Abstract: In modern control applications such as soft robotics or human-robot-interaction, the control design and modelling process becomes very time-consuming or even infeasible due to the complexity of the models. A solution to overcome these issues is provided by data-driven models which require only a minimum of prior knowledge since the complexity of the model scales with the amount of training data. However, a general problem of data-driven models is the estimation of the model accuracy which is usually necessary for robust control design and stability. In this project, we use Gaussian Process models since they provide not only a mean prediction, but also a variance as uncertainty measure of the model. We exploit this uncertainty to derive control laws which guarantee the stability of the closed loop. In addition, the control error based on the number and distribution of training points is determined. As consequence, more advanced control strategies can be applied to safety-critical systems which results in an improved overall performance.

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