A comprehensive evaluation of the effectiveness and robustness of statistical methods and artificial intelligence in control applications

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Artificial intelligence has a wide variety of applications. Novel research attempts to implement AI and general statistical models in a variety of applications, already with many successes. Nevertheless, the great freedom of choice in model design for any implementation complicates optimisation, while the statistical nature of such models can often be deemed not sufficiently robust. This is especially the case in real-life control, where the margin for error and its consequences are of crucial importance in system design. This project proposes to investigate possible ways to design effective statistical control systems for any general control application, exploiting their benefits while minimising their drawbacks in data collection, robustness and usability.