Joint design of model set identification and learning type control

对于模型集识别和学习类型控制的联合设计，我们得到了以下结果：模型集识别的难点之一是模型集识别的框架与传统的随机参数识别方法不一致。因此，得到的模型集趋于保守。我们提出了一种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的识别方法。这部分地克服了这个缺点。通过柔性结构的实验验证了该方法的有效性。接下来，我们考虑了一类称为哈密顿系统的非线性系统。这门课包含机械系统和电气系统的结合。我们已经澄清了固有结构，如被动和伴随系统，它们构成了学习控制对这类系统适用性的基础。对于学习控制，现有方法的主要缺点是当无法获得被控对象的精确信息时，必须使用误差信号的微分。我们已经用几种方法解决了这个问题。一是在采用迭代学习控制时，将输入空间限制为规定的有限输入信号，这与模型识别密切相关。另一种是利用哈密顿系统的伴随系统的I/O信号来计算其相对于给定代价函数的梯度。这里的重点是，我们可以在没有任何模型参数的情况下实现这一点。通过非线性机械臂的实验证明了这两种方法的有效性。我们还开发了一种新的迭代反馈调谐方法，该方法对摩擦具有鲁棒性。将学习控制与模型集识别相结合的更有效的方法是未来的研究工作。

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.