【作者】 刘小河； 苏杭； 刘丽华； 张耀辉；

【Author】 Liu Xiaohe,Su Hang,Liu Lihua,Zhang yaohui School of Automation,Beijing Information Science&Technology University,Beijing 100085,P.R.China

【机构】 北京信息科技大学自动化学院；

【摘要】 讨论了神经网络在非线性系统自适应控制中的应用。针对一类典型的非线性系统,基于超稳定性理论,采用基于BP算法的神经网络设计了非线性环节的逆模型和模型参考自适应控制器,目的在于改善非线性被控对象跟踪参考模型输出的跟踪速率,并以此进行了MATLAB仿真。对电弧炉三相电极调节系统的仿真结果表明,神经网络自适应逆控制的跟踪速度明显优于PID控制,并且提高了系统的动态性能。更多还原

【Abstract】 This paper discussed the neural network in the application of nonlinear system adaptive control.For a typical class of nonlinear system,A BP neural network is designed to control the system based on hyperstability.The nonlinear link was eliminated by introducing its inverse model,for transforming the nonlinearity into a linear system.The neural network controller’s purpose is to optimize the effect on tracing velocity and make the output of controlled object closing to the one of reference model.Satisfactory results have been achieved by using MATLAB simulation on the model for three-phase electrode regulator system of arc furnace.The simulation and analysis shows that neural network adaptive control system is more effective than the traditional PID system,and guarantees the system has a better dynamic quality.更多还原