Design of multi-parametric NCO-tracking controllers for linear continuous-time systems

Process optimization for industrial applications aims to achieve performance enhancements while satisfying system constraints. A major challenge for any such method lies in the problem of uncertainty stemming from model mismatch and process disturbances. Classical approaches such as model predictive control usually handle the uncertainty by repeatedly solving the optimization problem on-line, which may prove a rather computationally demanding task nonetheless and cause serious delays for fast dynamic systems. Existing approaches for mitigating the on-line computational burden via off-line optimization include multi-parametric programming and NCO-tracking. Multi-parametric programming aims to generate a mapping of control strategies as a function of given parameters; whereas NCO-tracking involves tracking the necessary conditions of optimality (NCOs) based on a precomputed control switching structure, which enables a dynamic real-time optimization problem to be transferred into an on-line tracking problem using a feedback controller. A methodology, called multi-parametric (mp-)NCO-tracking is developed in this thesis, whereby multi-parametric dynamic optimization and NCO-tracking methods are combined into a unified framework. An algorithm for the design of mp-NCO-tracking controllers for continuous-time, linear-quadratic optimal control problems is presented in Chapter 2. The off-line step defines the multi-parametric control structure mapped to given uncertain (measurable) parameters in terms of so-called critical regions and feedback laws. Specifically, each critical region corresponds to a unique control switching structure in terms of the sequence of active constraints. The on-line step involves determining the current critical region once the parameter value has been revealed, and then applying the corresponding feedback control laws in a receding horizon manner. The mp-NCO-tracking approach provides a means for relaxing the invariant switching structure assumption in NCO-tracking by constructing critical regions for various switching structures. Moreover, addressing the problem directly in continuous-time can potentially reduce the number of critical regions compared with standard multi-parametric programming based on a time discretization and a control vector parameterization. The methodology and its benefits are illustrated for a number of simple case studies. To obtain the mathematical representation of the generally nonlinear critical regions, Chapter 3 investigates a machine learning model as a classifier, based on deep neural network. This feed-forward network is selected for its representational power as a universal approximator for arbitrary continuous functions. Here, the classifier takes the unknown parameter as input and maps the corresponding critical regions in terms of their switching structures. An algorithm for training the classifier is presented, which involves generating the training data set, setting up a neural network architecture, and applying optimization based training. By using a Softmax classifier in the output layer of the network, a normalized probability distribution is obtained, which consist of a vector with as many elements as the total number of critical regions, and each element representing the likelihood for a region to be the correct one. The classifier is conveniently embedded into the multi-parametric NCO-tracking controller for choosing the real-time switching structure in on-line control. Lastly, a robustification of the mp-NCO-tracking methodology is developed in Chapter 4, where constraints are guaranteed to be satisfied under all possible uncertainty scenarios, which leads to a min-max formulation. A robust counterpart formulation of the multi-parametric dynamic optimization problem is presented, which considers both additive or multiplicative time-varying disturbances. The approach involves backing-off the path and terminal constraints of the linear-quadratic optimal control problem based on a worst-case uncertainty propagation computed using either interval or ellipsoidal reachability tubes. The uncertain system state is decomposed into a nominal reference and a perturbed component, and a convex enclosure of the reachable set for the perturbed component is precomputed via some auxiliary differential equations. Conservative constraint back-offs are obtained from the precomputed reachability tubes, which enables the controller design procedure in the nominal case to be directly applied for the robust control problem, and to retain the same computational effort as in the nominal case. These developments are demonstrated by numerical case studies, and ways of extending this approach to more general, nonlinear optimal control problems are discussed in Chapter 5.Open Acces

Vitamin B2 enhances development of puberty ovaries via regulation of essential elements and plasma endocrine hormones

Purpose: To investigate the effect of vitamin B2 (VB2) on ovarian development during puberty.Methods: Four groups of domestic hens (Jinghong-1 strain, 12 hens/group) were housed under standard conditions and fed basal diet with or without graded doses of VB2 (10 – 40 mg/kg). At 10 weeks old, 9 hens were sacrificed from each group. Plasma levels of AST, ALT, steroid hormones and growth hormones were determined. In addition, some essential mineral elements in the ovarian tissue of the hens were assayed.Results: Treatment with VB2 significantly improved ovary and liver organ indices (p < 0.01), but had no deleterious effect on the liver. The different doses of VB2 exerted regulatory effects on homeostasis of essential elements in the ovary (p < 0.01). Moreover, VB2 treatment elevated plasma levels of progesterone (PR) and estrogen (ES), suggesting that it might regulate steroid hormone levels.Conclusion: These results indicate that VB2 enhances the development of the ovaries during puberty.Keywords: Domestic hen, Ovarian development, Vitamin B2, Steroid hormones, Mineral element

Research on Optimal Selection Measurement of DNS Root Instance

DNS root servers are located at the top of the domain name system and are the cornerstone of the Internet. Currently, root servers deploy numerous root instances using anycast technology. Introducing root instances can improve the parsing performance of root servers and the user access experience. However, we found that some root instances do not show optimal performance, and users cannot access the closest root instance when accessing the root instance or even cross-border access. This paper deploys three types of operator detection points in 31 provincial-level administrative regions in mainland China. Each detection point requests the NS record of the top-level domain name from the root instance server introduced in China to obtain the access data of the root instance server hit by domestic users. At the same time, we propose two methods to determine whether users have achieved the optimal choice of root instance, including the method based on the shortest AS path and the method based on geographical distance. In these two methods, we analyze the optimal selection of root instances for each root server. Finally, we analyze the cross-border access of users and find that China Telecom users are more likely to access the root instance across borders

Research on Fault Diagnosis Optimization of Intelligent Acquisition Terminal

Intelligent acquisition terminal is an important medium for power users to collect electric energy data and load data. It realizes centralized collection of electricity information through local and remote communication technology. With the continuous development of communication technology, the acquisition terminal is becoming more and more intelligent and its functions are becoming more and more complex. The content of field equipment fault diagnosis analysis technology is getting higher and higher. This paper combines with the current intelligent acquisition terminal fault diagnosis mode to optimize the research, from the technical and management aspects, makes an analysis of the causes of acquisition anomalies and the lack of means, and puts forward reasonable efficiency improvement suggestions

Cation-Induced Strategy toward an Hourglass-Shaped Cu₆I₇^– Cluster and Its Color-Tunable Luminescence

We have designed and synthesized a series of two-dimensional materials featuring with a (3,6)-connected <b>kgd</b> layer, in which an unprecedented anionic Cu₆I₇^– cluster was first trapped through a cation-induced synthetic strategy. The emission colors of these cluster-based luminophores gradually shift from blue to yellow as the monovalent cations (Li⁺, Na⁺, NH₄⁺, K⁺, TEA⁺) located between the neighboring layers changed. SCXRD analyses discover that the variation of the emission may be attributed to the transformation of the hourglass-shaped Cu₆I₇^– cluster. The bright, tunable, and broad luminescent emissions make them promising candidates as phosphors for light-emitting diodes (LEDs). Particularly, compound <b>1-TEA</b> emitting intensive yellow light with high luminescence quantum efficiency (QY = 79.9%) shows extremely high thermal, pH, organic solvent, and mechanical photostabilities. By employing it as a yellow phosphor, we fabricate a series of white lighting materials with high color rendering index (CRI)