Error Compensation for Delta Robot Based on Improved PSO-GA-BP Algorithm

Aiming to address the problem of accuracy degradation in Delta robots caused by machining accuracy, assembly precision, etc., this paper corrects the robot’s driving angles to achieve error compensation and designs a compensation algorithm based on particle swarm optimization (PSO) and BP neural network. In terms of algorithm improvement, the inertia weight and learning factors of the PSO algorithm are optimized to effectively enhance the global search ability and convergence performance of the algorithm. Additionally, the core mechanisms of genetic algorithms, including selection, crossover, and mutation operations, are introduced to improve algorithm diversity, ultimately proposing an improved PSO-GA-BP error compensation algorithm. This algorithm uses the improved PSO-GA algorithm to optimize the optimal correction angles and trains the BP network with the optimized dataset to achieve predictive compensation for other points. The simulation results show that the comprehensive error of the robot after compensation by this algorithm is reduced by 83.8%, verifying its effectiveness in positioning accuracy compensation and providing a new method for the accuracy optimization of parallel robots.