This paper proposes virtual impedance adaptation of the lower-limb exoskeleton for human performance augmentation(LEHPA) based on deep reinforcement learning(VIADRL) to mitigate reliance on model accuracy and address ...This paper proposes virtual impedance adaptation of the lower-limb exoskeleton for human performance augmentation(LEHPA) based on deep reinforcement learning(VIADRL) to mitigate reliance on model accuracy and address the ever-changing human-exoskeleton interaction(HEI) dynamics. The classical sensitivity amplification control strategy is expanded to the virtual impedance control strategy with more learnable virtual impedance parameters. The adjustment of these virtual impedance parameters is formalized as finding the optimal policy for a Markov Decision Process and can then be effectively resolved using deep reinforcement learning algorithms. To ensure safe and efficient policy training, a multibody simulation environment is established to facilitate the training process, supplemented by the innovative hybrid inverse-forward dynamics simulation approach for executing the simulation. For comparison purposes, the SADRL strategy is introduced as a benchmark. A novel control performance evaluation method based on the HEI forces at the back, thighs, and shanks is proposed to quantitatively evaluate the performance of our proposed VIADRL strategy. The VIADRL controller is systematically compared with the SADRL controller at five selected walking speeds. The lumped ratio of HEI forces under the SADRL strategy relative to those under the SADRL strategy is as low as 0.81 in simulation and approximately 0.89 on the LEHPA prototype. The overall reduction of HEI forces demonstrates the superiority of the VIADRL strategy in comparison to the SADRL strategy.展开更多
During the hot rolling process,the performance of most control systems gradually degrades due to equipment aging and changing process conditions.However,existing gauge-looper-tension control method remain restricted b...During the hot rolling process,the performance of most control systems gradually degrades due to equipment aging and changing process conditions.However,existing gauge-looper-tension control method remain restricted by a lack of intelligent parameter maintenance strategies.To address this challenge and enhance the smart manufacturing capabilities of strip hot rolling,based on the digital twin method,this paper proposes a data-driven optimized control method for the gauge-looper-tension system of strip hot rolling.First,a hot rolling process model is constructed based on a digital twin method to serve as an evaluation and optimization platform.Subsequently,relevant data are collected to calculate the Hurst index for identifying the performance of the controller during the rolling process.Additionally,for controllers with poor Hurst index values,the crayfish optimization algorithm is employed for adjusting controller parameters to maximize the Hurst index.Experimental results demonstrate that the evaluation method effectively recognized the control state of gauge-looper-tension system and the optimization method successfully enhances the performance of the control system.Therefore,based on the digital twin platform,the proposed method can effectively maintain performance-degraded control systems.展开更多
文摘This paper proposes virtual impedance adaptation of the lower-limb exoskeleton for human performance augmentation(LEHPA) based on deep reinforcement learning(VIADRL) to mitigate reliance on model accuracy and address the ever-changing human-exoskeleton interaction(HEI) dynamics. The classical sensitivity amplification control strategy is expanded to the virtual impedance control strategy with more learnable virtual impedance parameters. The adjustment of these virtual impedance parameters is formalized as finding the optimal policy for a Markov Decision Process and can then be effectively resolved using deep reinforcement learning algorithms. To ensure safe and efficient policy training, a multibody simulation environment is established to facilitate the training process, supplemented by the innovative hybrid inverse-forward dynamics simulation approach for executing the simulation. For comparison purposes, the SADRL strategy is introduced as a benchmark. A novel control performance evaluation method based on the HEI forces at the back, thighs, and shanks is proposed to quantitatively evaluate the performance of our proposed VIADRL strategy. The VIADRL controller is systematically compared with the SADRL controller at five selected walking speeds. The lumped ratio of HEI forces under the SADRL strategy relative to those under the SADRL strategy is as low as 0.81 in simulation and approximately 0.89 on the LEHPA prototype. The overall reduction of HEI forces demonstrates the superiority of the VIADRL strategy in comparison to the SADRL strategy.
基金supported by the National Natural Science Foundation of China[grant numbers U21A20117,52074085]the Fundamental Research Funds for the Central Universities[grant number N2004010]the LiaoNing Applied Basic Research Program Project[grant numbers 2022JH24/10200013].
文摘During the hot rolling process,the performance of most control systems gradually degrades due to equipment aging and changing process conditions.However,existing gauge-looper-tension control method remain restricted by a lack of intelligent parameter maintenance strategies.To address this challenge and enhance the smart manufacturing capabilities of strip hot rolling,based on the digital twin method,this paper proposes a data-driven optimized control method for the gauge-looper-tension system of strip hot rolling.First,a hot rolling process model is constructed based on a digital twin method to serve as an evaluation and optimization platform.Subsequently,relevant data are collected to calculate the Hurst index for identifying the performance of the controller during the rolling process.Additionally,for controllers with poor Hurst index values,the crayfish optimization algorithm is employed for adjusting controller parameters to maximize the Hurst index.Experimental results demonstrate that the evaluation method effectively recognized the control state of gauge-looper-tension system and the optimization method successfully enhances the performance of the control system.Therefore,based on the digital twin platform,the proposed method can effectively maintain performance-degraded control systems.
