抄録
There are many plants which should be assumed to be a linear time-varying system in order to improve their transient properties. The plant can be handled with the MRAC (Model Reference Adaptive Control) and the learning control, which decreases a control error through trials when the control can be applied repeatedly. However, the former requires a priori information about the time-varying structure of the plant and the latter requires a great part of the plant characteristics in order to design a stable learning control system. These requirements can be satisfied rarely in the practical situation.
We redesign MRAC from the view point of the learning control and call it MRLC (Model Reference Learning Control), which can control the discrete-time linear time-varying system without any knowledge except it's system order and time delay in the input. Since the input is generated through the feed back manner, the control system can cope with a change of the target responses between trials unlike the learning control where it is done through the feed forward manner. This paper presents a detail description and a stability analysis of the MRLC system. Numerical simulations are also performed for the verification of the analysis.
