By Recai Kilic
Self sufficient and nonautonomous Chua's circuits are of specified value within the research of chaotic approach modeling, chaos-based technology and engineering functions. due to the fact that and software-based layout and implementation ways may be utilized to Chua's circuits, those circuits also are first-class educative versions for learning and experimenting nonlinear dynamics and chaos. This ebook not just offers a suite of the author's released papers on layout, simulation and implementation of Chua's circuits, it additionally presents a scientific method of working towards chaotic dynamics.
Read Online or Download A Practical Guide for Studying Chua's Circuits (Nonlinear Science, Series a) (World Scientific Series on Nonlinear Science: Series a) PDF
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Additional resources for A Practical Guide for Studying Chua's Circuits (Nonlinear Science, Series a) (World Scientific Series on Nonlinear Science: Series a)
14). The rest of the parameters are chosen as C1 = 10 nF, C2 = 100 nF, and R = 1700 Ω such that the circuit exhibits double-scroll attractor behavior. 2k Fig. 20 Hybrid-I realization of Chua’s circuit. The PSPICE simulation results for the Hybrid-I realization of Chua’s circuit are shown in Fig. 21. While the chaotic dynamics VC1, VC2 and iL are shown in Fig. 21(a), the double-scroll attractor is observed in Fig. 21(b). This realization uses both VOAs and CFOAs. Due to the use of CFOAs for the synthetic inductor, not only the state variables VC1 and VC2, but also the third state variable, iL, are accessible in a direct manner.
Subplot(3,1,3); % get figure sub window 3 ready. plot(t, x(:,3),'–'); ylabel('z'); xlabel('t'); % plot in the third sub window and label % y-axis as z. figure; % open a new figure window plot(x(:,1), x(:,2),'–'); ylabel('y'); xlabel('x'); axis('tight') % plot x(2) according % to x(1) and label x and y axes as x and y and set the % length scales of the two axes to be tight. 2 Simulation and Modeling of Chua’s Circuit in SIMULINK In addition to MATLAB’s code-based solution, there is a very effective tool used for modeling linear and nonlinear dynamic systems: MATLAB/SIMULINK.
1). 87; % alpha and beta are constants. 0157; % k1=-8/7; k2=4/90; % d1=-8/7; d2=4/63; % a, b, c, h1, h2, k1, k2, d1 and d2 are changeable nonlinear function parameters. dx = zeros (3,1); % dx is a 3x1 zero matrix. f=b*x(1)+(1/2)*(a-b)*(abs(x(1)+c)-abs(x(1)-c)); % The first nonlinear function. %f=h1*x(1)-(h2*x(1)*x(1)*x(1)); % The second nonlinear function. %f=k1*x(1)+(k2*x(1)*x(1)*x(1)); % The third nonlinear function. %f=d1*x(1)+(d2*x(1)*abs(x(1))); % The fourth nonlinear function. dx(1) = alpha*(x(2)-x(1)-f); % dx(1) represents dx .
A Practical Guide for Studying Chua's Circuits (Nonlinear Science, Series a) (World Scientific Series on Nonlinear Science: Series a) by Recai Kilic