The design of stimuli‐induced equilibrium point and its assessment in motion planning of a heavy‐load servo system

Abstract To address the issues of power saturation and high energy consumption in heavy‐load servo systems, we propose a motion planning algorithm called the stimuli‐induced equilibrium point motion planning (SIEP‐MP) algorithm. We first explore the correlation between the bionic eye system and heavy‐load servo systems using head‐eye motion control theory and then derive the core formula of the SIEP‐MP algorithm from psychological field theory. Next, phase plane analysis is used for qualitative analysis of the influence of the state variable coefficient in the SIEP‐MP algorithm on motion planning performance. We also propose a piecewise linearization method to quantitatively analyze the relationship between SIEP‐MP, linearized SIEP‐MP, and low‐pass filtering algorithm. Using an experimental platform, target trajectories, and control strategies, we test the low‐pass filtering, range‐limiting, power saturation elimination, and energy‐saving capabilities of SIEP‐MP. In comparison to two similar motion planning algorithms, the experimental results demonstrate the advantages of the SIEP‐MP algorithm. The proposed SIEP‐MP algorithm can ensure optimal tracking performance of heavy‐load servo systems in different modes through mode switching.