Control of Parallel Quadruped Robots Based on Adaptive Dynamic Programming Control

With the rapid development of robotics technology, quadruped robots have shown significant potential in navigating complex terrains due to their excellent stability and adaptability. This paper proposes an adaptive dynamic programming control method based on policy iteration, aimed at improving the motion performance and autonomous adaptability of parallel quadruped robots in unknown environments. First, the study establishes a kinematic model of the robot and performs inverse kinematics calculations to determine the angular functions for each joint of the robot's legs. To improve the robot's mobility on challenging terrains, we design an optimal tracking controller based on Generalized Policy Iteration (GPI). This approach reduces the model's dependency on strict requirements and is applied to the control of quadruped robots. Finally, kinematic simulations are conducted based on pre-planned robot gaits. In addition, experiments are then conducted based on the simulation results. The results of simulation experiments indicate that the quadruped robot, under the adaptive optimal control algorithm, can achieve smooth walking on complex terrains, verifying the rationality and effectiveness of the parallel quadruped robot in handling such conditions. The experimental results further demonstrate that this strategy significantly improves the stability and robustness of the robot across various terrains.