The embodiment of the invention provides a dual-motor seamless
variable stiffness control method based on deep
reinforcement learning and related equipment, and belongs to the technical field of
intelligent control. The method comprises the following steps: modeling a dual-
motor variable stiffness control task into a Markov
decision process to obtain a
state space, an action space and a
variable stiffness reward function; performing near-end strategy optimization on the strategy network through the
state space, the action space and the
variable stiffness reward function, so that the strategy network learns and generates a
motor torque instruction for realizing variable stiffness behavior balance according to the environment, and an
intelligent control network is obtained; the current state vectors of the double motors are collected in each
control period, the current state vectors are input into the
intelligent control network for control
decision making, and an optimal
motor torque instruction is obtained; and according to the optimal
motor torque instruction, driving the double motors to carry out variable-stiffness cooperative motion. The embodiment of the invention can get rid of dependence on an accurate
mathematical model, actively and autonomously learn the
optimal control strategy through interaction with the environment, and realize a variable stiffness control behavior.