A New Powered Lower Limb Prosthesis Control Framework Based on Adaptive Dynamic Programming

Yue Wen, Jennie Si, Xiang Gao, Stephanie Huang, He Huang

Research output: Contribution to journalArticlepeer-review

67 Scopus citations


This brief presents a novel application of adaptive dynamic programming (ADP) for optimal adaptive control of powered lower limb prostheses, a type of wearable robots to assist the motor function of the limb amputees. Current control of these robotic devices typically relies on finite state impedance control (FS-IC), which lacks adaptability to the user's physical condition. As a result, joint impedance settings are often customized manually and heuristically in clinics, which greatly hinder the wide use of these advanced medical devices. This simulation study aimed at demonstrating the feasibility of ADP for automatic tuning of the twelve knee joint impedance parameters during a complete gait cycle to achieve balanced walking. Given that the accurate models of human walking dynamics are difficult to obtain, the model-free ADP control algorithms were considered. First, direct heuristic dynamic programming (dHDP) was applied to the control problem, and its performance was evaluated on OpenSim, an often-used dynamic walking simulator. For the comparison purposes, we selected another established ADP algorithm, the neural fitted Q with continuous action (NFQCA). In both cases, the ADP controllers learned to control the right knee joint and achieved balanced walking, but dHDP outperformed NFQCA in this application during a 200 gait cycle-based testing.

Original languageEnglish (US)
Article number7508991
Pages (from-to)2215-2220
Number of pages6
JournalIEEE Transactions on Neural Networks and Learning Systems
Issue number9
StatePublished - Sep 2017


  • Adaptive control
  • adaptive dynamic programming (ADP)
  • optimal control
  • prostheses

ASJC Scopus subject areas

  • Software
  • Computer Science Applications
  • Computer Networks and Communications
  • Artificial Intelligence


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