Online Optimization Method of Controller Parameters for Robot Constant Force Grinding Based on Deep Reinforcement Learning Rainbow

Yuan, Chao; Zou, Yanbiao

doi:10.1007/s10846-022-01688-z

Cited by 9 publications

(5 citation statements)

References 27 publications

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“…Tie Zhang et al proposed an online optimization method for constant force grinding controller parameters based on deep reinforcement learning rainbow. The technique can optimize the control parameters online while converging stably to solve the constant force control problem in the grinding process [16]. Tie Zhang et al proposed a reinforcement learning-based machine manpower control algorithm to solve the problem that the contact force of the robot end-effector is challenging to keep constant when the robot is tracking an unknown curved workpiece [17].…”

Section: Introductionmentioning

confidence: 99%

Constant Force Grinding Controller for Robots Based on Soft-Actor-Critic Optimal Parameter Finding Algorithm

Rei,

Wang,

Chen

et al. 2024

Preprint

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Since conventional PID (Proportional-Integral-Derivative) controllers hardly control the robot to stabilize for constant force grinding under changing environmental conditions, it is necessary to add a compensation term to conventional PID controllers. An optimal parameter finding algorithm based on SAC(Soft-Actor-Critic) is proposed to solve the problem that the compensation term parameters are difficult to obtain, including training state action and normalization preprocessing, reward function design, and targeted deep neural network design. The algorithm is used to find the optimal controller compensation term parameters and applied to the PID controller to complete the compensation through the inverse kinematics of the robot to achieve constant force grinding control. To verify the algorithm's feasibility, a simulation model of a grinding robot with sensible force information is established, and the simulation results show that the controller trained with the algorithm can achieve constant force grinding of the robot. Finally, the robot constant force grinding experimental system platform is built for testing, which verifies the control effect of the optimal parameter finding algorithm on the robot constant force grinding and has specific environmental adaptability.

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Section: Introductionmentioning

confidence: 99%

Constant Force Grinding Controller for Robots Based on Soft-Actor-Critic Optimal Parameter Finding Algorithm

Rei,

Wang,

Chen

et al. 2024

Preprint

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“…Xu et al [ 15 ] proposed the combination of an active strategy, based on PI/PD control, and a passive strategy, based on PID control, which was shown to improve the accuracy and efficiency of the controlled force and avoid over- and under-cutting. Zhang et al [ 16 ] presented a method for optimizing the parameters of a robot PD constant grinding force controller using deep reinforcement learning DRL Rainbow, which can ensure that a constant force is applied during grinding by adjusting the grinding depth. Further, some studies have been carried out on the normal vector of the surface; in establishing the positioning of the grinding tool during processing, Zhao et al [ 17 ] proposed an adaptive PD constant force controller and a normal vector search algorithm, which together ensure that constant force is applied during the grinding of workpieces with unknown shapes.…”

Section: Introductionmentioning

confidence: 99%

Research on Surface Tracking and Constant Force Control of a Grinding Robot

Shi

Yang

et al. 2023

Sensors

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To improve the quality and efficiency of robot grinding, a design and a control algorithm for a robot used for grinding the surfaces of large, curved workpieces with unknown parameters, such as wind turbine blades, are proposed herein. Firstly, the structure and motion mode of the grinding robot are determined. Secondly, in order to solve the problem of complexity and poor adaptability of the algorithm in the grinding process, a force/position hybrid control strategy based on fuzzy PID is proposed which greatly improves the response speed and reduces the error of the static control strategy. Compared with normal PID, fuzzy PID has the advantages of variable parameters and strong adaptability; the hydraulic cylinder used to adjust the angle of the manipulator can control the speed offset within 0.27 rad/s, and the grinding process can be carried out directly without obtaining the specific model of the surface to be machined. Finally, the experiments are carried out, the grinding force and feed speed are maintained within the allowable error range of the expected value, and the results verify the feasibility and effectiveness of the position tracking and constant force control strategy in this paper. The surface roughness of the blade is maintained within Ra = 2~3 μm after grinding, which proves that the grinding quality meets the requirements of the best surface roughness required for the subsequent process.

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“…Grinding robots (GR), characterized by flexibility, intelligence, and low cost, can effectively improve the surface quality of workpieces when combined with professional grinding tools for machining. They are widely applied in the manufacturing process of mechanical parts [1]. The intelligence of GR is highlighted by their strong compatibility with artificial intelligence methods, making them ideally suited for use in machining processes with complex process features.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, researchers have focused on improving the grinding quality by enhancing the control performance of GR and increasing the grinding force control accuracy [3,4]. In fact, the grinding force is also affected by grinding depth, grinding speed and tool feed rate [1]. The grinding quality not only depends on the grinding force but also is affected by variant process parameters such as contact stress, spindle speed and feed speed [5].…”

Section: Introductionmentioning

confidence: 99%

Multi-Objective Optimization of the Process Parameters of a Grinding Robot Using LSTM-MLP-NSGAII

Li,

Wang,

Yan

2023

Machines

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Grinding robots are widely used in the automotive, mechanical processing, aerospace industries, among others, due to their strong adaptability, high safety and intelligence. The grinding process parameters are the main factors that affect the quality and efficiency of grinding robots. However, it is difficult to obtain the optimal combination of the grinding process parameters only by manual experience. This study proposes an artificial intelligence-based method for optimizing the process parameters of a grinding robot using neural networks and a genetic algorithm, with the aim to reduce the workpiece surface roughness and shorten the grinding time. Specifically, this is the first study utilizing a multi-objective optimization approach to optimize the process parameters of a grinding robot. Based on the experimental data of the grinding robot ROKAE XB7, the long short-term memory (LSTM) and multilayer perceptron (MLP) neural networks were trained to fit the quantitative relationships between the process parameters of the grinding robot, such as feed rate, spindle pressure and pneumatic motor pressure, and the result of grinding surface roughness and grinding time. After that, the non-dominated sorting genetic algorithm II (NSGA-II) was used to calculate the Pareto optimal process parameter combinations using the trained LSTM and MPL model as the objective function. Compared with the method based on manual experience, the process parameters optimized with this method achieved a reduction in surface roughness of at least 13.62% and a reduction in the whole grinding process time of 28%. The excellent grinding results obtained for grinding time and surface roughness validated the feasibility and efficiency of the proposed multi-objective method for the optimization of grinding robots’ process parameters in practical manufacturing applications.

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Online Optimization Method of Controller Parameters for Robot Constant Force Grinding Based on Deep Reinforcement Learning Rainbow

Cited by 9 publications

References 27 publications

Constant Force Grinding Controller for Robots Based on Soft-Actor-Critic Optimal Parameter Finding Algorithm

Constant Force Grinding Controller for Robots Based on Soft-Actor-Critic Optimal Parameter Finding Algorithm

Research on Surface Tracking and Constant Force Control of a Grinding Robot

Multi-Objective Optimization of the Process Parameters of a Grinding Robot Using LSTM-MLP-NSGAII

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