Adaptive Variable Impedance Force Control for Robotic Manipulators Based on Improved Artificial Lemming Algorithm

To address the issues of weak adaptive capability of impedance parameters and insufficient anti-disturbance ability in robot constant force control under uncertain environments, a compliant control method integrating Fractional Order Active Disturbance Rejection Control (FOADRC) and Radial Basis Function Neural Network (RBFNN) is proposed. Firstly, a FOADRC composite controller is designed in the inner loop of position control to estimate and compensate the total system disturbance in real time. Secondly, an adaptive variable impedance model based on RBFNN is constructed in the outer loop, where convolutional operations are embedded in the hidden layer of the neural network to accelerate feature extraction, realizing the nonlinear mapping from force/position errors to impedance parameters. Furthermore, the Improved Artificial Lemming Algorithm (IALA) is used to tune the controller parameters in both the inner and outer loops. Simulation experiments show that the proposed method can better achieve the desired force tracking, reduce the force overshoot and force fluctuations during contact.