Joint design of model set identification and learning type control

对于模型集辨识与学习型控制的联合设计，我们得到了如下结果：模型集辨识的难点之一是模型集辨识的框架与传统的随机参数辨识方法不一致。因此，所得到的模型集趋于保守。我们已经提出了识别方法，通过考虑噪声和输出信号之间的独立性等随机特性来获得模型集。这部分克服了这一缺点。通过柔性结构的实验验证了该方法的有效性。其次，我们考虑了一类非线性系统，称之为Hamilton系统。此类包含机械系统和电气系统的组合。我们阐明了无源性和伴随系统等固有结构是学习控制适用于这类系统的基础.对于学习控制，现有方法的主要缺点是在不能获得被控对象精确知识的情况下，必须利用误差信号的微分.我们已经用几种方法解决了这个问题。一种是采用迭代学习控制时，将输入空间限制为给定的有限个输入信号，这与模型辨识密切相关。另一种方法是利用Hamilton系统的伴随系统的I/O信号来计算其相对于给定代价函数的梯度。这里的要点是，我们可以在没有任何模型参数的情况下实现这一点。这两种方法的有用性，通过使用非线性机械手的实验证明。本文还提出了一种新的迭代反馈整定方法，该方法对摩擦具有鲁棒性，而将学习控制与模型集辨识相结合的方法则有待于进一步研究。

As for the joint design of model-set identification and learning type control, we have obtained the following results.Concerning to the model-set identification, one of the difficulties is that the framework of model set-identification is not consistent with the traditional stochastic approach of parameter identification. As a result, the obtained model set tends to be conservative. We have proposed identification methods which obtain model sets by taking the stochastic properties such as independency between noises and output signals into account. This overcomes the shortcoming partially. The effectiveness is evaluated through experiments using flexible structures.Next, we have considered a class of nonlinear systems which are called Hamiltonian systems. This class contains a combination of mechanical systems and electrical systems. We have clarified that the inherent structures such as passivity and adjoint systems, which form a basis of applicability of learning control to this class of systems.As for learning control, major demerits of the existing methods is that they have to use differential of error signals when the precise knowledge of the plants is not available. We have solved this problem in a couple of ways. One is to restrict the input space into a prescribed finite input signals when we adopt an iterative learning control, which turns out to be related to model identification very closely. The other is to use I/O signals of the adjoint systems of Hamiltonian systems in order to calculate its gradient with respect to given cost functions. The point here is that we can achieve this without any model parameters. The usefulness of both methods are demonstrated through experiments using nonlinear manipulators. We also have developed a new method of iterative feedback tuning which is robust against frictions.The development of more effective way of combining learning control with model-set identification is left as a future research work.