PID Control Model Based on Back Propagation Neural Network Optimized by Adversarial Learning-Based Grey Wolf Optimization

Abstract

In processes of industrial production, the online adaptive tuning method of proportional-integral-differential (PID) parameters using a neural network is found to be more appropriate than a conventional controller with PID for controlling different industrial processes with varying characteristics. However, real-time implementation and high reliability require the adjustment of specific model parameters. Therefore, this paper proposes a PID controller that combines a back-propagation neural network (BPNN) and adversarial learning-based grey wolf optimization (ALGWO). To enhance the unpredictable behavior and capacity for exploration of the grey wolf, this study develops a new parameter-learning technique. Alpha gray wolves use the random walk of levy flight as their hunting method. In beta and delta gray wolves, a search strategy centering on the top gray wolf is employed, and in omega gray wolves, the decision wolves handle the confrontation strategy. A fair balance between exploration and exploitation can be achieved, as evidenced by the success of the adversarial learning-based grey wolf optimization technique in ten widely used benchmark functions. The effectiveness of different activation functions in conjunction with ALGWO were evaluated in resolving the parameter adjustment issue of the BPNN model. The results demonstrate that no unique activation function outperforms others in different controlled systems, but their fitnesses are significantly inferior to those of the conventional PID controller.

Path Planning of Obstacle-Crossing Robot Based on Golden Sine Grey Wolf Optimizer

“…Farzad Kiani [33] proposed two different strategies to improve the grey wolf algorithm and applied them to the 3D environmental path planning of agricultural robots. Huaiqin Liu [34] combined BPNN and ALGWO to enhance the unpredictable behavior and exploration ability of the grey wolf algorithm and applied it to the PID control model.…”

Microsatellite Yaw-axis Attitude Control System Using Model Reference Adaptive Control Based PID Controller

“…This can be attributed to the fact that heuristic information are used by FLC to offer realistic and expedient alternative to addressing nonlinear problems related to control systems [14]. However, the use of FLC controller only results in steady-state error [13,15] while expert PID and fuzzy-PID controllers on the other hands, always have sub-par timing precision and limited antiinterference capabilities [16]. Even though CMGs are satellite attitude control actuators that act as torque amplifier and suitable for three-axis slew manoeuvring, the drawback of the scheme is the possibility of singularities for certain gimbal angles combination [17].…”

Special Issue on Advanced Technology of Intelligent Control and Simulation Evaluation

“…A total of eight papers in various fields of control and simulation evaluation are presented in this Special Issue. In [1], a PID controller that combines a back-propagation neural network and adversarial learning-based grey wolf optimization is presented. To enhance the unpredictable behavior and capacity for exploration of the grey wolf, a new parameter-learning technique is developed.…”