Neural Network Control
Neural Network Control
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发表时间：
2003
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通讯作者：
D. Eggert
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作者：
D. Eggert
本论文研究两个以类神经网路为基础的控制系统。第一种是基于神经网络的预测控制器。讨论了系统辨识和控制器设计。第二种是直接神经网络控制器。讨论了参数选择和训练方法。这两种控制器在两个不同的工厂进行了测试。有关实施的问题进行了讨论。首先，基于神经网络的预测控制器被引入作为广义预测控制器（GPC）的扩展，以允许控制非线性植物。控制器的设计包括GPC参数，但预测是明确地通过使用神经网络模型的工厂。讨论了系统辨识问题。两个控制系统构造两个不同的植物：一个耦合的坦克系统和倒立摆。这显示了如何在系统识别期间处理诸如设备激励的实施方面。控制器类型的限制进行了讨论，并显示在两个实现。本文第二部分讨论了直接神经网络控制器。输出反馈控制器是围绕神经网络构造的。控制器参数的确定使用系统仿真。该控制系统作为单步超前控制器应用于两个不同的工厂。其中之一是与倒车拖车卡车有关的路径跟踪问题。该系统说明了一种逐步增加控制器复杂性的方法来处理不稳定的控制对象。第二种设备是耦合罐系统。与第一个控制器进行比较。两个控制器都显示工作。但对于基于神经网络的预测控制器，特别是当需要长预测时域时，构造高精度的神经网络模型是关键。这限制了对可以建模到足够精确的植物的应用。直接神经网络控制器不需要模型。相反，控制器在工厂的模拟运行上进行训练。这需要仔细选择训练场景，因为这些场景会影响控制器的性能。
This thesis addresses two neural network based control systems. The first is a neural network based predictive controller. System identification and controller design are discussed. The second is a direct neural network controller. Parameter choice and training methods are discussed. Both controllers are tested on two different plants. Problems regarding implementations are discussed. First the neural network based predictive controller is introduced as an extension to the generalised predictive controller (GPC) to allow control of non-linear plant. The controller design includes the GPC parameters, but prediction is done explicitly by using a neural network model of the plant. System identification is discussed. Two control systems are constructed for two different plants: A coupled tank system and an inverse pendulum. This shows how implementation aspects such as plant excitation during system identification are handled. Limitations of the controller type are discussed and shown on the two implementations. In the second part of this thesis, the direct neural network controller is discussed. An output feedback controller is constructed around a neural network. Controller parameters are determined using system simulations. The control system is applied as a single-step ahead controller to two different plants. One of them is a path-following problem in connection with a reversing trailer truck. This system illustrates an approach with step-wise increasing controller complexity to handle the unstable control object. The second plant is a coupled tank system. Comparison is made with the first controller. Both controllers are shown to work. But for the neural network based predictive controller, construction of a neural network model of high accuracy is critical especially when long prediction horizons are needed. This limits application to plants that can be modelled to sufficient accuracy. The direct neural network controller does not need a model. Instead the controller is trained on simulation runs of the plant. This requires careful selection of training scenarios, as these scenarios have impact on the performance of the controller.