Optimal Control of Blank Holder Force Based on Deep Reinforcement Learning In deep drawing, reasonable control of blank holder force is the key to the quality of finished products. Traditional blank holder force control methods often need to model the highly nonlinear deep drawing process. Because it is difficult to obtain an accurate system dynamics model, the results of traditional methods deviate from the actual situation. In this paper, a blank holder force control model based on the integration of deep reinforcement learning and finite element analysis is proposed. The blank holder force control policy is optimized by combining the perception ability of deep neural network with the decisionmaking ability of reinforcement learning, which avoiding the fitting of system dynamics. Firstly, an algorithm of blank holder force optimization based on deep reinforcement learning is proposed. The deep neural network is used to deal with the large state space. Secondly, by using a new network structure to construct the policy network, the blank holder force policy is divided into global part and local part, which effectively improves the control effect of policy. Experiments show that the proposed control model can effectively optimize blank holder force control policy and improve product quality compared with the traditional deep reinforcement learning algorithm.

Control of a CableDriven Parallel Robot via Deep Reinforcement Learning Deep reinforcement learning (DRL) introduces deep neural networks to solve reinforcement learning problems. DRL is suitable for sequential decisionmaking applications and has been used to solve a variety of robot manipulation tasks recently. This paper applies a DRL algorithm named deep deterministic policy gradient (DDPG) to control a cabledriven parallel robot (CDPR) for the first time. Besides the control of a CDPR, this study investigates the ability of DDPG to learn the optimal tension distribution of the CDPR and the robustness of DDPG to certain model uncertainties of the CDPR. Simulation results show that DDPG can not only control the CDPR but also learn the optimal tension distribution of the CDPR.