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Advanced SOC Estimation of Lead-Acid Battery for HEM Application
Published in P. C. Thomas, Vishal John Mathai, Geevarghese Titus, Emerging Technologies for Sustainability, 2020
Saira Philip, Liss Abraham Maret, Heinz Varghese Maymana, Herma Mariam Jacob, Shameer Asharaf, Rani Chacko
The book keeping estimation methods include Coulomb counting method and modified Coulomb counting method. Coulomb counting technique is the most commonly used technique in which the current entering and leaving the battery is measured and is integrated over time. In order to improve the accuracy of Coulomb counting, a modified Coulomb counting is used in which a corrected current is taken for measurement [7]. Coulomb counting technique has limitations in determining the initial SOC of battery. Adaptive systems method includes Back Propagation (BP) neural network, Radial Basis Function (RBF) neural network, Support vector machine, Fuzzy neural network, and Kalman filter. These methods are advanced intelligent estimation methods in which they are self-designed and can automatically change the SOC value for different charging and discharging conditions [8]. In the BP neural network, the SOC value is predicted using the recent history of voltage, current and ambient temperature of battery [9]. The RBF neural network is a useful SOC estimation methodology for systems with incomplete information.
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Published in Reinhard Oppermann, Adaptive User Support, 2017
The goal of adaptive systems is to increase the suitability of the system for specific tasks; facilitate handling the system for specific users, and so enhance user productivity; optimize workloads, and increase user satisfaction. The ambition of adaptivity is that not only that “everyone should be computer literate”, but also that “computers should be user literate” (Browne, Totterdell, & Norman 1990). There are examples of adaptive systems that support the user in the learning and training phase by introducing the user into the system operation. Others draw the user’s attention to tools he is not familiar with to perform specific operations for routine tasks more rapidly. Evaluation of system use is designed to reduce system complexity for the user. In the event of errors or disorientation on the part of the user, or where the user requires help, the adaptive system is to provide task-related and user-related explanations. Automatic error correction is to be employed when user errors can be uniquely identified. The user is spared the necessity of correcting obvious errors.
Motivation, Definitions, and Classification
Published in V. V. Chalam, Adaptive Control Systems, 2017
Depending on how these functions are brought about, we have different types of adaptive controllers. To outline the essential aspects of adaptive control, the following “definition” [74] may be considered: An adaptive system measures a certain index of performance (IP) using the inputs, the states, and the outputs of the adjustable system. From the comparison of the measured IP values and a set of given ones, the adaptation mechanism modifies the parameters of the adjustable system or generates an auxiliary input in order to maintain the IP values close to the set of given ones.
Performance enhancement of unfalsified adaptive control strategy using fuzzy logic
Published in International Journal of Systems Science, 2019
S. I. Habibi, A. Khaki-Sedigh, M. N. Manzar
Model-free and data-driven control strategies aim to achieve closed-loop control objectives with minimum plant model dependence (Parastvand & Khosrowjerdi, 2015; Zhai, Zhang, & Liu, 2014). Adaptive systems are systems that monitor their behaviour against unpredictable changes and tune (switch) parameters (controller) for better performance (Li, Zhang, Yan, & Xie, 2019; Li, Zhao, He, & Lu, 2019; Zhou, Zhao, Li, Lu, & Wu, 2018). Adaptive fuzzy control is a branch of control which employs fuzzy logic alongside adaptive concept (Wu, Liu, Jing, Li, & Wu, 2017). Switching control is a class of hybrid control systems which is applicable to uncertain systems (Li, Dimirovski, Fu, & Wang, 2019). Unfalsified adaptive switching supervisory control (UASSC) is an adaptive, data-driven and model-free control methodology which uses a performance analysis to achieve closed-loop control objectives. UASSC guarantees closed-loop stability using input-output plant data only. The key approach of this method is the use of fictitious reference signals to evaluate the outside loop controllers' performance at each time instance. Principles of the UASSC are discussed thoroughly in Safonov (2018). Multi-model UASSC (MMUASSC) employs the controller falsification philosophy of UASSC. However, unlike the UASSC, it benefits from both the controller and the model banks.
Two-stage multi-innovation stochastic gradient algorithm for multivariate output-error ARMA systems based on the auxiliary model
Published in International Journal of Systems Science, 2019
Qinyao Liu, Feng Ding, Quanmin Zhu, Tasawar Hayat
Recursive algorithms, which can be used for on-line identification, can compute the parameter estimates recursively in time (Ding, Chen, Lin, & Jiang, 2019; Liu & Ding, 2019; Ma et al., 2019; Song, 2018). Most adaptive systems, e.g. adaptive control systems, are based on the recursive identification algorithms (Ding, Chen, Lin, & Wan, 2019; Gu, Chou, Liu, & Ji, 2019; Vau & Bourlès, 2018; Zhou, Wen, & Wang, 2018). The gradient estimation algorithms are one type of recursive algorithms, which modifies the parameter estimates along the negative gradient direction of the criterion function until the criterion function reaches its minimum value (Mendel, 1974; Pan, Jiang, Wan, & Ding, 2017). The stochastic gradient was introduced in Goodwin and Sin (1984). The stochastic gradient type identification algorithm is a kind of least mean square identification algorithm whose gain vector or gain matrix tends to zero with the data quantity increasing. This property makes the parameter estimates of the stochastic gradient algorithm converge to the true values. To date, many gradient methods have been developed to rich the identification field (Ding & Chen, 2007; Xu & Ding, 2017a, 2017b, 2018). For Hammerstein nonlinear systems, Cheng, Wei, Sheng, Chen, and Wang (2018) studied a fractional-order stochastic gradient algorithm using the multi-innovation theory. To deal with the Wiener systems with unknown integer time-delay, a multistage gradient algorithm was studied by dividing the problem into three parts (Atitallah, Bedoui, & Abderrahim, 2017).
A framework for managing organizations in complex environments
Published in Construction Management and Economics, 2018
Legge (1990) categorizes a system, depending on the external and internal conditions, into four states: stable system (in its ground state), oscillating system (working and active), exploding state (reactions or driving energy causes the system’s destruction) and chaos state (between oscillating and exploding). In the chaos state, the system is not stable because it is changing and it is not destroyed; it behaves in a complicated pattern that it is impossible to predict. Cilliers (2005) describes the complex system as an open system which consists of a large number of elements which interact dynamically by exchanging energy or information; has nonlinear interaction; includes direct and indirect feedback loops; operates far from equilibrium; its behaviour is unpredicted, and can re-organize its internal structure without intervention from external agents. Marion (1999, p. 28) defines the complex system as “one whose component parts interact with sufficient intricacy that they cannot be predicted by standard linear equations; so many variables are at work in the system that its overall behaviour can only be understood as an emergent consequence of the holistic sum of all the myriad behaviour embedded within”. Nan (2011, p. 508) defines agents as “individual actors or basic entities of actions in a CAS” where agents can represent a wide variety of entities such as human beings, organizations, objects or concepts. Plsek et al. (1997) define the CAS as a system of individual agents, who have the freedom to act in ways that are not always very predictable, and whose actions are interconnected such that one agent’s action changes the context for other agents. Examples of complex adaptive systems include the stock market, the human body immune system, a business organization, a department within an organization, or a family.