CHEN Hao, WANG Yagang, BAI Chong, HU Zhenli, WU Qibiao, TIAN Xinchi
Control Engineering of China. 2025, 32(7): 1207-1216.
In the control of bronchoscopic robots, the accuracy of the conventional proportional integral differential (PID) control is insufficient, and the back propagation (BP) neural network is prone to fall into local optimum. To solve the problems, a BP neural network-PID control method optimized by the improved whale optimization algorithm (IWOA) is proposed. Firstly, based on the conventional whale optimization algorithm, IWOA introduces a nonlinear convergence factor to dynamically balance the global search ability and local search accuracy, optimizes the population distribution by using tent chaotic mapping, enhances global optimization by using the Lévy flight strategy, maintains population diversity by combining the greedy selection mechanism, and provides the optimal initial connection weights for the BP neural network. Then, the BP neural network fuses the reference input, system output and tracking error at the input layer, and dynamically adjusts the PID control parameters through backpropagation. The simulation results show that, compared with PID control, BP neural network-PID control and their improved methods, the proposed method can significantly reduce the overshooting of the system, shorten the regulation time, and make the steady-state error approach zero. This method has good control accuracy and anti-interference ability, so it can significantly reduce mechanical vibration and tissue friction during operation, and improve the safety of bronchoscopic surgery.
