71 research outputs found
MPC based optimal input design for nonlinear system identification
No full textA combined nonlinear model predictive control with extended Kalman filter strategy has been proposed for optimal input design. As the designed controller depend on the identified parameters, the achievable performance highly depends on the quality of the identified information. The degradation in achieving the desired control performance is quantified b y introducing an optimality criteria which minimize the error covariance matrix of the identified parameters. The major contribution is using the information of the system parameter at every sample time to improve the control performance at next time step. The the performance of the proposed algorithm is verified by numerical simulations for a example system
Optimal Input Design for Active Parameter Identification of Dynamic Nonlinear Systems
No full textThere are many important aspects to be considered while designing optimal excitation signal for system identification experiment in control applications. Active parameter identification is an important issue in system and control theory. In this dissertation, the problem of optimal input design for active parameter identification of dynamic nonlinear system is addressed. Real life physical systems are identified by excitation with a suitable input signal and observing the resulting output behavior of the system. It is important to choose the input signal intelligently in the sense that it is responsible to determine the accuracy and nature of the unknown system characteristics. This leads to a spurred interest in designing such an optimal excitation signals that can yield maximal information from the identification experiment. The information obtained from parameter identification is usually not accurate due to incomplete knowledge of the system, disturbance as exogenous inputs and noisy measurements. Hence, the input spectrum is designed in such a way that it can improve the system performance and shape the quality of obtained information. A welldesigned input signal can maximize the amount of information and reduce the experimental cost and time. The input signal is usually given some a-priori characteristics (knowledge on the pdf) so that “excitation” of the system is guaranteed. In this thesis, a closed-loop method is investigated which is able to improve the parameter identification on the basis of the actual system’s behavior. The effectiveness of the proposed algorithm is presented by the experimental results which corresponds to the perfect identification of the unknown parameter vector. The major technical contribution of this work is to propose an optimal feedback input design method for active parameter identification of dynamic nonlinear systems. The proposed framework can design such optimal excitation signals, considering the information from the identified parameters, that can maximize the
amount of information from the identified parameters, guarantee to meet the specified control performance and minimize some cost function of the error covariance matrix of the identified parameters. The problem is formulated in a receding horizon framework where extended Kalman filter is used for system identification and the optimal input is designed in a nonlinear model predictive control framework. In order to carry out a comparison study, also Unscented Kalman Filter and Gaussian Sum Filter are used for the active parameter identification of dynamic nonlinear system. Towards this end, a suitable optimality criterion related to the unknown parameters is proposed and motivated as an information measure. The aim of the optimal input design is to yield maximal information from the unknown system by minimizing the cost related to the unknown parameters while maintaining some process performance and satisfying the possible constraints. Simulations are performed to show the effectiveness of the proposed algorithm
Robust integrated lateral guidance and control of UAVs
No full textIn this paper, a novel guidance scheme is presented for UAVs using the Integrated Guidance and Control (IGC) framework. The proposed guidance scheme is derived using H∞ Loop Shaping Design Procedure (LSDP). To recover from an initial cross track error, the proposed guidance algorithm produces such aileron commands that ensure the roll maneuvers without saturating the roll angle. The shaping of the open loop plant is carried out using the pre and post weights and then the robust stabilization is done by using the normalized left coprime factor uncertainty. The performance and robustness of the system are verified by introducing parametric uncertainties in to the system model. The results of the proposed scheme are verified by implementing it on a complete 6-DOF nonlinear model in the presence of wind disturbance. The simulation results indicate the effectiveness and robustness of the proposed guidance algorithm
Revealing the quasiparticle electronic and excitonic nature in cubic, tetragonal, and hexagonal phases of FAPbI3
No full textThe development of three-dimensional (3D) hybrid organic-inorganic perovskites has sparked much interest because of their rich light-harvesting capabilities in solar cells. However, the understanding of the electronic and optical properties, particularly the excitonic shifts upon structural phase transition with temperature in these materials, is not fully clear. Here, we report the accurate description of electronic and optical properties of mostly studied FAPbI(3) across the cubic-tetragonal-hexagonal phases, using the relativistic GW method and Bethe-Salpeter Equation (BSE), including the spin-orbit coupling effects. Our GW calculations reveal that the bandgap values vary from 1.47 to 3.54 eV from the room temperature cubic phase to the low temperature hexagonal phase. Our optical analysis shows that excitonic peaks are blue-shifted, and exciton binding energies estimated by the model BSE approach increase from 74 to 567 meV going from the cubic to hexagonal phases. Our results may have important impacts on the practical uptake of hybrid perovskite based solar cells under different climatic conditions. (c) 2022 Author(s). All article content, except where otherwise noted, is licensed under a Creative Commons Attribution (CC BY) license(http://creativecommons.org/licenses/by/4.0/)
Impact of Ownership Structure and Corporate Governance on Earning Management: Empirical Findings from Listed Firms on the Pakistan Stock Exchange
No full textPurpose- This study investigates the interplay among Ownership Structure, Corporate Governance, and Earnings Management by employing ordinary least square (OLS) regression. To find the relationship among the three constructs based on data sourced from listed companies on the Pakistan Stock Exchange spanning 2016-2021 were used, excluding the financial industry due to its unique reporting system.Design/Methodology- The sample comprises 111 firms chosen based on data availability. To measure earnings management, the researchers used a modified version of John\u27s model (1995) to estimate discretionary accruals. Findings- The study\u27s key findings include the significant role of institutional investors in reducing earnings management. The number of board directors and ownership concentration were observed to impact discretionary accruals. Control variables indicated that more profitable, growing, and highly leveraged firms tend to engage in earnings management, which decreases with the firm\u27s age. The study revealed a diverse relationship between ownership structure, corporate governance codes, and earnings management. Notably, significant institutional investment reduces ownership concentration, leading to decreased earnings management. Moreover, the results show a positive and significant correlation between firm size and Return on Assets (ROA). Practical Implications- Board independence was found to have a positive impact on earnings management, suggesting that boards serve a more complex role than mere monitoring to mitigate accounting manipulation
Leadership lessons arising from the COVID-19 pandemic
No full textThe chapter looks at leadership challenges during the COVID-19 pandemic in the business, politics, education, and healthcare sector. The author discusses the leadership oversights and mistakes that could have been avoided during the COVID-19 pandemic and the possible leadership lessons learned from the pandemic.</p
Recent Trends in Computational Fluid Dynamics
No full textThis eBook is a collection of articles from a Frontiers Research Topic. Frontiers Research Topics are very popular trademarks of the Frontiers Journals Series: they are collections of at least ten articles, all centered on a particular subject. With their unique mix of varied contributions from Original Research to Review Articles, Frontiers Research Topics unify the most influential researchers, the latest key findings and historical advances in a hot research area! Find out more on how to host your own Frontiers Research Topic or contribute to one as an author by contacting the Frontiers Editorial Office: frontiersin.org/about/contac
Recent Trends in Computational Fluid Dynamics
No full textThis eBook is a collection of articles from a Frontiers Research Topic. Frontiers Research Topics are very popular trademarks of the Frontiers Journals Series: they are collections of at least ten articles, all centered on a particular subject. With their unique mix of varied contributions from Original Research to Review Articles, Frontiers Research Topics unify the most influential researchers, the latest key findings and historical advances in a hot research area! Find out more on how to host your own Frontiers Research Topic or contribute to one as an author by contacting the Frontiers Editorial Office: frontiersin.org/about/contac
Collected Papers (on various scientific topics), Volume XII
No full textThis twelfth volume of Collected Papers includes 86 papers comprising 976 pages on Neutrosophics Theory and Applications, published between 2013-2021 in the international journal and book series “Neutrosophic Sets and Systems” by the author alone or in collaboration with the following 112 co-authors (alphabetically ordered) from 21 countries: Abdel Nasser H. Zaied, Muhammad Akram, Bobin Albert, S. A. Alblowi, S. Anitha, Guennoun Asmae, Assia Bakali, Ayman M. Manie, Abdul Sami Awan, Azeddine Elhassouny, Erick González-Caballero, D. Dafik, Mithun Datta, Arindam Dey, Mamouni Dhar, Christopher Dyer, Nur Ain Ebas, Mohamed Eisa, Ahmed K. Essa, Faruk Karaaslan, João Alcione Sganderla Figueiredo, Jorge Fernando Goyes García, N. Ramila Gandhi, Sudipta Gayen, Gustavo Alvarez Gómez, Sharon Dinarza Álvarez Gómez, Haitham A. El-Ghareeb, Hamiden Abd El-Wahed Khalifa, Masooma Raza Hashmi, Ibrahim M. Hezam, German Acurio Hidalgo, Le Hoang Son, R. Jahir Hussain, S. Satham Hussain, Ali Hussein Mahmood Al-Obaidi, Hays Hatem Imran, Nabeela Ishfaq, Saeid Jafari, R. Jansi, V. Jeyanthi, M. Jeyaraman, Sripati Jha, Jun Ye, W.B. Vasantha Kandasamy, Abdullah Kargın, J. Kavikumar, Kawther Fawzi Hamza Alhasan, Huda E. Khalid, Neha Andalleb Khalid, Mohsin Khalid, Madad Khan, D. Koley, Valeri Kroumov, Manoranjan Kumar Singh, Pavan Kumar, Prem Kumar Singh, Ranjan Kumar, Malayalan Lathamaheswari, A.N. Mangayarkkarasi, Carlos Rosero Martínez, Marvelio Alfaro Matos, Mai Mohamed, Nivetha Martin, Mohamed Abdel-Basset, Mohamed Talea, K. Mohana, Muhammad Irfan Ahamad, Rana Muhammad Zulqarnain, Muhammad Riaz, Muhammad Saeed, Muhammad Saqlain, Muhammad Shabir, Muhammad Zeeshan, Anjan Mukherjee, Mumtaz Ali, Deivanayagampillai Nagarajan, Iqra Nawaz, Munazza Naz, Roan Thi Ngan, Necati Olgun, Rodolfo González Ortega, P. Pandiammal, I. Pradeepa, R. Princy, Marcos David Oviedo Rodríguez, Jesús Estupiñán Ricardo, A. Rohini, Sabu Sebastian, Abhijit Saha, Mehmet Șahin, Said Broumi, Saima Anis, A.A. Salama, Ganeshsree Selvachandran, Seyed Ahmad Edalatpanah, Sajana Shaik, Soufiane Idbrahim, S. Sowndrarajan, Mohamed Talea, Ruipu Tan, Chalapathi Tekuri, Selçuk Topal, S. P. Tiwari, Vakkas Uluçay, Maikel Leyva Vázquez, Chinnadurai Veerappan, M. Venkatachalam, Luige Vlădăreanu, Ştefan Vlăduţescu, Young Bae Jun, Wadei F. Al-Omeri, Xiao Long Xin.
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