Through the use of the Fundamental Lemma for linear systems, a direct data-driven state-feedback control synthesis method is presented for a rather general class of nonlinear (NL) systems. The core idea is to develop ...
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This work considers the synthesis of state feedback controllers established as deep artificial feed-forward neural networks for the control of discrete-time nonlinear but input-affine systems. The idea is to design ou...
This work considers the synthesis of state feedback controllers established as deep artificial feed-forward neural networks for the control of discrete-time nonlinear but input-affine systems. The idea is to design output layers of particular structure to guarantee the satisfaction of state constraints in form of control-invariant ellipsoids. Since an analytical expression can be derived for the resulting neural network controller, the latter can be stored and evaluated efficiently. Moreover, the proposed output layer guarantees the satisfaction of the considered state constraints for each specification of the parameter vector. Numerical examples are provided for illustration and evaluation of the approach, in which the approximation of a nonlinear model predictive control law is considered as application.
To design linear control systems a new method is suggested on the basis of the principle of output and impacts control. The main difference of this principle consists in the use of only measurable variables and separa...
To design linear control systems a new method is suggested on the basis of the principle of output and impacts control. The main difference of this principle consists in the use of only measurable variables and separate operators for each input signal of the control device. The design problem has a solution if the plant is completely controllable, and at least the controllable variable is measured. The suggested approach makes it possible to eliminate the well-known contradiction between the accuracy and stability of systems with control on deviation, and to provide the required astatism orders or selective invariance to the external actions. At the same time, the conditions of ensuring the direct indicators of the transient quality and the physical implementation of the control device are taken into account. The coefficients of the equation of the output and impacts control device are determined by solving a system of linear algebraic equations. The control device can be implemented on the basis of either operational amplifiers or sufficiently high-speed digital automation tools. The suggested method can be applied for designing high-quality linear control systems for various purposes.
Industry 4.0 involves the integration of digital technologies, such as IoT, Big Data, and AI, into manufacturing and industrial processes to increase efficiency and productivity. As these technologies become more inte...
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This contribution describes the use of Natural Language Processing (NLP) methods for the lexical analysis of requirements for control, sensors, and information systems in the Agriculture 4.0 domain. The analysis is pr...
This contribution describes the use of Natural Language Processing (NLP) methods for the lexical analysis of requirements for control, sensors, and information systems in the Agriculture 4.0 domain. The analysis is presented on an orchard 4.0 *** proposed orchard includes a sensor network (containing mainly measurements of hydrometeorological and soil variables), camera monitoring of conditions, and yield, support for autonomous robotic care and harvesting based on machine vision, prediction of appropriate times for interventions, etc. Requirements specification for mentioned system is written in natural language.A sentence splitting, Tokenization, Lemmatization, and POS (Part-of-Speech) tagging methods are applied to the mentioned structured requirements of the system and Use Case description. From these and by means of NLP, the candidates of classes, attributes, operations, and associations of the UML (Unified Modeling Language) class diagram are filtered and the UML model is synthesized. This paper presents the application of software engineering methods to support the development of complex heterogeneous sensors, information, and control systems.
Koopman operator theory offers a rigorous treatment of dynamics and has been emerging as a powerful modeling and learning-based control method enabling significant advancements across various domains of robotics. Due ...
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The paper presents a promising method of increasing by more than an order of magnitude the maximum slew rate (SR) of integrated operational amplifiers (OpAmps) based on two-cycle “folded“ cascodes. This method is ba...
The paper presents a promising method of increasing by more than an order of magnitude the maximum slew rate (SR) of integrated operational amplifiers (OpAmps) based on two-cycle “folded“ cascodes. This method is based on the introduction of special differentiating transient correction circuits into the classical circuits of OpAmps. Developed on the basis of the MH2XA031 array chip, the high-speed OpAmp is recommended for practical use in the subclass of so-called discrete-analog SC-filters on switched capacitors, for which (in some important cases) higher SR values are required. The effect of significantly improving the OpAmp’s speed is to provide higher output current levels of the two-cycle “folded” cascode that recharges the integrating correction capacitor during the transient edge. The OpAmp’s speed performance in the large-signal mode is increased from $248.7 \mathrm{~V} / \mu \mathrm{s}$ to $4148 \mathrm{~V} / \mu \mathrm{s}$ as a result of this.
Cancer disparities are adverse differences in cancer measures that exist among certain population groups. Given that the role they play not only in the disease prognosis but also in therapy response, there is an urgen...
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ISBN:
(数字)9798350371499
ISBN:
(纸本)9798350371505
Cancer disparities are adverse differences in cancer measures that exist among certain population groups. Given that the role they play not only in the disease prognosis but also in therapy response, there is an urgent need to understand what causes them. Most studies investigate these disparities by analyzing transcriptomic data and in particular miRNAs for their regulatory role, but only focusing on expression levels. To face this challenge we propose MIRROR, a new method which analyzes a differential co-expression network of miRNAs between patients’ cohorts, to study the role they play at the target genes’ level. Doing so, we can study the altered molecular mechanism that are linked to cancer disparities. The application of MIRROR to two different cases of cancer disparities has demonstrated its efficacy in identifying molecular players involved in the considered disparity, presenting itself as a viable option to approach this challenge.
This paper proposes a scheme to model the energy consumption of LoRaWAN, which is a popular example of low-power wide-area networks (LPWANs), nodes via the results of outdoor field experiments by assuming regional sma...
This paper proposes a scheme to model the energy consumption of LoRaWAN, which is a popular example of low-power wide-area networks (LPWANs), nodes via the results of outdoor field experiments by assuming regional smart agriculture as a use case for internet of things (IoT). Specifically, we derive an experimental approximation formula to estimate the battery lifetime by introducing parameters such as spread factor and payload length. The validity of the proposed scheme is demonstrated by confirming that the results obtained by the approximate formula, the experimental and theoretical results generally agree, regardless of the node state and spread factor. Furthermore, we show that the obtained approximate formula can be used to identify the current consumption value in the sleep state that should be achieved to achieve the desired battery lifetime.
System identification (SysID) is the art and science of dealing with dynamic data modelling problems from systems science perspectives. It has been an active field and is still very active today, due to its wide range...
System identification (SysID) is the art and science of dealing with dynamic data modelling problems from systems science perspectives. It has been an active field and is still very active today, due to its wide range of applications, especially its basic principles of finding transparent, interpretable and parsimonious models for different purposes. The past decades have witnessed the explosive growth in machine learning (ML) and its applications in all areas of science and engineering. Meanwhile, there has been an increasing demand for the development of transparent, explainable and/or interpretable ML models. This paper proposes a new framework for developing System Identification-informed Transparent and Explainable MAchine Learning (SITEMAL) models. A case study, involving a real power consumption dataset, is presented to demonstrate the application of the proposed modelling framework and its performance for power consumption forecasting.
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