# Review of Recent Type-2 Fuzzy Controller Applications
## 3 Review of IT2 FLSs  
### Robotic Control  
In [37], a T2 FLC was designed for navigating a mobile robot in dynamic environments with a hierarchical structure (HFLC). The HFLC reduces the number of rules to increase the speed in real-time applications. Using the T2 FLC resulted in very good real-time control. In [38], an IT2 Takagi-Sugeno-Kang (TSK) FLC was developed for modular and reconfigurable robots (MRRs). This controller was designed for tracking purposes, which can be implemented in real time. Experiments were done on the IT2 TSK FLC, which shows that it can be applied with guaranteed uniformly ultimately bounded (UUB) stability. It was also determined that the proposed controller can, in some cases, outperform some linear and non-linear controllers in similar configurations. In [39], [PERSON] _et al._ proposed a T2 fuzzy control system (FCS) for biped robots based on a switched nonlinear model. This paper also proposes a new fuzzy c-mean variance (FCMV) clustering algorithm to model the T2 FCS. At the time, there were no appropriate mathematical tools to design a control system for the biped robot. Prospects from this work are to develop a switched system theory for industrial applications. In [40], a mobile robot wall-follower was controlled using a reinforcement ant optimized fuzzy controller (RAOFC). The rules are automatically generated for the robots using an aligned IT2 fuzzy clustering (AIT2 FC) technique. Using the AIT2 FC reduced the number of FSs for each input dimension. Furthermore, the consequence of each rule is generated using a Q-value-aided ant colony optimization (QACO). It was found that by using T2 FSs, the FC was more robust to uncertainties in the environment and noise. The Pioneer 3-DX was an experimental platform for which an optimized IT2 fuzzy PID controller was designed [41]. This controller was evaluated for the path tracking problem. It was found that the IT2-FPID controller was superior in the control performance in the presence of disturbances and uncertainties in comparison to PID, T1-PID and T1-FPID self-tuning controllers. T2 FCs have been implemented in many real-time control applications; however, the main issues of adopting such systems is their complexity in large-scale implementations. T2 FLCs systems have not been \"widely\" implemented in robotics due to the limitations of mathematical proofs to have closed-loop system stability, although there are some recent works that have been able to overcome this problem, such as the work of [PERSON] _et al._[42]. In [43], [PERSON] and [PERSON] implemented an IT2 FLCs on a flexible-joint manipulator. This work takes into account a trade-off between the link position and the actuators' internal stability, and it is based on sliding mode control. It was shown that this controller was superior to T1 FLC in simulations. In [44], an IT2 TSK FLS was developed for tracking control of robot manipulators. The IT2 TSK FLS was able to approximate the uncertainty and the non-linear robot dynamics. This was then incorporated into a controller, which combined the IT2 TSK FLS and a sliding mode control (SMC) with different weights. This controller was designed to minimize the approximation errors and to reduce the amount of uncertainty in the system. An advantage of this design was that the IT2 TSK FLS controller combines the robustness of SMC with the adaptive IT2 TSK FLS. Experiments were done on a robot with two degrees of freedom. It was concluded that the IT2 TSK FLS controller performed better than an adaptive IT1 FLS and provided more flexibility. In [45], an IT2 FLC was used for controlling multiple robots to cooperate and reach certain targets. This controller was optimized using a particle swarm optimization algorithm (PSO). The PSO algorithm can find a solution to an optimization problem by searching space and predict social behavior in the presence of objectives [46]. This method allows the robot to find the best solution by iterations after the particles of the algorithm are randomly distributed.