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Peer-reviewedCC BY 4.0Mathematics

Direct Neural Dynamic Programming Adaptive Control of Knee and Ankle Joints in a Simulated Prosthetic Leg Model

  • Jordan K. Kralev1
  • Tsonyo N. Slavov1
  • Asparuh Markovski1
  • Vladislav Slavov2
  1. 1Department of Systems and Control, Technical University of Sofia, 1700 Sofia, Bulgaria
  2. 2Department of Electrical Measurement, Technical University of Sofia, 1700 Sofia, Bulgaria
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Abstract

Effective control of prosthetic legs is essential for restoring natural mobility and enhancing the quality of life for amputees. This paper presents a Direct Neural Dynamic Programming (DNDP) algorithm that leverages coupled action and critic neural networks to solve the optimal control problem for prosthetic gait trajectory tracking. By continuously learning and adapting to individual gait patterns, the DNDP action networks generate the control for the knee and ankle joints to reduce the gait tracking error. The proposed approach enables real-time optimization of control strategies without relying on precise system models, offering adaptability to subject-specific dynamics and varying conditions.

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Cite

BibTeXRIS
BibTeX
@article{kralev2026direct,
  title = {{Direct Neural Dynamic Programming Adaptive Control of Knee and Ankle Joints in a Simulated Prosthetic Leg Model}},
  author = {Jordan K. Kralev and Tsonyo N. Slavov and Asparuh Markovski and Vladislav Slavov},
  journal = {Mathematics},
  year = {2026},
  month = oct,
  doi = {10.3390/math14203769},
  url = {https://www.mdpi.com/2227-7390/14/20/3769},
  note = {CC BY 4.0},
}
RIS
TY  - JOUR
TI  - Direct Neural Dynamic Programming Adaptive Control of Knee and Ankle Joints in a Simulated Prosthetic Leg Model
AU  - Jordan K. Kralev
AU  - Tsonyo N. Slavov
AU  - Asparuh Markovski
AU  - Vladislav Slavov
PY  - 2026
DA  - 2026/10/17
JO  - Mathematics
DO  - 10.3390/math14203769
UR  - https://www.mdpi.com/2227-7390/14/20/3769
AB  - Effective control of prosthetic legs is essential for restoring natural mobility and enhancing the quality of life for amputees. This paper presents a Direct Neural Dynamic Programming (DNDP) algorithm that leverages coupled action and critic neural networks to solve the optimal control problem for prosthetic gait trajectory tracking. By continuously learning and adapting to individual gait patterns, the DNDP action networks generate the control for the knee and ankle joints to reduce the gait tracking error. The proposed approach enables real-time optimization of control strategies without relying on precise system models, offering adaptability to subject-specific dynamics and varying conditions.
SN  - 2227-7390
ER  -