Characterization of Two-Cylinder Parallel Electro-hydraulic Force/Position Synchronization Based on RBF Fuzzy Neural Network Control

Due to the two-cylinder parallel electro-hydraulic force/position synchronous drive system, the synchronous control accuracy under the master–slave synchronous control condition is low, as well as the problem of poor suppleness during force/position switching. In order to solve this problem, we take the double-cylinder synchronous control system as the research object, establish the mathematical model of the double-cylinder parallel electro-hydraulic force/position synchronous control system, and design a PID controller based on the fuzzy radial basis function (radial basis function, referred to as RBF) neural network control strategy. The MATLAB/Simulink simulation model based on classical PID, fuzzy PID, and fuzzy RBF neural network PID controller is constructed, respectively, and the control effect of force and position is compared and analyzed by three different controllers, respectively, and it is concluded that the fuzzy PID control and the fuzzy RBF neural network PID control satisfy the usage requirements of the position control system and the force control system, respectively. Finally, based on Matlab/Simulink and AMESim platforms, a joint simulation is carried out on the effect of synchronized control of parallel electro-hydraulic force/position switching of two cylinders, and the PID controller based on the fuzzy RBF neural network control strategy is experimentally verified to be effective in improving the synchronized control accuracy and the suppleness problem during the switching of force/position control modes.