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Patent · US8864846B2 · B2 · US

Model-based neuromechanical controller for a robotic leg

(11) Publication number
US8864846B2
(21) Application number
US-69812810-A
(22) Filing date
2010-02-01
(30) Priority date
2005-03-31
(43) Publication date
2014-10-21
(45) Date of grant
2014-10-21
(51) IPC
A61F 2/66; A61F 2/68; A61F 2/70; A61F 2/72; A61F 2/76; A61F 5/01; G05B 21/00
(52) CPC
  • A61F Filters implantable into blood vessels; prostheses; devices providing patency to, or preventing collapsing of, tubular structures of the body, e.g. stents; orthopaedic, nursing or contraceptive devices; fomentation; treatment or protection of eyes or ears; bandages, dressings or absorbent pads; first-aid kits: 2/70, 2/605, 2/64, 2/66, 2/68, 2/74, 2/741, 2002/701, 2002/704, 2002/705, 2002/7625, 2002/7635, 2002/764, 2002/7645, 2005/0155, 5/0123, 5/0127
  • B25J Manipulators; chambers provided with manipulation devices: 9/0006, 9/1075, 9/1633
  • G16H Healthcare informatics, i.e. information and communication technology [ICT] specially adapted for the handling or processing of medical or healthcare data: 50/50
(73) Assignee
HERR HUGH M; GEYER HARTMUT; EILENBERG MICHAEL FREDERICK; MASSACHUSETTS INST TECHNOLOGY
(72) Inventors
HERR HUGH M; GEYER HARTMUT; EILENBERG MICHAEL FREDERICK
(54) Title
Model-based neuromechanical controller for a robotic leg
(57) Abstract

A model-based neuromechanical controller for a robotic limb having at least one joint includes a finite state machine configured to receive feedback data relating to the state of the robotic limb and to determine the state of the robotic limb, a muscle model processor configured to receive state information from the finite state machine and, using muscle tendon lever arm and muscle tendon length equations and reflex control equations in a neuromuscular model, to determine at least one desired joint torque or stiffness command to be sent to the robotic limb, and a joint command processor configured to command the biomimetic torques and stiffnesses determined by the muscle model processor at the robotic limb joint. The feedback data is preferably provided by at least one sensor mounted at each joint of the robotic limb. In a preferred embodiment, the robotic limb is a leg and the finite state machine is synchronized to the leg gait cycle.

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Claims (18)

  1. A model-based neuromechanical controller for controlling at least one robotic limb joint of a robotic limb, the controller comprising: a) a neuromuscular model including a muscle model, muscle tendon lever arm and muscle tendon length equations and reflex control equations, the neuromuscular model being configured to receive feedback data relating to a measured state of the robotic limb and, using the feedback data, and the muscle model, muscle tendon lever arm and muscle tendon length equations and reflex control equations of the neuromuscular model, to determine at least one torque command; and b) a torque control system in communication with the neuromuscular model, whereby the torque control system receives the at least one torque command from the neuromuscular model for controlling the robotic limb joint.
  2. The controller of claim 1, wherein the robotic limb includes a sensor mounted to the robotic limb, and the feedback data is provided by the at least one sensor mounted at the robotic limb.
  3. The controller of claim 2, wherein the neuromuscular model and the torque control system are configured to control the robotic limb, wherein the robotic limb is a leg, and wherein the neuromechanical controller further includes a finite state machine synchronized to the leg gait cycle, the finite state machine being configured to receive the feedback data from the at least one sensor and to determine a gait phase of the robotic leg using the feedback data received.
  4. The controller of claim 3, wherein the neuromuscular model and the torque control system are configured to control a robotic leg comprising an ankle joint.
  5. The controller of claim 3, wherein the neuromuscular model and the torque control system are configured to control a robotic leg comprising a knee joint.
  6. The controller of claim 4, the robotic leg further comprising a knee joint.
  7. The controller of claim 6, the robotic leg further comprising a hip joint.
  8. The controller of claim 2, wherein at least one sensor is an angular joint displacement and velocity sensor, a torque sensor, or an inertial measurement unit.
  9. The neuromechanical controller of claim 2, wherein the feedback data includes joint angle and joint angular velocity measured by the at least one sensor.
  10. The neuromechanical controller of claim 9, wherein the muscle tendon lever arm and muscle tendon length equations are configured to determine a muscle moment arm and a muscle tendon length using the measured joint angle.
  11. The neuromechanical controller of claim 10, wherein the muscle model determines muscle force using the muscle tendon length and a stimulation input.
  12. The neuromechanical controller of claim 11, wherein the muscle model comprises a contractile element and a series-elastic element arranged in a muscle tendon unit.
  13. The neuromechanical controller of claim 12, wherein the reflex control equations are configured in a local feedback loop, and the reflex control equations are configured to receive muscle force feedback from the muscle model and to provide the stimulation input to the muscle model.
  14. The neuromechanical controller of claim 13, wherein the muscle force feedback is positive force feedback.
  15. The neuromechanical controller of claim 13, wherein the reflex control equations are configured to mimic the stretch reflex of an intact human muscle.
  16. The neuromechanical controller of claim 1, wherein the torque control system includes a feed forward gain, a lead compensator and a friction compensator to adapt the torque command and thereby obtain the current command.
  17. The neuromechanical controller of claim 16, wherein the torque control system further includes a motor controller for driving an actuator of the robotic limb joint with the at least one current command.
  18. The neuromechanical controller of claim 17, wherein the torque control system further includes a parallel spring model.

Description

The present invention relates to control of artificial joints and limbs for use in prosthetic, orthotic, exoskeletal, or robotic devices and, in particular, to control methodology for a robotic leg based on a neuromuscular model of locomotion.

Legged locomotion of animals and humans is controlled by a complex network of neurons. Proposed in the early 20th century [Brown, T. G., 1914. On the nature of the fundamental activity of the nervous centres; together with an analysis of the conditioning of rhythmic activity in progression, and a theory of the evolution of function in the nervous system. J Physiol 48 (1), 18-46.]. and firmly established today [Orlovsky, G., Deliagina, T., Grillner, S., 1999. Neuronal control of locomotion: from mollusc to man. Oxford University Press, New York], the central pattern generator (CPG) forms the basis of this network. In the current view, the CPG consists of layers of neuron pools in the spinal cord [Rybak, I. A., Shevtsova, N. A., Lafreniere-Roula, M., McCrea, D. A., 2006. Modelling spinal circuitry involved in locomotor pattern generation: insights from deletions during fictive locomotion. J Physiol 577 (Pt 2), 617-639] which, through other neuron pools channeling muscle synergies, provide rhythmic activity to the leg extensor and flexor muscles [Dietz, V., 2003. Spinal cord pattern generators for locomotion. Clin Neurophysiol 114 (8), 1379-1389; Minassian, K., Persy, I., Rattay, F., Pinter, M. M., Kern, H., Dimitrijevic, M. R., 2007. Human lumbar cord circuitries can be activated by extrinsic tonic input to generate locomotor-like activity.

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Record as JSON 
{
  "publication_number": "US8864846B2",
  "country": "US",
  "kind": "B2",
  "title": "Model-based neuromechanical controller for a robotic leg",
  "abstract": "A model-based neuromechanical controller for a robotic limb having at least one joint includes a finite state machine configured to receive feedback data relating to the state of the robotic limb and to determine the state of the robotic limb, a muscle model processor configured to receive state information from the finite state machine and, using muscle tendon lever arm and muscle tendon length equations and reflex control equations in a neuromuscular model, to determine at least one desired joint torque or stiffness command to be sent to the robotic limb, and a joint command processor configured to command the biomimetic torques and stiffnesses determined by the muscle model processor at the robotic limb joint. The feedback data is preferably provided by at least one sensor mounted at each joint of the robotic limb. In a preferred embodiment, the robotic limb is a leg and the finite state machine is synchronized to the leg gait cycle.",
  "claims": [
    "1. A model-based neuromechanical controller for controlling at least one robotic limb joint of a robotic limb, the controller comprising: a) a neuromuscular model including a muscle model, muscle tendon lever arm and muscle tendon length equations and reflex control equations, the neuromuscular model being configured to receive feedback data relating to a measured state of the robotic limb and, using the feedback data, and the muscle model, muscle tendon lever arm and muscle tendon length equations and reflex control equations of the neuromuscular model, to determine at least one torque command; and b) a torque control system in communication with the neuromuscular model, whereby the torque control system receives the at least one torque command from the neuromuscular model for controlling the robotic limb joint.",
    "2. The controller of claim 1, wherein the robotic limb includes a sensor mounted to the robotic limb, and the feedback data is provided by the at least one sensor mounted at the robotic limb.",
    "3. The controller of claim 2, wherein the neuromuscular model and the torque control system are configured to control the robotic limb, wherein the robotic limb is a leg, and wherein the neuromechanical controller further includes a finite state machine synchronized to the leg gait cycle, the finite state machine being configured to receive the feedback data from the at least one sensor and to determine a gait phase of the robotic leg using the feedback data received.",
    "4. The controller of claim 3, wherein the neuromuscular model and the torque control system are configured to control a robotic leg comprising an ankle joint.",
    "5. The controller of claim 3, wherein the neuromuscular model and the torque control system are configured to control a robotic leg comprising a knee joint.",
    "6. The controller of claim 4, the robotic leg further comprising a knee joint.",
    "7. The controller of claim 6, the robotic leg further comprising a hip joint.",
    "8. The controller of claim 2, wherein at least one sensor is an angular joint displacement and velocity sensor, a torque sensor, or an inertial measurement unit.",
    "9. The neuromechanical controller of claim 2, wherein the feedback data includes joint angle and joint angular velocity measured by the at least one sensor.",
    "10. The neuromechanical controller of claim 9, wherein the muscle tendon lever arm and muscle tendon length equations are configured to determine a muscle moment arm and a muscle tendon length using the measured joint angle.",
    "11. The neuromechanical controller of claim 10, wherein the muscle model determines muscle force using the muscle tendon length and a stimulation input.",
    "12. The neuromechanical controller of claim 11, wherein the muscle model comprises a contractile element and a series-elastic element arranged in a muscle tendon unit.",
    "13. The neuromechanical controller of claim 12, wherein the reflex control equations are configured in a local feedback loop, and the reflex control equations are configured to receive muscle force feedback from the muscle model and to provide the stimulation input to the muscle model.",
    "14. The neuromechanical controller of claim 13, wherein the muscle force feedback is positive force feedback.",
    "15. The neuromechanical controller of claim 13, wherein the reflex control equations are configured to mimic the stretch reflex of an intact human muscle.",
    "16. The neuromechanical controller of claim 1, wherein the torque control system includes a feed forward gain, a lead compensator and a friction compensator to adapt the torque command and thereby obtain the current command.",
    "17. The neuromechanical controller of claim 16, wherein the torque control system further includes a motor controller for driving an actuator of the robotic limb joint with the at least one current command.",
    "18. The neuromechanical controller of claim 17, wherein the torque control system further includes a parallel spring model."
  ],
  "description_excerpt": "The present invention relates to control of artificial joints and limbs for use in prosthetic, orthotic, exoskeletal, or robotic devices and, in particular, to control methodology for a robotic leg based on a neuromuscular model of locomotion.\n\nLegged locomotion of animals and humans is controlled by a complex network of neurons. Proposed in the early 20th century [Brown, T. G., 1914. On the nature of the fundamental activity of the nervous centres; together with an analysis of the conditioning of rhythmic activity in progression, and a theory of the evolution of function in the nervous system. J Physiol 48 (1), 18-46.]. and firmly established today [Orlovsky, G., Deliagina, T., Grillner, S., 1999. Neuronal control of locomotion: from mollusc to man. Oxford University Press, New York], the central pattern generator (CPG) forms the basis of this network. In the current view, the CPG consists of layers of neuron pools in the spinal cord [Rybak, I. A., Shevtsova, N. A., Lafreniere-Roula, M., McCrea, D. A., 2006. Modelling spinal circuitry involved in locomotor pattern generation: insights from deletions during fictive locomotion. J Physiol 577 (Pt 2), 617-639] which, through other neuron pools channeling muscle synergies, provide rhythmic activity to the leg extensor and flexor muscles [Dietz, V., 2003. Spinal cord pattern generators for locomotion. Clin Neurophysiol 114 (8), 1379-1389; Minassian, K., Persy, I., Rattay, F., Pinter, M. M., Kern, H., Dimitrijevic, M. R., 2007. Human lumbar cord circuitries can be activated by extrinsic tonic input to generate locomotor-like activity.",
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  ],
  "assignees": [
    "HERR HUGH M",
    "GEYER HARTMUT",
    "EILENBERG MICHAEL FREDERICK",
    "MASSACHUSETTS INST TECHNOLOGY"
  ],
  "inventors": [
    "HERR HUGH M",
    "GEYER HARTMUT",
    "EILENBERG MICHAEL FREDERICK"
  ],
  "filing_date": "2010-02-01",
  "publication_date": "2014-10-21",
  "grant_date": "2014-10-21",
  "priority_date": "2005-03-31",
  "application_number": "US-69812810-A",
  "family_id": "43354988",
  "citations": [
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    "CN101111211A",
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}

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