Automatic Identification and Switching of Multi-MRAC Systems

Controlling hybrid systems - a system that exhibits continuous and discrete behavior simultaneously - is of great interest since the new millennium. Switched linear systems are especially interesting due to the large amount of applications that may be solved. However, applying different control schemes on switched systems entails difficulties in identifying the underlying models and the transitions that occur between them. In this paper an automatic identification and switching for Multi-Model Reference Adaptive Control (MMRAC) scheme is proposed. The identification of the submodels is performed by curve clustering of the states plotted in the phase portrait. An unsupervised learning algorithm is proposed to cluster the curves. Each curve represents a single submodel and is paired with an MRAC. After the clustering process, correlation between every submodel and the current state is checked. Then the MRAC paired with the best representing curve is used to control the plant, and update the parameters of the curve and the MRAC itself. The results of two simulations are presented in the end of this paper.