Model-based neuromechanical controller for a robotic leg

• US 8,864,846 B2
• Filed: 02/01/2010
• Issued: 10/21/2014
• Est. Priority Date: 03/31/2005
• Status: Active Grant

• Current Assignee: Massachusetts Institute of Technology
• Original Assignee: Massachusetts Institute of Technology
• Inventors: Herr, Hugh M., Geyer, Hartmut, Eilenberg, Michael Frederick
• Primary Examiner(s): SNOW, BRUCE EDWARD
• Application Number:
  US12/698,128
• Publication Number:
  US 20100324699A1
• Time in Patent Office: 1,723 Days
• Field of Search: 700/245, 623/64, 623 24- 26
• US Class Current: 623/25
• CPC Class Codes: A61F 2/605 Hip joints
• CPC Class Codes: A61F 2/64 Knee joints
• CPC Class Codes: A61F 2/66 Feet; Ankle joints
• CPC Class Codes: A61F 2/70 electrical
• CPC Class Codes: A61F 2/74 fluid , i.e. hydraulic or pneumatic
• CPC Class Codes: A61F 2/741 using powered actuators, e.g. stepper motors or solenoids
• CPC Class Codes: A61F 2002/701 operated by electrically controlled means, e.g. solenoids or torque motors
• CPC Class Codes: A61F 2002/704 computer-controlled, e.g. robotic control
• CPC Class Codes: A61F 2002/705 Electromagnetic data transfer for implantable prostheses A61F2250/0002
• CPC Class Codes: A61F 2002/7625 for measuring angular position
• CPC Class Codes: A61F 2002/7635 for measuring force, pressure or mechanical tension
• CPC Class Codes: A61F 2002/764 for measuring acceleration
• CPC Class Codes: A61F 2002/7645 for measuring torque, e.g. hinge or turning moment, moment of force
• CPC Class Codes: A61F 2005/0155 with actuating means
• CPC Class Codes: A61F 5/0123 for the knees A61F5/0106 takes precedence
• CPC Class Codes: A61F 5/0127 for the feet A61F5/0111 takes precedence
• CPC Class Codes: B25J 9/0006 Exoskeletons, i.e. resembling a human figure
• CPC Class Codes: B25J 9/1075 with muscles or tendons
• CPC Class Codes: B25J 9/1633 compliant, force, torque control, e.g. combined with position control
• CPC Class Codes: G16H 50/50 for simulation or modelling of medical disorders

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.