Method for using a model-based controller for a robotic leg

• US 10,307,272 B2
• Filed: 11/03/2016
• Issued: 06/04/2019
• 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): Watkins, Marcia L
• Application Number:
  US15/342,661
• Publication Number:
  US 20170049587A1
• Time in Patent Office: 943 Days
• 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 2/6607 Ankle joints
• CPC Class Codes: A61F 2/68 Operating or control means
• CPC Class Codes: A61F 2/72 Bioelectric control, e.g. myoelectric
• 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 2/76 Means for assembling, fitting or testing prostheses, e.g. for measuring or balancing, e.g. alignment means
• CPC Class Codes: A61F 2002/5003 having damping means, e.g. shock absorbers
• CPC Class Codes: A61F 2002/5004 operated by electro- or magnetorheological fluids
• CPC Class Codes: A61F 2002/5006 Dampers, e.g. hydraulic damper
• CPC Class Codes: A61F 2002/5018 for adjusting angular orientation
• CPC Class Codes: A61F 2002/503 for adjusting elasticity, flexibility, spring rate or mechanical tension
• CPC Class Codes: A61F 2002/5033 for adjusting damping
• CPC Class Codes: A61F 2002/5066 Muscles
• CPC Class Codes: A61F 2002/5072 having spring elements
• CPC Class Codes: A61F 2002/5075 Multiple spring systems including two or more helical springs
• CPC Class Codes: A61F 2002/5079 Leaf springs A61F2002/6657 takes precedence
• CPC Class Codes: A61F 2002/5093 Tendon- or ligament-replacing cables
• CPC Class Codes: A61F 2002/6818 : for braking
• CPC Class Codes: A61F 2002/6845 : Clutches
• CPC Class Codes: A61F 2002/6854 : for locking or unlocking a joint
• CPC Class Codes: A61F 2002/6872 : attached and mechanically operated by remaining muscle
• 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
• CPC Class Codes: A61F 2002/762 : for measuring dimensions, e.g. a distance
• CPC Class Codes: A61F 2002/7625 : for measuring angular position
• CPC Class Codes: A61F 2002/763 : for measuring spatial position, e.g. global positioning system [GPS]
• 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 2005/0137 : with two parallel pivots
• CPC Class Codes: A61F 2005/0141 : with more than two parallel pivots
• CPC Class Codes: A61F 2005/0144 : Multibar
• CPC Class Codes: A61F 2005/0155 : with actuating means
• CPC Class Codes: A61F 2005/0179 : with spring 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 40/63 : for local operation
• 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 geometry and reflex architecture information and 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 method for controlling at least one robotic limb joint of a robotic limb, the method comprising the steps of:

providing a neuromuscular model including a muscle model, a muscle geometry, and reflex control equations;

receiving feedback data relating to a measured state of the robotic limb, the robotic limb being a powered prosthesis, orthosis, or exoskeleton;

using the feedback data, the muscle model, the muscle geometry, and the reflex control equations of the neuromuscular model, to determine at least one torque command to be applied to the at least one robotic limb joint; and

applying the at least one torque command to the at least one robotic limb joint.

2. The method of claim 1, further including receiving the at least one torque command from the neuromuscular model with a torque control system, and wherein the at least one torque command is applied by the torque control system.

3. The method of claim 2, wherein the robotic limb includes at least one sensor mounted to the robotic limb, and the feedback data is provided by the at least one sensor mounted to the robotic limb.

4. The method of claim 3, wherein the neuromuscular model and the torque control system are configured to control the robotic limb, wherein the robotic limb is a robotic leg, and wherein the method further includes, with a finite state machine synchronized to a leg gait cycle, receiving the feedback data from the at least one sensor and determining a gait phase of the robotic leg using the feedback data received.

5. The method of claim 4, wherein the neuromuscular model and the torque control system are configured to control the robotic leg, and wherein the robotic leg comprises an ankle joint.

6. The method of claim 5, wherein the robotic leg further comprises a knee joint.

7. The method of claim 6, wherein the robotic leg further comprises a hip joint.

8. The method of claim 4, wherein the neuromuscular model and the torque control system are configured to control the robotic leg, and wherein the robotic leg comprises a knee joint.

9. The method of claim 3, wherein the at least one sensor is an angular joint displacement and velocity sensor, a torque sensor, or an inertial measurement unit.

10. The method of claim 3, wherein the feedback data includes a joint angle and a joint angular velocity measured by the at least one sensor.

11. The method of claim 10, wherein the muscle model comprises a contractile element and a series-elastic element arranged in a muscle tendon unit.

12. The method of claim 11, wherein the reflex control equations are configured in a local feedback loop, and further including, with the reflex control equations, receiving muscle force feedback from the muscle model and providing the stimulation input to the muscle model.

13. The method of claim 12, wherein the muscle force feedback is positive force feedback.

14. The method of claim 12, wherein the reflex control equations are configured to mimic a stretch reflex of an intact human muscle.

15. The method of claim 10, further including using muscle tendon lever arm and muscle tendon length equations of the muscle geometry, in combination with the measured joint angle, to determine a muscle moment arm value and a muscle tendon length value.

16. The method of claim 15, further including, with the muscle model, determining muscle force using the muscle tendon length value and a stimulation input.

17. The method of claim 2, wherein the torque control system includes a feed forward gain, a lead compensator and a friction compensator to adapt the at least one torque command and thereby obtain at least one current command.

18. The method of claim 17, wherein the torque control system further includes a motor controller, and wherein applying the at least one torque command includes driving an actuator of the at least one robotic limb joint with the at least one current command using the motor controller.

19. The method of claim 18, wherein the torque control system further includes a parallel spring model.