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首页> 外文期刊>International Journal of Innovative Computing Information and Control >ADAPTIVE BACKSTEPPING AND PID OPTIMIZED BY GENETIC ALGORITHM IN CONTROL OF CHAOTIC
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ADAPTIVE BACKSTEPPING AND PID OPTIMIZED BY GENETIC ALGORITHM IN CONTROL OF CHAOTIC

机译：遗传算法在混沌控制中的自适应反步和PID优化

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摘要

In this paper, the robust adaptive control scheme based on adaptive back-stepping design is used to control the autonomous second order strict feedback form such as the Lorenz Chaotic system. The design procedure is recursive; at the ith step, the ith subsystem is stabilized with respect to a Lyapunov function V_i by the design of a stabilizing function α_i, tuning function τ_i, and the update law θ_i for the unknowns' adaptive parameters estimates θ_i, the feedback control u is designed at the final step. This procedure possesses strong properties of global stability and tracking which are built into the nonlinear system in a number of steps, which is never higher than the system order, without any growth restrictions on nonlinearities. The results show the guarantee of all the parameter estimates to converge to their true values. Some simulation results for the chaotic system cited above are shown to illustrate the parameter convergence with adaptive backstepping design.
机译：本文采用基于自适应反步设计的鲁棒自适应控制方案来控制自主的二阶严格反馈形式，如Lorenz混沌系统。设计过程是递归的；在第i步，通过设计稳定函数α_i，调谐函数τ_i以及未知数自适应参数估计值θ_i的更新定律θ_i，使第i个子系统相对于Lyapunov函数V_i稳定，设计了反馈控制u在最后一步。该程序具有强大的全局稳定性和跟踪特性，这些特性可以通过许多步骤内置到非线性系统中，并且永远不会高于系统阶数，并且对非线性没有任何增长限制。结果表明，所有参数估计都可以收敛到其真实值。上面提到的混沌系统的一些仿真结果显示了说明自适应反步设计的参数收敛。

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