Abstract
An adaptive proportional integral robust (PIR) control method based on deep deterministic policy gradient (DDPGPIR) is proposed for n-link robotic manipulator systems with model uncertainty and time-varying external disturbances. In this paper, the uncertainty of the nonlinear dynamic model, time-varying external disturbance, and friction resistance of the n-link robotic manipulator are integrated into the uncertainty of the system, and the adaptive robust term is used to compensate for the uncertainty of the system. In addition, dynamic information of the n-link robotic manipulator is used as the input of the DDPG agent to search for the optimal parameters of the proportional integral robust controller in continuous action space. To ensure the DDPG agent’s stable and efficient learning, a reward function combining a Gaussian function and the Euclidean distance is designed. Finally, taking a two-link robot as an example, the simulation experiments of DDPGPIR and other control methods are compared. The results show that DDPGPIR has better adaptive ability, robustness, and higher trajectory tracking accuracy.
Highlights
To achieve a better control effect and meet the control requirements of different fields, the manipulator must have the ability to track a trajectory with high precision
When designing a control strategy, good adaptability and high-precision trajectory tracking abilities are necessary for the uncertainty of the n-link robotic manipulator system
Considering the uncertainty and time-varying disturbance of the dynamic model of the n-link robot manipulator system and the influence of friction resistance, the adaptive robust term is used to compensate for the uncertainty of the system
Summary
A robotic manipulator is similar to the human arm and can replace or assist humans to complete the tasks of picking, placing, painting, welding, and assembling. The manipulator plays an important role in industrial production, underwater exploration, medical application, aerospace, and other fields [1–4]. To achieve a better control effect and meet the control requirements of different fields, the manipulator must have the ability to track a trajectory with high precision. Due to the highly nonlinear, dynamic characteristics of a robotic manipulator, and the influence of joint friction and time-varying external interference in practical applications, it is difficult to obtain accurate information about model parameters. When designing a control strategy, good adaptability and high-precision trajectory tracking abilities are necessary for the uncertainty of the n-link robotic manipulator system
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