Simulation-based optimum sensor selection design for an uncertain EMS system via Monte-Carlo technique

Optimum sensor selection in control system design is often a non-trivial task to do. This paper presents a systematic design framework for selecting the sensors in an optimum manner that simultaneously satisfies complex system performance requirements such as optimum performance and robustness to structured uncertainties. The framework combines modern control design methods, Monte Carlo techniques and genetic algorithms. Without loosing generality its efficacy is tested on an electromagnetic suspension system via appropriate realistic simulations.

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