Tuning of PID Controller Using GA, SA and QFT for Three Interacting Tank Process

A Temperature Control Method of Lysozyme Fermentation Based on LRWOA-LSTM-PID

“…In [6,34], PID and BP-PID have been widely applied in the industry and are the two most representative controllers. In [17][18][19][20][21][22]39], heuristic algorithms are used to optimize The input data of the control system are different from the dataset of traditional neural networks. The input of the neural network in this paper is a time series sequence.…”

“…In [6,34], PID and BP-PID have been widely applied in the industry and are the two most representative controllers. In [17][18][19][20][21][22]39], heuristic algorithms are used to optimize the initial parameters of the BP neural network, among which PSO-BP-PID is the most representative. In [37], the method of using the RNN is employed to optimize the PID parameters, which demonstrates excellent position tracking results and robustness against variations in system parameters.…”

“…The PID parameter tuning method based on the Particle Swarm Optimization (PSO) algorithm [17][18][19][20] combines bird foraging behavior, group organization, and optimization mechanisms to better optimize system performance, but it can fall into local optima and premature convergence. The PID parameter tuning method based on the Genetic Algorithm (GA), through a compilation mechanism [21][22][23][24][25], avoids the PID parameter optimization from falling into local optimal solutions. Although the aforementioned meta-heuristic algorithms have strong adaptability and high efficiency [26][27][28][29], their nature of work is still based on iteration, and they are usually based on one-time parameter optimization.…”