Empirical investigation and analysis of the computational potentials of bio-inspired nonlinear model predictive controllers: success and challenges

In this investigation, a comprehensive study is carried out to excavate the potentials of bio-inspired computing (BIC) for the development of model predictive controllers (MPCs) for different classes of nonlinear problems. The two mentioned fields are now playing pivotal roles in industry, and there is a large consensus on the fact that BIC and MPCs are among the most applicable techniques in the coming decades. One of the most important decisions for developing MPCs is the selection of the optimisation technique. Here, the authors would like to demonstrate the applicability of BICs to be used as an optimisation method at the heart of MPCs to calculate the controlling commands. The resulting controllers are applied to some challenging problems to clearly demonstrate the applicability of BICs for developing high-performance MPCs.

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