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Research Papers

Robust Adaptive Control of Antagonistic Tendon-Driven Joint in the Presence of Parameter Uncertainties and External Disturbances

[+] Author and Article Information
Hongqian Lu, Xianlin Huang

Center for Control Theory and
Guidance Technology,
School of Astronautics,
Harbin Institute of Technology,
Harbin 150001, Hei Longjiang, China

Xu Zhang

Center for Control Theory and
Guidance Technology,
School of Astronautics,
Harbin Institute of Technology,
Harbin 150001, Hei Longjiang, China
e-mail: alonsobobo@gmail.com

1Corresponding author.

Contributed by the Dynamic Systems Division of ASME for publication in the JOURNAL OF DYNAMIC SYSTEMS, MEASUREMENT, AND CONTROL. Manuscript received August 26, 2016; final manuscript received February 16, 2017; published online June 5, 2017. Assoc. Editor: Hashem Ashrafiuon.

J. Dyn. Sys., Meas., Control 139(10), 101003 (Jun 05, 2017) (10 pages) Paper No: DS-16-1418; doi: 10.1115/1.4036364 History: Received August 26, 2016; Revised February 16, 2017

The design of nonlinear tracking controller for antagonistic tendon-driven joint has garnered considerable attention, whereas many existing control methodologies are impractical in the real-time applications due to complexity of computations. In this work, a robust adaptive control method for controlling antagonistic tendon-driven joint is mainly studied by combining adaptive control with mapping filtered forwarding technique. To enhance the robustness of the closed-loop systems, the true viscous friction coefficients are not needed to be known in our controller design. Typically, to tackle the problem of “explosion of complexity,” filters are introduced to bridge the virtual controls such that the controller is decomposed into several submodules. Mappings and their analytic derivatives are computed by these filters, and the mathematical operations of nonlinearities are greatly simplified. The block diagram of this controller of tendon-driven joint is provided, and controller performances are validated through simulations.

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Figures

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Fig. 1

Illustration of the immersion and invariance approach

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Fig. 2

An antagonistic tendon-driven joint

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Fig. 3

Controller module including virtual controls and filter computation units for an antagonistic actuated joint

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Fig. 4

Trajectories of the joint angle and angular velocity from the origin to the desired position

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Fig. 6

Modularized adaptive controller: control inputs u¯α and u¯β

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Fig. 7

Estimated value of viscous friction coefficients

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Fig. 8

Joint stiffness and stiffness error

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Fig. 9

Joint stiffness of different reference signal. Solid line: r=2π/3. Dashed line: r=π/2. Dotted line: r=π/3.

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Fig. 10

The comparison of joint angle between MFFT and forwarding-based dynamic surface control method

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Fig. 11

The comparison of actuator angle between MFFT and forwarding-based dynamic surface control method

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Fig. 12

The comparison of actuator torques between MFFT and forwarding-based dynamic surface control method

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Fig. 13

The comparison of stiffness between MFFT and forwarding-based dynamic surface control method

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Fig. 14

The comparison of disturbance attenuation between MFFT and disturbance observer based forwarding method

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