On-demand type feedback controller for self-tuning generalized minimum variance control in state-space representation

This paper proposes on-demand type feedback controller for self-tuning generalized minimum variance control (GMVC) in state-space representation using coprime factorization. GMVC can be extended by using coprime factorization, and the extended controller introduces a new design parameter. The parameter can change the characteristics of the extended controller without changing the closed-loop characteristics. In order to design safe systems, strong stability systems are effective because both the closed-loop system and its controller are stable. So the parameter mentioned above is useful to design such a system. Moreover, focusing on feedback signal, the extended controller can adjust the magnitude of the feedback signal. It means that the proposed controller has the ability to make the magnitude of the feedback signal be zero when the control objective was achieved. In other words, the feedback signal of the proposed method emerges on demand of achieving the control objective. Therefore, this paper explores the design method of on-demand type feedback controller using self-tuning GMVC in state-space representation. A numerical example is given in order to check the characteristics of the proposed method.