Search results

Filters

  • Journals
  • Authors
  • Keywords
  • Date
  • Type

Search results

Number of results: 3
items per page: 25 50 75
Sort by:
Download PDF Download RIS Download Bibtex

Abstract

Dynamics and control of discrete chaotic systems of fractional-order have received considerable attention over the last few years. So far, nonlinear control laws have been mainly used for stabilizing at zero the chaotic dynamics of fractional maps. This article provides a further contribution to such research field by presenting simple linear control laws for stabilizing three fractional chaotic maps in regard to their dynamics. Specifically, a one-dimensional linear control law and a scalar control law are proposed for stabilizing at the origin the chaotic dynamics of the Zeraoulia-Sprott rational map and the Ikeda map, respectively. Additionally, a two-dimensional linear control law is developed to stabilize the chaotic fractional flow map. All the results have been achieved by exploiting new theorems based on the Lyapunov method as well as on the properties of the Caputo h-difference operator. The relevant simulation findings are implemented to confirm the validity of the established linear control scheme.
Go to article

Bibliography

[1] C. Goodrich and A.C. Peterson: Discrete Fractional Calculus. Springer: Berlin, Germany, 2015, ISBN 978-3-319-79809-7.
[2] P. Ostalczyk: Discrete Fractional Calculus: Applications in Control and Image Processing. World Scientific, 2016.
[3] K. Oprzedkiewicz and K. Dziedzic: Fractional discrete-continuous model of heat transfer process. Archives of Control Sciences, 31(2), (2021), 287– 306, DOI: 10.24425/acs.2021.137419.
[4] T. Kaczorek and A. Ruszewski: Global stability of discrete-time nonlinear systems with descriptor standard and fractional positive linear parts and scalar feedbacks. Archives of Control Sciences, 30(4), (2020), 667–681, DOI: 10.24425/acs.2020.135846.
[5] J.B. Diaz and T.J. Olser: Differences of fractional order. Mathematics of Computation, 28 (1974), 185–202, DOI: 10.1090/S0025-5718-1974-0346352-5.
[6] F.M. Atici and P.W. Eloe: A transform method in discrete fractional calculus. International Journal of Difference Equations, 2 (2007), 165–176.
[7] F.M. Atici and P.W. Eloe: Discrete fractional calculus with the nabla operator. Electronic Journal of Qualitative Theory of Differential Equations, Spec. Ed. I, 3 , (2009), 1–12.
[8] G. Anastassiou: Principles of delta fractional calculus on time scales and inequalities. Mathematical and Computer Modelling, 52(3-4), (2010), 556– 566, DOI: 10.1016/j.mcm.2010.03.055.
[9] T. Abdeljawad: On Riemann and Caputo fractional differences. Computers and Mathematics with Applications, 62(3), (2011), 1602–1611, DOI: 10.1016/j.camwa.2011.03.036.
[10] M. Edelman, E.E.N. Macau, and M.A.F. Sanjun (Eds.): Chaotic, Fractional, and Complex Dynamics: New Insights and Perspectives. Springer International Publishing, 2018.
[11] G.C. Wu, D. Baleanu, and S.D. Zeng: Discrete chaos in fractional sine and standard maps. Physics Letters A, 378(5-6), (2014), 484–487, DOI: 10.1016/j.physleta.2013.12.010.
[12] G.C. Wu and D. Baleanu: Discrete fractional logistic map and its chaos. Nonlinear Dynamics, 75(1-2), (2014), 283–287, DOI: 10.1007/s11071-013-1065-7.
[13] T. Hu: Discrete chaos in fractional Henon map. Applied Mathematics, 5(15), (2014), 2243–2248, DOI: 10.4236/am.2014.515218.
[14] G.C. Wu and D. Baleanu: Discrete chaos in fractional delayed logistic maps. Nonlinear Dynamics, 80 (2015), 1697–1703, DOI: 10.1007/s11071-014-1250-3.
[15] M.K. Shukla and B.B. Sharma: Investigation of chaos in fractional order generalized hyperchaotic Henon map. International Journal of Electronics and Communications, 78 (2017), 265–273, DOI: 10.1016/j.aeue.2017.05.009.
[16] A. Ouannas, A.A. Khennaoui, S. Bendoukha, and G. Grassi: On the dynamics and control of a fractional form of the discrete double scroll. International Journal of Bifurcation and Chaos, 29(6), (2019), DOI: 10.1142/S0218127419500780.
[17] L. Jouini, A. Ouannas, A.A. Khennaoui, X. Wang, G. Grassi, and V.T. Pham: The fractional form of a new three-dimensional generalized Henon map. Advances in Difference Equations, 122 (2019), DOI: 10.1186/s13662-019-2064-x.
[18] F. Hadjabi, A. Ouannas,N. Shawagfeh, A.A. Khennaoui, and G. Grassi: On two-dimensional fractional chaotic maps with symmetries. Symmetry, 12(5), (2020), DOI: 10.3390/sym12050756.
[19] D. Baleanu, G.C. Wu, Y.R. Bai, and F.L. Chen: Stability analysis of Caputo-like discrete fractional systems. Communications in Nonlinear Science and Numerical Simulation, 48 (2017), 520–530, DOI: 10.1016/j.cnsns.2017.01.002.
[20] A-A. Khennaoui, A. Ouannas, S. Bendoukha, G. Grassi, X. Wang, V-T. Pham, and F.E. Alsaadi: Chaos, control, and synchronization in some fractional-order difference equations. Advances in Difference Equations, 412 (2019), DOI: 10.1186/s13662-019-2343-6.
[21] A. Ouannas, A.A. Khennaoui, G. Grassi, and S. Bendoukha: On chaos in the fractional-order Grassi-Miller map and its control. Journal of Computational and Applied Mathematics, 358(2019), 293–305, DOI: 10.1016/j.cam.2019.03.031.
[22] A. Ouannas, A.A. Khennaoui, S. Momani, G. Grassi and V.T. Pham: Chaos and control of a three-dimensional fractional order discrete-time system with no equilibrium and its synchronization. AIP Advances, 10 (2020), DOI: 10.1063/5.0004884.
[23] A. Ouannas, A-A. Khennaoui, S. Momani, G. Grassi, V-T. Pham, R. El- Khazali, and D. Vo Hoang: A quadratic fractional map without equilibria: Bifurcation, 0–1 test, complexity, entropy, and control. Electronics, 9 (2020), DOI: 10.3390/electronics9050748.
[24] A. Ouannas, A-A. Khennaoui, S. Bendoukha, Z.Wang, and V-T. Pham: The dynamics and control of the fractional forms of some rational chaotic maps. Journal of Systems Science and Complexity, 33 (2020), 584–603, DOI: 10.1007/s11424-020-8326-6.
[25] A-A. Khennaoui, A. Ouannas, S. Bendoukha, G. Grassi, R.P. Lozi, and V-T. Pham: On fractional-order discrete-time systems: Chaos, stabilization and synchronization. Chaos, Solitons and Fractals, 119(C), (2019), 150– 162, DOI: 10.1016/j.chaos.2018.12.019.
[26] A. Ouannas, A-A. Khennaoui, Z. Odibat, V-T. Pham, and G. Grassi: On the dynamics, control and synchronization of fractional-order Ikeda map. Chaos, Solitons and Fractals, 123(C), (2015), 108–115, DOI: 10.1016/j.chaos.2019.04.002.
[27] A. Ouannas, F. Mesdoui, S. Momani, I. Batiha, and G. Grassi: Synchronization of FitzHugh-Nagumo reaction-diffusion systems via onedimensional linear control law. Archives of Control Sciences, 31(2), 2021, 333–345, DOI: 10.24425/acs.2021.137421.
[28] Y. Li, C. Sun, H. Ling, A. Lu, and Y. Liu: Oligopolies price game in fractional order system. Chaos, Solitons and Fractals, 132(C), (2020), DOI: 10.1016/j.chaos.2019.109583.
[29] D. Mozyrska and E. Girejko: Overview of fractional h-difference operators. In Advances in harmonic analysis and operator theory. Birkhäuser, Basel, 2013, 253–268.
Go to article

Authors and Affiliations

A. Othman Almatroud
1
Adel Ouannas
2
Giuseppe Grassi
3
Iqbal M. Batiha
4
Ahlem Gasri
5
M. Mossa Al-Sawalha
1

  1. Department of Mathematics, Faculty of Science, University of Ha'il, Ha'il 81451, Saudi Arabia
  2. Department of Mathematics and Computer Science, University of Larbi Ben M’hidi, Oum El Bouaghi 04000, Algeria
  3. Dipartimento Ingegneria Innovazione, Universita del Salento, 73100 Lecce, Italy
  4. Department of Mathematics, Faculty of Science and Information Technology, Irbid National University, Irbid, Jordan and Nonlinear Dynamics Research Center (NDRC), Ajman University, Ajman, UAE
  5. Department of Mathematics, University of Larbi Tebessi, Tebessa 12002, Algeria
Download PDF Download RIS Download Bibtex

Abstract

In this paper, a creative dung beetle optimization (CDBO) algorithm is proposed and applied to the offline parameter identification of permanent magnet synchronous motors. First, in order to uniformly initialize the population state and increase the population diversity, a strategy to improve the initialization of the dung beetle population using Singer chaotic mapping is proposed to improve the global search performance; second, in order to improve the local search performance and enhance the convergence accuracy of the algorithm, a new dung beetle position update strategy is designed to increase the spatial search range of the algorithm. Simulation results show that the proposed optimization algorithm can quickly and accurately identify parameters such as resistance, inductance, and magnetic chain of the PMSM, with significant improvements in convergence algebra, identification accuracy and stability.
Go to article

Authors and Affiliations

Xiaoliang Yang
1 2
ORCID: ORCID
Yuyue Cui
1 2
Lianhua Jia
3
Zhihong Sun
3
Peng Zhang
3
ORCID: ORCID
Jiane Zhao
4
Rui Wang
1 2
ORCID: ORCID

  1. School of Electrical and Information Engineer, Zhengzhou University of Light Industry, Zhengzhou, China
  2. Henan Key Lab of Information based Electrical Appliances, Zhengzhou, China
  3. China Railway Engineering Equipment Group Co. Ltd, Zhengzhou, China
  4. School of Electrical and Electronic Engineering, Zhengzhou University of Science and Technology, Zhengzhou, China
Download PDF Download RIS Download Bibtex

Abstract

In order to optimise the operation state of the distribution network in the presence of distributed generation (DG), to reduce network loss, balance load and improve power quality in the distribution system, a multi-objective fruit fly optimisation algorithm based on population Manhattan distance (pmdMOFOA) is presented. Firstly, the global and local exploration abilities of a fruit fly optimisation algorithm (FOA) are balanced by combining population Manhattan distance ( PMD) and the dynamic step adjustment strategy to solve the problems of its weak local exploration ability and proneness to premature convergence. At the same time, Chebyshev chaotic mapping is introduced during position update of the fruit fly population to improve ability of fruit flies to escape the local optimum and avoid premature convergence. In addition, the external archive selection strategy is introduced to select the best individual in history to save in external archives according to the dominant relationship amongst individuals. The leader selection strategy, external archive update and maintenance strategy are proposed to generate a Pareto optimal solution set iteratively. Lastly, an optimal reconstruction scheme is determined by the fuzzy decision method. Compared with the standard FOA, the average convergence algebra of a pmdMOFOA is reduced by 44.58%. The distribution performance of non-dominated solutions of a pmdMOFOA, MOFOA, NSGA-III and MOPSO on the Pareto front is tested, and the results show that the pmdMOFOA has better diversity. Through the simulation and analysis of a typical IEEE 33-bus system with DG, load balance and voltage offset after reconfiguration are increased by 23.77% and 40.58%, respectively, and network loss is reduced by 57.22%, which verifies the effectiveness and efficiency of the proposed method.
Go to article

Bibliography

[1] Merlin A., Back H., Search for a minimal-loss operating spanning tree configuration in an urban power distribution system, Fifth Power Systems Computer Conference (PSCC), Cambridge, Britain, pp. 1–18 (1975).
[2] Kashem M.A., Ganapathy V., Jasmon G.B., Network reconfiguration for load balancing in distribution networks, IEE Proceedings-Generation Transmission and Distribution, vol. 146, no. 6, pp. 563–567 (1999).
[3] Liu Y.K., Li J., Wu L., Coordinated Optimal Network Reconfiguration and Voltage Regulator/DER Control for Unbalanced Distribution Systems, IEEE Transactions on Smart Grid, vol. 10, no. 3, pp. 2912–2922 (2019).
[4] Ahmadi S., Vahidinasab V., Ghazizadeh M. et al., Co-optimising distribution network adequacy and security by simultaneous utilisation of network reconfiguration and distributed energy resources, IET Generation Transmission & Distribution, vol. 13, no. 20, pp. 4747–4755 (2019).
[5] Liu H.Q., Qu J.M., Shanshan Yang S.S. et al., Intelligent optimal dispatching of active distribution network using modified flower pollination algorithm, Archives of Electrical Engineering, vol. 69, no. 1, pp. 159–174 (2020).
[6] Rahman Y.A., Manjang S., Yusran et al., Distributed generation’s integration planning involving growth load models by means of genetic algorithm, Archives of Electrical Engineering, vol. 67, no. 3, pp. 667–682 (2018).
[7] Olamaei J., Niknam T., Gharehpetian G., Application of particle swarm optimisation for distribution feeder reconfiguration considering distributed generators, Applied Mathematics and Computation, vol. 201, no. 1, pp. 575–586 (2008).
[8] Tang H.L., Wu J., Multi-objective coordination optimisation method for DGs and EVs in distribution networks, Archives of Electrical Engineering, vol. 68, no. 1, pp. 15–32 (2019).
[9] Rao R.S., Ravindra K., Satish K. et al, Power loss minimization in distribution system using network reconfiguration in the presence of distributed generation, IEEE Transactions on Power Systems, vol. 28, no. 1, pp. 317–325 (2013).
[10] Ling F.H., Zhang J.H., Sun X.B. et al., The application based on improved FOA optimisation algorithm in distribution network reconfiguration, Electric Switchgear, vol. 57, no. 1, pp. 91–95 (2019).
[11] Singh D., Misra R.K., Multi-objective feeder reconfiguration in different tariff structures, IET Generation Transmission & Distribution, vol. 4, no. 8, pp. 974–988 (2010).
[12] Sun K.M., Chen Q., Zhao P., Genetic algorithm with mesh check for distribution network topology reconfiguration, Automation of Electric Power Systems, vol. 42, no. 11, pp. 64–71 (2018).
[13] Ganesh S., Kanimozhi R., Meta-heuristic technique for network reconfiguration in distribution system with photovoltaic and D-STATCOM, IET Generation, Transmission & Distribution, vol. 12, no. 20, pp. 4524–4535 (2018).
[14] Chen D.Y., Zhang X.X., Distribution network reconfiguration of distributed generation based on AMOPSO algorithm, Acta Energiae Solaris Sinica, vol. 38, no. 8, pp. 2195–2203 (2017).
[15] Li Z.K., Lu Q., Fu Y. et al., State split multi-objective dynamic programming algorithm for dynamic reconfiguration of active distribution network, Proceedings of the CSEE, vol. 39, no. 17, pp. 5025-5036 (2019).
[16] Ding Y., Wang F., Bin F. et al., Multi-objective distribution network reconfiguration based on game theory, Electric power automation equipment, vol. 39, no. 2, pp. 28–35 (2019).
[17] Li H.J., Zhang P.W., Guo H.D., Adaptive multi-objective particle swarm optimisation algorithm based on population Manhattan distance, Computer Integrated Manufacturing Systems, vol. 26, no. 4, pp. 1019–1032 (2020).
[18] Liao J.Q., Wang H., Wang X.P., Fruit fly optimisation algorithm with chaotic dynamical step factor, Transducer and Microsystem Technologies, vol. 38, no. 8, pp. 139–142 (2019).
[19] Marko M., Najdan V., Milica P. et al., Chaotic fruit fly optimisation algorithm, Knowledge-based systems, vol. 89, no. 11, pp. 446–458 (2015).
[20] Saffar A., Hooshmand R., Khodabakhshian A., A new fuzzy optimal reconfiguration of distribution systems for loss reduction and load balancing using ant colony search-based algorithm, Applied Soft Computing, vol. 11, no. 5, pp. 4021–4028 (2011).
[21] ZhaoY.L., Lu J.X.,Yan Q. et al., Research on 3D-U intelligent manufacturing cell facilities layout based on self-adapting multi-objective fruit fly optimisation algorithm, Computer Integrated Manufacturing Systems, vol. 8, no. 1, pp. 1–21 (2020).
[22] Deb K., Pratap A., Agarwal S. et al., A fast and elitist multi-objective genetic algorithm: NSGA-II, IEEE Transactions on Evolutionary Computation, vol. 6, no. 2, pp. 182–197 (2002).
[23] Coello C.A.C., Pulido G.T., Lechuga M.S., Handling multiple objective with particle swarm optimisation, IEEE Transactions Evolutionary Computation, vol. 8, no. 3, pp. 256–279 (2004).
[24] Zitzler E., Deb K., Thiele L., Comparison of multi-objective evolutionary algorithms: empirical study, Evolutionary Computation, vol. 8, no. 8, pp. 173–195 (2000).
[25] Baran M.E., Wu F.F., Network reconfiguration in distribution systems for loss reduction and load balancing, IEEE Transactions on Power Delivery, vol. 4, no. 2, pp. 1401–1407 (1989).
Go to article

Authors and Affiliations

Minan Tang
1
Kaiyue Zhang
1
Qianqian Wang
2
Haipeng Cheng
3
Shangmei Yang
1
ORCID: ORCID
Hanxiao Du
1

  1. School of Automation and Electrical Engineering, Lanzhou Jiaotong University, Lanzhou, China
  2. College of Electrical and Information Engineering, Lanzhou University of Technology, Lanzhou, China
  3. CRRC Qingdao Sifang Co., Ltd. Qingdao, China

This page uses 'cookies'. Learn more