# A New General Type-2 Fuzzy Predictive Scheme for PID Tuning
## 2 Literature Review  
The computational intelligence is recently widely used in optimization problems [3; 4; 5]. The PID tuning by the neural-networks (NNs), FLSs and evolutionary optimization techniques have recently been studied. For example, in [6] the Kalman filter is developed for optimizing a PID in control of joint robotic manipulator. In [7], by the use of Matlab toolbox a model is estimated and then the parameters of PID are adjusted and evaluated on a robotic application. The harmony search technique is developed in [8] for destining an optimal PID. The gradient descent is suggested in [9] for tuning and the convergence speed is analyzed. In [10], the NNs are used to determine the gains of PID and the back-propagation scheme based on Smith predictor is developed for training of NNs. In [11], a NN is learned to tune the PID gains and effect of nonlinearities is investigated. In [12], using the NNs and FLSs, the gains of PID are calculated, and it is shown that the suggested approach has less settling time than the other models. In [13], a robust PID is introduced for Quadcopters that are constantly exposed to parametric changes as well as external disturbances. In [2], for tuning the parameters of PID, a deep learning NN is suggested and it is shown that, the number of attempts to adjust the controller is reduced, and also less data is needed for learning. In [14] to remote control operation of underwater vehicles with unknown disturbances, an NN based self tuning PID controller is designed to track the route, and it is shown that compared to conventional PIDs, suggested method shows better performance with less energy. In [15], a PID controller is optimized by NNs and it is applied for oxygen control system. The other class of PID regulation methods is the evolutionary based algorithms. For example the weight beetle antenna method is developed in [16], for PID tuning and its performance is evaluated on hydraulic plant. The genetic algorithm is applied in [17] to tune the PID gains and the optimized PID is used for a temperature control problem. The various swarm-based tuning methods are studied in [18] and a new scheme is suggested by combining the bacterial foraging and particle swarm optimization (PSO) methods. In [19], the performance of PSO is improved by the use of chaotic systems and the optimized PIDis applied for a voltage control problem. The multi-objective PSO is formulated in [20] for optimizing the PID controller of a aerial vehicle. The Chien-Hrones-Reswick approach is analyzed in [21] and it is applied for glucose regulation problem. The continuous stirred tank reactor (CSTR) is one of the units used in the industry to perform some chemical activities and produce products. As the operating level of the system changes, CSTRs exhibit nonlinear behavior. To better control of CSTR some PID based methods have been designed. For example, in [22], considering two input disturbances, a Particle Swarm optimization (PSO)-based PID is proposed. In [23], a PID-based fuzzy controller is designed to control the CSTR temperature. In this controller, the gain is controlled and determined by an adaptive stabilization block. By the results comparing, it is shown that FLS based PID has a much better performance in tracking than the conventional PIDs. In [24], the dynamically updated PID is suggested to control the CSTR units under variable parameterise. The gray wolf optimization scheme is suggested in [25] for PID tuning, and it is shown that the suggested tuning scheme gives better control accuracy than genetic and PSO algorithms. The predictive controller is one of the interesting approaches to cope with system restrictions and improve the tracking performance. However, this technique has been rarely used for accuracy improvement of PID. For example, in [26], a PID is optimized by the use of FLSs and a predictive controller is used to improve the performance. In [27], the MPC is developed for control of eco-driving and it is compared with PID.