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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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Direct heuristic dynamic programming based on an improved PID neural network

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控制理论与应用（英文版） 2012年第4期10卷 497-503页

作者： Jian SUN Feng LIU jennie si Shengwei MEI Department of Electrical Engineering Tsinghua University Department of Electrical Engineering Arizona State University

In this paper, an improved PID-neural network （IPIDNN） structure is proposed and applied to the critic and action networks of direct heuristic dynamic programming （DHDP）. As one of online learning algorithm of approximate dynamic programming （ADP）, DHDP has demonstrated its applicability to large state and control problems. Theoretically, the DHDP algorithm requires access to full state feedback in order to obtain solutions to the Bellman optimality equation. Unfortunately, it is not always possible to access all the states in a real system. This paper proposes a solution by suggesting an IPIDNN configuration to construct the critic and action networks to achieve an output feedback control. since this structure can estimate the integrals and derivatives of measurable outputs, more system states are utilized and thus better control performance are expected. Compared with traditional PIDNN, this configuration is flexible and easy to expand. Based on this structure, a gradient decent algorithm for this IPIDNN-based DHDP is presented. Convergence issues are addressed within a single learning time step and for the entire learning process. Some important insights are provided to guide the implementation of the algorithm. The proposed learning controller has been applied to a cart-pole system to validate the effectiveness of the structure and the algorithm.

关键词： Approximate dynamic programming (ADP) Direct heuristic dynamic programming (DHDP) ImprovedPID neural network (IPIDNN)

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一种改进的近似动态规划方法及其在SVC的应用

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电机与控制学报 2011年第5期15卷 95-102页

作者：孙健刘锋 si jennie 郭文涛梅生伟清华大学电力系统国家重点实验室亚利桑那州立大学电机系

近似动态规划的基本思想是通过近似计算代价函数,从而避免动态规划中的"维数灾"问题。随机选取初值使得近似动态规划方法需要多次的学习才能最终收敛,极大地限制了在实际系统中的应用。针对上述问题,提出一种基于改进PID神经网络的直接... 详细信息

近似动态规划的基本思想是通过近似计算代价函数,从而避免动态规划中的"维数灾"问题。随机选取初值使得近似动态规划方法需要多次的学习才能最终收敛,极大地限制了在实际系统中的应用。针对上述问题,提出一种基于改进PID神经网络的直接启发式动态规划算法,将初始执行网络与PID控制器之间建立起一种等价关系,因此可以利用已经设计好的PID控制器来指导其初值选取,从而使算法收敛性大大提高。改进的神经网络与常规PID神经网络相比,结构简单且具有更好的扩展性,性能上具有更强的鲁棒性。对4机2区系统的静止无功补偿器附加阻尼控制进行仿真测试,仿真结果表明基于改进PID神经网络的直接启发式动态规划算法和初值选取方法的有效性,并且在部分状态反馈和延时两种情况下有着很好的控制效果。

关键词：近似动态规划直接启发式动态规划改进PID神经网络静止无功补偿器附加阻尼控制

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Online and Model-Free Supplementary Learning Control Based on Approximate Dynamic Programming

Online and Model-Free Supplementary Learning Control Based o...

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第26届中国控制与决策会议

作者： Wentao Guo Feng Liu jennie si Shengwei Mei State Key Laboratory of Power Systems Department of Electrical EngineeringTsinghua University Department of Electrical Engineering Arizona State University

An approximate dynamic programming（ADP） based supplementary learning control method is developed to online improve the performance of existing controllers. The proposed supplementary learning structure can make full use of the prior knowledge of the pre-designed controller and endow the controller with learning ability. Moreover, by introducing the action dependent value function for policy evaluation, the supplementary learning control can work in a model-free manner. The policy iteration algorithm is employed to train the actor-critic structure of the ADP supplementary controller. simulation studies are carried out on the cart-pole system to validate the optimization and the adaptation capability of the proposed methodology.

关键词： Approximate Dynamic Programming Model-Free Online Supplementary Control

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