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Robotic Knee Tracking control to Mimic the Intact Human Knee Profile Based on Actor-Critic reinforcement learning

IEEE/CAA Journal of Automatica Sinica 2022年第1期9卷 19-30页

作者： Ruofan Wu Zhikai Yao Jennie Si He(Helen)Huang School of Electrical Computer and Energy EngineeringArizona State UniversityTempeAZ 85287 USA Department of Biomedical Engineering North Carolina State UniversityRaleighNC 27695 USA University of North Carolina at Chapel Hill Chapel HillNC 27599 USA

We address a state-of-the-art reinforcement learning(RL)control approach to automatically configure robotic pros-thesis impedance parameters to enable end-to-end,continuous locomotion intended for transfemoral amputee ***,our actor-critic based RL provides tracking control of a robotic knee prosthesis to mimic the intact knee *** is a significant advance from our previous RL based automatic tuning of prosthesis control parameters which have centered on regulation control with a designer prescribed robotic knee profile as the *** addition to presenting the tracking control algorithm based on direct heuristic dynamic programming(dHDP),we provide a control performance guarantee including the case of constrained *** show that our proposed tracking control possesses several important properties,such as weight convergence of the learning networks,Bellman(sub)optimality of the cost-to-go value function and control input,and practical stability of the human-robot *** further provide a systematic simulation of the proposed tracking control using a realistic human-robot system simulator,the OpenSim,to emulate how the dHDP enables level ground walking,walking on different terrains and at different *** results show that our proposed dHDP based tracking control is not only theoretically suitable,but also practically useful.

关键词： Automatic tracking of intact knee configuration of robotic knee prosthesis direct heuristic dynamic programming(dHDP) reinforcement learning control

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Design of Deep reinforcement learning controller Through Data-assisted Model for Robotic Fish Speed Tracking

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Journal of Bionic Engineering 2023年第3期20卷 953-966页

作者： Palmani Duraisamy Manigandan Nagarajan Santhanakrishnan Amirtharajan Rengarajan Robotics Lab School of EEESASTRA Deemed UniversityThanjavurTamil Nadu613401India Department of ECE School of EEESASTRA Deemed UniversityThanjavurTamil Nadu613401India

It is common for robotic fish to generate thrust using reactive force generated by the tail’s physical motion, which interacts with the surrounding fluid. The coupling effect of the body strongly correlates with this thrust. However, hydrodynamics cannot be wholly modeled in analytical form. Therefore, data-assisted modeling is necessary for robotic fish. This work presents the first method of its kind using Genetic Algorithm (GA)-based optimization methods for data-assistive modeling for robotic fish applications. To begin, experimental data are collected in real time with the robotic fish that has been designed and fabricated using 3D printing. Then, the model’s influential parameters are estimated using an optimization problem. Further, a model-based deep reinforcement learning (DRL) controller is proposed to track the desired speed through extensive simulation work. In addition to a deep deterministic policy gradient (DDPG), a twin delayed DDPG (TD3) is employed in the training of the RL agent. Unfortunately, due to its local optimization problem, the RL-DDPG controller failed to perform well during training. In contrast, the RL-TD3 controller effectively learns the control policies and overcomes the local optima problem. As a final step, controller performance is evaluated under different disturbance conditions. In contrast to DDPG and GA-tuned proportional-integral controllers, the proposed model with RL-TD3 controller significantly improves the performance.

关键词： Bioinspired robot Data-assisted model Genetic algorithm reinforcement learning control Robotic fish

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Differential pressure reset strategy based on reinforcement learning for chilled water systems

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Building Simulation 2022年第2期15卷 233-248页

作者： Xinfang Zhang Zhenhai Li Zhengwei Li Shunian Qiu Hai Wang School of Mechanical Engineering Tongji UniversityShanghaiChina Key Laboratory of Performance Evolution and control for Engineering Structures of Ministry of Education Tongji UniversityShanghaiChina

Air conditioning water systems account for a large proportion of building energy *** a pressure-controlled water system,one of the key measures to save energy is to adjust the differential pressure setpoints during ***,such adjustments are based either on certain rules,which rely on operator experience,or on complicated models that are not easy to *** this paper,a data-driven control method based on reinforcement learning is *** main idea is to construct an agent model that adapts to the researched *** of directly being told how to react,the agent must rely on its own experiences to *** with traditional control strategies,reinforcement learning control(RLC)exhibits more accurate and steady performances while maintaining indoor air temperature within a limited range.A case study shows that the RLC strategy is able to save substantial amounts of energy.

关键词： water system differential pressure reset reinforcement learning control energy saving

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