Abstract: Aiming at the difficulty of manipulator to realize high-precision motion tracking in complex and harsh environment, a strategy method based on the combination of adaptive dynamic programming (ADP) and sliding mode admittance control is proposed. The unknown environment is modeled as a linear model and based on quasi, a sliding mode admittance controller is derived to resist disturbance interference. An optimal control method that combines ADP with sliding mode admittance controller is proposed, in which the definition of R-matrix in value function is optimized and improved to further improve the tracking accuracy. The neural network based on ADP is used to approximate the solution of optimal value function which improves the rate of convergence and quickly obtains the approximate optimal strategy of Hamilton Jacobi-Behrmann equation (HJB). The method is applied in the trajectory tracking control of a robotic arm. The experimental results show that the method effectively reduces the cost of controlling robotic arm and ensures the optimal trajectory tracking.
Li, Ming; Xu, Qun; Wang, Yan; and Ji, Zhicheng
"Simulation and Research of Manipulator Motion Strategy Based on Adaptive Dynamic Programming,"
Journal of System Simulation: Vol. 35:
10, Article 10.
Available at: https://dc-china-simulation.researchcommons.org/journal/vol35/iss10/10
Li Ming, Xu Qun, Wang Yan, et al. Simulation and Research of Manipulator Motion Strategy Based on Adaptive Dynamic Programming[J]. Journal of System Simulation, 2023, 35(10): 2182-2192.
Artificial Intelligence and Robotics Commons, Computer Engineering Commons, Numerical Analysis and Scientific Computing Commons, Operations Research, Systems Engineering and Industrial Engineering Commons, Systems Science Commons