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Input-output pairing criterion applied in the genetic algorithm for unstable linear systems

Baban P. Q., Rahimabadi I. N.

Bulletin of the Polish Academy of Sciences. Technical Sciences

2016

Vol. 64, nr 4

873--876

In this paper, a new approach towards input-output pairing for an unstable system has been proposed. First, it is demonstrated that the previous method of input-output pairing for unstable plants cannot find appropriate pairs as it only checks necessary conditions for stability and integrity. Then, a new approach using relative error matrix and genetic algorithm for finding appropriate pairs in unstable systems is proposed. As it is shown, this approach takes into consideration both static and dynamic information of plant in measuring interaction. Finally, the accuracy of proposed method is demonstrated by an example and closed loop simulation.

A matrix inequality based design method for consensus problems in multi-agent systems

Zhai G., Okuno S., Imae J., Kobayashi T.

International Journal of Applied Mathematics and Computer Science

2009

Vol. 19, no 4

639-646

In this paper, we study a consensus problem in multi-agent systems, where the entire system is decentralized in the sense that each agent can only obtain information (states or outputs) from its neighbor agents. The existing design methods found in the literature are mostly based on a graph Laplacian of the graph which describes the interconnection structure among the agents, and such methods cannot deal with complicated control specification. For this purpose, we propose to reduce the consensus problem at hand to the solving of a strict matrix inequality with respect to a Lyapunov matrix and a controller gain matrix, and we propose two algorithms for solving the matrix inequality. It turns out that this method includes the existing Laplacian based method as a special case and can deal with various additional control requirements such as the convergence rate and actuator constraints.