Time-Varying Target Predictive Entrapment Based on Gene Regulatory Network and Sliding Mode Control

To address slow convergence and formation maintenance challenges in swarm robotic entrapment tasks, a predictive entrapment control algorithm that combines gene regulatory network and sliding mode control (GRN-SMC) is proposed. First, to stabilize the target position information generated by the hierarchical GRN, a novel sorting rule is designed. Then, an artificial neural network (ANN) is employed to perform on-line prediction of the swarm robots’ kinematic states. These predicted values are then fed into a specifically designed sliding mode controller, which ultimately outputs the optimal control velocities for the swarm robots. Comparative simulation experiments with three state-of-the-art algorithms demonstrate that our method achieves significant improvements in tracking accuracy(error reduced by 82%), single-iteration execution time(reduced by 29%), and formation maintenance (formation integrity increased by 34%). Furthermore, physical robot experiments demonstrate that even in the presence of unknown external disturbances (such as ground slippage) and robot positioning errors (±0.1 m, ±5°), the proposed algorithm still exhibits excellent robustness.