Anti Impact Drilling Robot Walking Lateral Correction Based on Fuzzy Neural Network Compensation LQR

The walking control of the anti-impact drilling robot is an important prerequisite for the use of drilling robots for pressure relief operations. In the walking process of the anti-impact drilling robot, encountering different working conditions such as unstable center of gravity, undulations and obstacles will lead to inconsistent working resistance of the walking motors on both sides, resulting in mistracking, which needs to be corrected in real time. Therefore, a robot walking lateral control method based on fuzzy neural network nonlinear compensation linear quadratic regulator (FNN-LQR) is proposed for adjustment. Firstly, a walking lateral correction controller based on linear quadratic optimal control model is designed, and a fuzzy neural network optimization model for nonlinear walking lateral deviation compensation is established. Secondly, the walking lateral deviation control algorithm is designed to improve the lateral control accuracy of the anti-impact drilling robot. Then, simulation experiments are carried out, and the simulation shows that the lateral control method can meet the lateral correction requirements of anti-impact drilling robots. Finally, part of the experimental analysis verify the feasibility and superiority of the proposed method.