Gene Regulatory Network Inspired Cooperative Controllers

Comparing to neural networks, genetic regulatory networks (GRNs) have two variables, mRNA and proteins, interacting in a very complex way. This special structure of GRNs promises better adaptive performance. In this chapter, cooperative controllers inspired by GRNs are proposed. First, the robust H ∞ state estimation problem is investigated for a class of discrete-time stochastic GRNs with probabilistic measurement delays. Then, a bio-inspired cooperative controller via evolution of GRN is presented to make the performance index of each stage stable over the whole study horizon and the overall performance index close to the desired value against vibrations simultaneously.

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