A fuzzy model based adaptive PID controller design for nonlinear and uncertain processes

A novel auto-tuning PID control mechanism for nonlinear systems

“…The PID parameters which are tuned by (11) and (12) may not be appropriate enough to produce an optimal control signal ðu½n þ 1Þ when the reference signals and dynamics of the system change due to modeling errors and external disturbances. In the case that the PID controller is inadequate, a correction term δu½nþ1, which is obtained so as to minimize the F, is added to the control action produced by the mechanism.…”

“…Therefore many PID controllers namely Sliding-mode (SM) adaptive PID controller for uncertain systems [5], neural-network (NN) based adaptive PID controller for the systems with unknown dynamics [6][7][8][9] and support-vector machine (SVM) based PID controller [10] have been proposed to tune PID parameters in the literature. Adaptive control scheme can be alternatively invoked a PID controller in cascade with fuzzy predictor [11]. Also, many new PID controllers which were tested for electromechanical systems are proposed in the literature [12][13][14][15][16][17].…”

A fractional order fuzzy PID controller for binary distillation column control

“…Savran and Kahraman developed an adaptive PID control technique which used fuzzy process model for the adaptation of the gains of PID controller (Savran and Kahraman, 2014). They used a fuzzy predictor to obtain multi-step ahead output of the process, which was further used to adapt the gains of the PID controller by minimizing the sum of the squared errors between the predicted output and the reference input.…”

A torque estimator using online tuning grey fuzzy PID for applications to torque-sensorless control of DC motors

“…But the fuzzy is a nonlinear control and the controlled system output has a static error [11]. Then fuzzy PID control which combines the traditional PID and fuzzy control algorithms is a solution [11][12][13][14][15][16][17][18]. However, the typical fuzzy PID controllers are experimentally designed based on working conditions of the control systems and their dynamic responses [19].…”

“…Hence, the typical fuzzy PID controllers cannot adapt for a wide range of working environments with large variation of perturbations [18]. As a result, other control techniques such as robust control, intelligent theory, or estimation methods are needed to combine with the fuzzy PID to overcome this weakness [14][15][16][17][18][19][20][21]. Among them, fuzzy PID combined with neural network and grey predictive techniques is a feasible solution.…”