## 8 Technology Outlook  
There is a variety of technology that has the potential to influence the future development of LAC applications positively. Although the establishment is pushed forward with this work, there are various other promising control approaches, many of which are collected in Section 2. As a first step, these have to be implemented on multi-megawatt wind turbines in the field and long-term investigations have to be carried out. Another advantageous addition to conventional feedback controllers and CPC feedforward controllers is given by a multivariable approach where the transition between partial and full load operation is improved in a straightforward manner [27]. Further load reduction potential lies within the field of extreme event detection. A simulation environment has been developed that allows the modeling of realistic extreme events, aiding the design of controllers that can transition the turbine into a safety mode [60]. In addition to individual wind turbine control applications, preview measurements of the wind provided by lidar are beginning to be explored for measuring wakes as part of wind plant control strategies [61].  
Wind field reconstruction methods are being improved by (1) applying nonlinear optimization methods for estimating shear and direction simultaneously [55] and (2) integrating lidar measurements into state estimation algorithms based on fluid dynamics models, allowing the individual components of the wind to be estimated at locations not limited to the measurement points, thereby partially overcoming LOS limitations and wind evolution [62]. Additionally, in the near future, it will be possible to study wind evolution phenomena by combining lidar measurements at several range gates with high resolution wind measurements performed by a swarm of unmanned aerial vehicles (UAVs) [63].  
Finally, the primary question explored in this paper arises: \"how should lidar hardware be designed in the future to fit control purposes optimally?\". As outlined in the previous sections, there is no universal answer. Several boundary conditions like the control task (yaw, CPC, IPC, etc.), the turbine size, and the site characteristics, to list a few, determine optimal scanning angles and preview distances,  
Figure 9: **(Left)** Transfer function from frequency domain model and fitted filter using optimal circularly-scanning CW lidar scan. (Right) Power spectral density of rotor speed for feedback-only (FB) and combined feedback-feedforward (FBFF) control from frequency domain model as well as simulations using optimal lidar scan.  
etc. As a logical consequence, there is no \"one\" lidar for all LAC applications. In fact, one key to keeping a sustainable market position is to offer customizable lidar systems optimized for special purposes. This can even lead to varying designs for different turbines belonging to the same OEM. Of course, this does not imply developing dozens of lidar systems in parallel. A modularization approach could overcome the need for many different lidar configurations, by placing lidars in the context of a platform architecture. Starting from a common base, customer-specific modules could be assembled to create an entire system. Another advantage of modularization is that submodules could be optimized individually, potentially through outsourcing to specialized companies, which could increase reliability and save costs at the same time. In addition, maintenance services would likely be simplified. However, pointing out this scenario does not mean that every lidar should be trimmed to fulfill a specific task. On the contrary, there is still a need for more complex high-tech lidar systems in the future. They are typically more costly and over-designed for certain applications, but the flexibility to address many different questions with a single device can compensate for these disadvantages. Especially for the research community, this will continue to be of inestimable value.