# Review of Recent Type-2 Fuzzy Controller Applications
## 4 Conclusions  
This paper introduced a general overview of T2 FLCs. We briefly reviewed general T2 FLSs, as well as IT2 FLSs algorithms. Next, we presented a comprehensive literature survey of most updated real-world applications of T2 FLCs. The most applied areas of T2 FLCs are seen in robotic control, bandwidth control, industrial systems control, electrical control and aircraft control. The most promising applications are found in robotics and automotive, where improvements over traditional controllers have been achieved. It was shown that IT2 FLCs have many benefits over T1 FLCs, especially in uncertain systems, and in many cases, it was shown that IT2 FLCs outperformed traditional PID controllers, as well [95]. Moving forward, if the computational complexities of T2 FLCs can be compensated using enhanced algorithms or better software/hardware, then using these controllers for systems with uncertainty is promising. As technology develops, it is natural to witness increasing applications of these advanced controllers. It is believed that many other applications can be developed and discovered for generic and industrial applications.  
Several algorithms are being developed in the hopes of improving the robustness, performance and speed of existing algorithms. Mostly these approaches seek to calculate the centroid endpoints of IT2 FSs, which is similar to the KM method. Some algorithms that have been discussed are the EKM, INT, CJ, DY, TTCCM, LYZ and HC methods. Some of these methods work better for different applications; however, as of now, there is no conclusive evidence that one is better than the other. It is expected that newer and more advanced techniques will continue to be developed and that improvements to existing algorithms will be discovered.  
Other developments in the area of T2 FLCs are the optimization methods that are applied to these controllers. These methods are currently bio-inspired, which include GA, PSO and ACO [96]. There are also optimization methods that involve quantum computing, such as the Type-2 quantum fuzzy logic controller (T2 QFLC) [97]. Similar to the T2 algorithms, there is no consensus as to which optimization method is best as of yet. Other optimization methods that have not been used yet are membrane computing, harmony computing and electro-magnetism-based computing.  
T2 FLCs have also been increasingly combined with traditional PI, PD or PID controllers. These hybrid methods have shown more robust behavior [94]. It is however unknown whether these hybrid control methods will be implemented in industrial settings due to increased costs and computational time. To increase the speed to near real-time, FPGAs have been increasingly utilized. They are seen as an effective near real-time hardware method, which can tackle the computational time problem. Furthermore, faster FPGAs and improved software will continue to facilitate the implementation of IT2 FLCs for more applications in the future.  
An important future direction for work in Type-2 fuzzy control is using general Type-2 fuzzy systems in real control applications, [98]. As shown in our illustrative example of Section 4, general Type-2 fuzzy controllers have the potential to outperform interval Type-2 and Type-1 fuzzy controllers; the only thing limiting right now the use of general Type-2 fuzzy controllers in real-world applications is the extra computational overhead required for processing general Type-2 fuzzy systems. We believe that theoretical advances in Type-2 fuzzy logic and in hardware technology will make the use of general Type-2 fuzzy controllers possible in real-world applications in the near future.  
The authors declare no conflict of interest.