Adaptive impedance control of climbing robot based on improved firefly algorithm

To address the challenge of unknown and time-varying impedance control parameters in manipulators, a method for adaptive generation and optimization of impedance parameters using an enhanced firefly algorithm is proposed. Initially, the manipulator’s dynamics model is constructed using the Lagrange method. Subsequently, an objective function is formulated to minimize the errors in both the end displacement and contact force of the manipulator. The impedance parameters are then mapped to the positions of fireflies, and the firefly algorithm is employed to generate and optimize these parameters. To tackle issues such as low search efficiency and susceptibility to local optima in the standard firefly algorithm, a random attraction model and an adaptive step factor strategy are integrated into the algorithm. This leads to the development of an improved firefly optimization algorithm for generating impedance parameters, enabling adaptive impedance control of the robotic arm. Simulation results reveal that the adaptive impedance control outperforms both the standard firefly impedance control and traditional impedance control in terms of steady-state error, peak error, and adjustment time. This highlights the superior steady-state performance and dynamic response of adaptive impedance control in robotic arm applications. Experimental results further validate the effectiveness and advantages of adaptive impedance control in manipulator control.