With data-driven control it is possible to design a controller for systems with non-parametric models. The intermediate step of modelling or identification of the system is not necessary, because a non-parametric model of the system can be obtained by means of experimental data. In the linear time-invariant (LTI) framework, these non-parametric models are well defined in the frequency-domain, they are represented as frequency response functions (FRFs). Data-driven control in the frequency-domain has gained momentum in the past decades, but still relatively few methods have been developed for multiple-input-multiple-output (MIMO) systems. One of the objectives of this thesis is to develop a controller synthesis method for MIMO systems. While the LTI framework has many advantages, such as the vast available literature and the relatively simple theory, it has its limitations. In reality, all systems have nonlinearities and some systems, especially position dependent systems (which are common in mechatronics), are less suitable to be modelled as LTI systems. The nonlinear dynamics are most likely interpreted as an uncertainty for which a robust controller has to be designed at the cost of performance. By taking into account the scheduling variable, in this case the position dependency, it is possible to improve the performance. To do so, the system is modelled as a linear parameter-varying (LPV) system for which an LPV controller is designed that takes the scheduling variable into account. This thesis is concerned with developing a controller synthesis approach in the LPV framework using non-parametric models of MIMO systems using the local approach. This implies that an LPV controller is designed for the nonlinear system at the operating points of interest, at these points the system is assumed to exhibit LTI behaviour. What follows is an interpolation of the multiple LTI controllers to obtain a global parametrisation of the controller in the entire operating range, such that local stability and performance is guaranteed at every operating point. Two novel data-driven LPV controller synthesis methods, based on LTI methods, are presented in this thesis. These methods improve the performance of a parameter dependent system compared to an LTI approach. The improvements are shown through simulations with nonlinear systems. From these simulations it can be concluded that the LPV controllers improve the performance compared to a similar LTI controller. Furthermore, an example is shown where an LPV controller is crucial to guarantee closed-loop stability of the nonlinear system.