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Intelligent control using multiple models and neural networks

机译:使用多种模型和神经网络进行智能控制

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摘要

Most adaptive control algorithms for nonlinear discrete time systems become invalid when the controlled systems have non-minimum phase properties and large uncertainties. In this paper, an intelligent control method using multiple models and neural networks (NN) is developed to deal with those problems. The proposed control method includes a set of fixed controllers, a re-initialized neural network (NN) adaptive controller and a free-running NN adaptive controller. The bounded-input-bounded-output (BIBO) stability and performance convergence of the system are guaranteed by the free-running adaptive controller, while the multiple fixed controllers and the re-initialized adaptive controller are used to improve the transient response. Simulation results are presented to demonstrate the effectiveness of the proposed method.
机译:当受控系统具有非最小相位特性和较大不确定性时,大多数非线性离散时间系统的自适应控制算法将失效。在本文中,开发了一种使用多种模型和神经网络(NN)的智能控制方法来解决这些问题。所提出的控制方法包括一组固定控制器,一个重新初始化的神经网络(NN)自适应控制器和一个自由运行的NN自适应控制器。自由运行的自适应控制器保证了系统的有界输入界输出(BIBO)稳定性和性能收敛性,同时使用多个固定控制器和重新初始化的自适应控制器来改善瞬态响应。仿真结果表明了该方法的有效性。

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