# Review of Recent Type-2 Fuzzy Controller Applications
## 3 Review of IT2 FLSs
### Robotic Control
After this, the best values are found, and the particles update their velocity and position. To handle the collision avoidance, a hybrid reciprocal velocity obstacles (HRVO) were used in tandem. It was concluded that the PSO-optimized IT2 FLS was faster the non-optimized IT2 FLS. [PERSON] _et al._ in [47] used a genetic algorithm (GA) for the optimization of a T2 FLC for the dynamic model of a unicycle mobile robot. In [48], a combination of PSO and GA was used and was proved to have the best performance possible. The same combination of algorithms was used later on to minimize the steady state error of linear systems in [49]. Furthermore, in [50], a hybrid PSO-GA optimization method was used for the automatic design of the FLC. The optimal FLC design was tested using benchmark control plants and an autonomous mobile robot for trajectory tracking control. This bio-inspired method was determined to be feasible for these control applications, and it is a feasible method for improving FLC designs. The ant colony optimization (ACO) is another method used to design optimal T2 FLSs. The ACO technique is inspired by the foraging behavior of real ants. It is a population-based, meta-heuristic technique that takes advantage of past performance memory and mainly depends on values defined for its parameters [51]. ACO was found to be robust and versatile in handling a wide range of combinatorial optimization problems [46]. In [51], a new fuzzy approach was presented for diversity control. This approach was used in controlling the trajectory of a unicycle mobile robot with optimized MFs. For this mobile robot, encouraging results were demonstrated. Tuning techniques for T2 FLCs are widely used, and they have been applied in two different ways. The first is by tuning the MFs of a given rule set, and the second method is by selecting an optimal subset of rules from all of the possible rules [52]. For tuning FLCs, evolutionary algorithms are the most adopted. However, a simple tuning algorithm (STA) can also be used. The STA was designed to improve the response of T1 FLCs in practical, intuitive and simple ways. In [53], an extension of the STA was presented on the theory of T2 FLCs using a parallel model implementation. This paper also presented a mechanism to calculate the feedback gain, new integral criteria parameters and the effect of the AND/OR operator combinations on the fuzzy rules. All of these mechanisms helped in improving the performance and applicability. Tuning techniques can also be classified as derivative-based, derivative-free and hybrid tuning methods. This classification refers to the information that is used during the tuning process. Extended discrete action reinforcement learning automata (EDARLA) is a technique that tracks the input reference signal by computing MF parameters. EDARLA was presented in [54], where it is used for adapting parameters and tuning processes. This technique searches for parameters in every cell dimensional space. It updates at discrete sample points, so the chance of obtaining better results is more likely to occur. Discrete action reinforcement learning automata (DARLA) and continuous action reinforcement learning automata (CARLA) are two other techniques used. The DARLA technique is operated by selecting actions in a stochastic trial and error process in an unknown environment, whereas CARLA is operated on a separate action [54]. The automata set in CARLA runs in a parallel implementation, thus determining multiple parameters. Actions have an associated probability density function, which helps in the selection process for better performance purposes. In [52; 54], it was proven that the best results were obtained using T2 FLCs systems using the EDARLA technique compared to CARLA and DARLA. Robotic excavator controllers were also designed based on IT2 FLC structure. In [55], the servo position control of an electro-hydraulic excavator was demonstrated. The controller utilized fluid flow rates, pump hydraulics and friction forces.