Intelligent Lifting Robot and Its Control System Based on Genetic Algorithm

Aim: In this work, a new intelligent control method that combines a genetic algorithm with a fuzzy control approach is used to look into the control system of an intelligent crane robot made for a gantry crane robot system with second-order nonholonomic constraints. Methodology: To create the intelligent control system for the gantry crane robot, fuzzy control techniques, and genetic algorithms were integrated. This method can swiftly and accurately achieve the position control task of the gantry crane robot while maintaining good stability. Results: The results show that the final two joint angles tend to stabilize the expected values, with angle errors of (0.031, 0.004) rad and relative errors within 3%. The active joint driving torque curve, the whole movement process, is relatively stable, and the expected position is achieved accurately, which fully shows that the designed controller is effective for the position control of the gantry crane robot. Conclusion: This method can be extended to the position control of multi-DOF gantry crane robots. When dealing with the high-dimensional problems of MIMO complex fuzzy models, the introduced structural decoupling identification method can fundamentally solve the dimensional disaster problem of multi-input, multi-output fuzzy systems.