Search

This research paper introduces an adaptive terminal synergetic nonlinear control. This control aims at synchronizing two hyperchaotic Zhou systems. Thus, the adaptive terminal synergetic control’s synthesis is applied to synchronize a hyperchaotic i.e., slave system with unknown parameters with another hyperchaotic i.e., master system. Accordingly, simulation results of each system in different initial conditions reveal significant convergence. Moreover, the findings proved stability and robustness of the suggested scheme using Lyapunov stability theory.

Blood glucose automatic regulation achievement depends on the robustness of the used control algorithm. However, some constraints were encountered due to the human glucose-insulin regulatory system’s complexity. It is proposed to tackle such a goal through the development of a robust synergetic control algorithm. An adaptive approach is integrated into this synergetic control scheme to handle disturbances and parameters variations. Multiple meal disturbances often occur daily as well as some other stochastic noises making efficient glucose regulation a tough challenge addressed in this paper via a new synergetic scheme. Simulation results show a robust function during multiple meal disturbances with good noise rejection.

Control of dc/dc buck converter is a complex task due to the nonlinearity inherent in the converter and introduced by the external changes. A robust synergetic controller for the control of dc/dc buck converter is described in this study. An adaptive terminal synergetic control is developed based on robust synergetic control theory and terminal attractor techniques. The method estimates the boundary of parameter uncertainty and external disturbance by adaptive law. The idea behind this strategy is to use the terminal synergetic control (TSYC) approach to assure finite time convergence of the output voltage error to the equilibrium point, and integrate an adaptive law to handle uncertainties is provided resulting in enhancing robustness as well as a better transient performance compared to the conventional control. In addition, modified PSO algorithms will be used to optimise controller's parameters using an ITAE criterion. The results show and demonstrate the effectiveness and feasibility of the proposed approach.

In this paper, an optimal synergetic controller based on a bat algorithm for a DC-DC boost converter is presented. DC-DC boost converters are some of the most widely used power electronics devices for their high conversion efficiency and versatility. Bat optimisation based on the echolocation behaviour of bats, a bio-inspired algorithm, is successfully used for synergetic parameters optimization. The proposed control scheme is evaluated in a simulation and implementation study of a boost converter indicating satisfactory overall performance with stability insured through Lyapunov synthesis. Experimental results show that the proposed control provides good regulation.

DC-DC converters are widely used as reliable power source for many industrial and military applications, computers and electronic devices. Several control methods were developed for DC-DC converters control mostly with asymptotic convergence. Synergetic control (SC) is a proven robust control approach and will be used here in a so called terminal scheme to achieve finite time convergence. Lyapounov synthesis is adopted to assure controlled system stability. Furthermore particle swarm optimization (PSO) algorithm, based on an integral time absolute of error (ITAE) criterion will be used to optimize controller parameters. Simulation of terminal synergetic control of a DC-DC converter is carried out for different operating conditions and results are compared to classic synergetic control performance, that which demonstrate the effectiveness and feasibility of the proposed control method., {"references":["L. Fan, and Y. Yu , \"Adaptive non-singular terminal sliding mode control for DC-DC converter\", Advances in Electrical and Computer Engineering, Vol. 11, Number 2, 2011.","V.I. Utkin. , Sliding modes in control and optimization, New York, Springer, 1992.","J.E. Slotine., and W.P. Li, Applied nonlinear control, Prentice-Hall, Englewood Cliffs, NJ, 1991.","H.F. Ho, Wong, Y.K., and A.B. Rad, \"Adaptive fuzzy sliding mode control with chattering elimination for nonlinear SISO systems\", Simulation Modelling Practice and Theory'', 2009,(17), pp. 1199–1210","Zhenhua.J, Roger.A. Dougal. Synergetic Control of Power Converters for Pulse Current Charging of Advanced Batteries from a Fuel Cell Power Source. IEEE Transactions on powr electronics,Vol. 19. No. 4. July 2004.","E. Santi. E, Monti. A, Donghong. L, Karthik. P, Roger. A. Dougal. Synergetic Control for DC-DC Boost Converter: Implementation Options. IEEE Transactions on industry applications. Vol. 39. No. 6. November 2003.","Mezghani N., Damak T., Adaptive terminal sliding mode control for rigid robotic manipulators, International Journal of Automation and Computing 8(2), May 2011, 215-220 DOI: 10.1007/s11633-011-0576-2.","H. Komurcugil, \"Adaptive terminal sliding-mode control strategy for DC-DC buck converters\", ISA Transactions 51 (2012) 673-681.","J. Kennedy and R.C. Eberhart, \"Particle swarm optimization\", Proc. Of IEEE International Conference on Neural Networks, Piscataway, NJ. Pp 1942-1948,1995.\n[10]\tB. Liu, K.Wang,and Q. Zou, \"Study on the application of particle swarm optimization algorithm to power regulation of CPS in interconnects power grids\". IEEE Electrical Power & Energy Conference. 2008. \n[11]\tR. Suresh Kumar,and J. Suganthi , Improving the boost Converter PID Controller performance using Particle swarm optimization, European Journal of Scientific Research, ISSN 1450-216X Vol. 85 No 3 September, 2012, pp.327 – 335. \n[12]\tS. Vishnu Teja, T. N. Shanavas, and S. K. Patnaik, Modified PSO Based Sliding Mode Controller Parameters for Buck Converter, 2012 IEEE, Students Conference on Electrical, Electronics and Computer Science."]}

Power system stability is enhanced through a novel stabilizer developed around an adaptive fuzzy sliding mode approach which applies the Nussbaum gain to a nonlinear model of a single-machine infinite-bus (SMIB) and multi-machine power system stabilizer subjected to a three phase fault. The Nussbaum gain is used to avoid the positive sign constraint and the problem of controllability of the system. A comparative simulation study is presented to evaluate the achieved performance.