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基於PSO優化的一類碰撞振動系統混沌控製

Chaos Control for A Class of Vibro-Impact System Based on PSO Optimization

  • 摘要: 針對一類含間隙單自由度剛性碰撞振動系統的混沌運動控製問題🧖🏻‍♂️,提出一種基於PSO優化RBF神經網絡控製器的參數反饋混沌運動控製方法〰️。分析了混沌運動與激勵頻率變化之間的關聯關系及表現特征💃🏻,總結了分岔及混沌運動的參數分析判據,並據此設計了RBF神經網絡參數反饋混沌控製器;構建了以Poincaré截面上相鄰2點距離最小為目標的適應度函數,以引導PSO算法優化控製器的參數🗣;通過給系統可控參數施加一個小擾動👽,達到將混沌運動控製為穩定周期運動的目的。該方法可適用於模型未知或難以建立精確數學模型的混沌運動控製💆🏼。仿真結果驗證了該控製方法的可行性及有效性。

     

    Abstract: In view of the chaos control problem for a single-degree-of-freedom vibro-impact system with clearance, a parameter feedback control method of chaotic motion based on radial basis function neural network (RBFNN) optimized by particle swarm optimization(PSO) was proposed. Firstly, the correlation relationship and its performance characteristics between chaotic motion and excitation frequency change were analyzed, and the parameter analysis criteria of bifurcation and chaotic motion were summarized. Then, a parameter feedback chaotic controller of radial basis function (RBF) neural network was designed on the basis of the analysis. Secondly, a fitness function aiming at minimizing the distance between two adjacent points on the Poincaré section was constructed to guide the PSO algorithm to optimize the parameters of the controller. Finally, a small perturbation was applied to the controllable parameters of the system to control the chaotic motion as a stable periodic motion. This method can be applied to chaotic motion control where the model is unknown or the precise mathematical model is difficult to establish. The feasibility and effectiveness of the proposed control method were verified by simulation results.

     

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