparative study

The present study, set up in the growing season 2011-2012, was designed to obtain quantitative data on the occurrence of deoxynivalenol in wheat grain and the corresponding wheat dust. The field experiment consisted of a complete randomised block design with five wheat varieties sown on a field on which maize was grown in the previous season. The impact of the tillage method and the influence of the wheat variety resistance on the deoxynivalenol content of wheat and wheat dust were investigated. The accumulation of deoxynivalenol in wheat dust was confirmed and a sigmoidal relationship between the deoxynivalenol content in wheat dust versus wheat grain was determined. Deoxynivalenol reduction was obtained by ploughing and by sowing moderately resistant wheat varieties. As wheat dust provides equal results and solves the problem of heterogeneity during sampling of conventional wheat matrix, the sampling of wheat dust can be considered as a promising alternative

Straw yield and quality: An extra motivation for the introduction of triticale in mixed farming systems**

Straw is a valuable by-product from cereal production. It is used for agricultural purposes as feed and bedding material for livestock. Additionally, cereal straw is a resource for the production of sustainable biomaterials and bio-energy. To meet the demands of these sectors substantial amounts of straw, with specific properties (e.g. water-holding capacity), are necessary. Since wheat breeding has mainly focused on grain yield rather than on straw yield other cereal species, such as triticale, can be of interest. Therefore, in this research the straw yield and water-holding capacity of four winter wheat and four winter triticale varieties were studied during two growing seasons. For both wheat and triticale there were differences in dry matter yield and percentage dry matter between growing seasons. Furthermore, depending on the growing season, there were significant differences in straw yield between the different wheat and triticale varieties. However, during both growing seasons, the straw yield obtained from the triticale varieties was significantly higher compared to the straw yield obtained from the wheat varieties. Concerning the water-holding capacity, it was concluded that the water absorption potential of triticale straw was higher compared to the water absorption potential of wheat straw. However, only in 2014 a significant difference between wheat and triticale was noted. So, it can be concluded that, besides the known advantages of triticale (performance on marginal soils, disease resistance, low fertilizer input, etc.), this crop has the potential to deliver high yields of high quality straw