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Iterative Learning Control of an Elbow Joint Motion Simulator

Schimoler, Patrick John (2023) Iterative Learning Control of an Elbow Joint Motion Simulator. Doctoral Dissertation, University of Pittsburgh. (Unpublished)

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Orthopaedic research studies the function, injury, and repair of the musculoskeletal system. Some orthopaedic research employs cadaveric testing; it allows many methods that are impossible in vivo, but it lacks the central nervous system to actuate a specimen. The joint motion simulator is an orthopaedic testbed developed to provide repeatable actuation of a cadaveric specimen. The control of joint motion simulators is complicated by nonlinearities, tendon actuation, and specimen variation.

This dissertation develops a control system for an elbow joint motion simulator. A combination of decoupling, mixed sensitivity robust control, and iterative learning control are used to track 100° sinusoidal flexion/extension and pronation/supination motions both individually and simultaneously. Small muscle co-contractions are maintained to prevent tendon slack. The control approach is applied to three systems of increasing complexity: flexion/extension controlled by the brachialis, flexion/extension controlled by the brachialis and triceps, and both flexion/extension and pronation/supination controlled by the biceps, brachialis, pronator, and triceps. All three levels of control are applied to a mechanical elbow and the two degree of freedom control is also applied to both a large and small cadaveric elbow. To compare with the state-of-the-art elbow joint motion simulator, joint position tracking error goals were set at 1.5° of root mean square error and 4° of maximum error; co-contraction moment error goals were set at 25 N-mm of root mean square error and 75 N-m of maximum error for flexion/extension and 30 N-m of maximum error for pronation/supination. All error goals were achieved for the first and second stages of testing. During the third stage of testing with the mechanical elbow and small cadaveric elbow, the tracking goals were achieved in all cases except during a flexion motion with fixed pronation angle. During the third stage of testing with the large cadaveric elbow, the tracking goals were achieved in all cases except the simultaneous pronation and extension motion and again during the flexion motion with fixed pronation angle.

This dissertation establishes a structured method of control synthesis for joint motion simulators devoid of the ad hoc methods often employed in joint motion simulator control.


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Item Type: University of Pittsburgh ETD
Status: Unpublished
CreatorsEmailPitt UsernameORCID
Schimoler, Patrick Johnpjs50@pitt.edupjs500000-0001-8214-0100
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Committee CoChairMiller, Mark
Committee CoChairVipperman, Jeffrey
Committee MemberCole, Daniel
Committee MemberMao,
Date: 13 June 2023
Date Type: Publication
Defense Date: 5 April 2023
Approval Date: 13 June 2023
Submission Date: 16 March 2023
Access Restriction: No restriction; Release the ETD for access worldwide immediately.
Number of Pages: 218
Institution: University of Pittsburgh
Schools and Programs: Swanson School of Engineering > Mechanical Engineering
Degree: PhD - Doctor of Philosophy
Thesis Type: Doctoral Dissertation
Refereed: Yes
Uncontrolled Keywords: Robust control Iterative learning control Joint motion simulator Biomechanics
Additional Information: Thank you very much for taking the time to review my dissertation format (again). I had to create a new entry because I could not edit the last submission to replace the old pdf with my new pdf that was approved by my committee. I apologize if this created a hassle. Sincerely, Pat Schimoler Sincerely, Patrick Schimoler
Date Deposited: 13 Jun 2023 14:09
Last Modified: 13 Jun 2023 14:09

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