Iterative Learning Control for Fast and Accurate Position Tracking with an Articulated Soft Robotic Arm

Hofer, Matthias; D’Andrea, Raffaello

doi:10.1109/iros40897.2019.8967636

Cited by 35 publications

(21 citation statements)

References 19 publications

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“…Angelini et al designed an ILC scheme in combination with low-gain feedback control for improve tracking accuracy [22]. Hofer and his colleagues presented a normoptimal ILC scheme for articulated soft manipulator to improve the tracking control performance [23]. These existing works using the ILC controller as an alternative of the pure inverse dynamics, which is not sufficiently taking advantage of the dynamic models.…”

Section: Introductionmentioning

confidence: 99%

Learning Feedforward Control for Industrial Manipulators

Liu

Popov

Turner

et al. 2021

2021 IEEE 10th Data Driven Control and Learning Systems Conference (DDCLS)

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In this work, an iterative learning control (ILC) algorithm is proposed for industrial manipulators. The proposed ILC algorithm works coordinately with the inverse dynamics of the manipulator and a feedback controller. The entire control scheme has the ability of compensating both repetitive and non-repetitive disturbances; guaranteeing the control accuracy of the first implementation; and improving the control accuracy of the manipulator progressively with successive iterations. In order to build the the convergence of the proposed ILC algorithm, a composite energy function is developed. A case study on a four degree of freedom industrial manipulator is demonstrated to illustrate the effectiveness of the proposed control scheme. By implementing the ILC algorithm, the maximum root mean square error of the control accuracy is improved from 0.0262 rad to 0.0016 rad within ten iterations.

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Section: Introductionmentioning

confidence: 99%

Learning Feedforward Control for Industrial Manipulators

Liu

Popov

Turner

et al. 2021

2021 IEEE 10th Data Driven Control and Learning Systems Conference (DDCLS)

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“…In [16], the authors establish an ILC scheme in combination with low-gain FBC to improve tracking accuracy for robotic manipulators. A norm-optimal ILC scheme for articulated soft manipulators is presented in [17] to improve the tracking control performance. It is worth to note that in most of the above mentioned works, the ILC schemes are designed directly to replace the role of the pure inverse dynamics in the traditional controllers, which however did not fully take the advantage of the available system dynamics.…”

Section: Introductionmentioning

confidence: 99%

Feedforward Enhancement through Iterative Learning Control for Robotic Manipulator

Liu

Wang

et al. 2021

2021 IEEE 17th International Conference on Automation Science and Engineering (CASE)

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This work presents an iterative learning control (ILC) algorithm to enhance the feedforward control (FFC) for robotic manipulators. The proposed ILC algorithm enables the cooperation between the ILC, inverse dynamics, and a PD feedback control (FBC) module. The entire control scheme is elaborated to guarantee the control accuracy of the first implementation; to improve the control performance of the manipulator progressively with successive iterations; and to compensate both repetitive and non-repetitive disturbances, as well as various uncertainties. The convergence of the proposed ILC algorithm is analysed using a well established Lyapunovlike composite energy function (CEF). A trajectory tracking test is carried out by a seven-degree-of-freedom (7-DoF) robotic manipulator to demonstrate the effectiveness and efficiency of the proposed control scheme. By implementing the ILC algorithm, the maximum tracking error and its percentage respect to the motion range are improved from 5.78 • to 1.09 • , and 21.09% to 3.99%, respectively, within three iterations.

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“…An ILC-based method is reported in [ 24 ] to learn the grabbing tasks in a soft fluidic elastomer manipulator. In [ 25 ], a norm-optimal iterative learning control scheme, based on a liner model, is used to improve the position tracking performance of a soft robotic arm. However, such applications of ILC did not consider the problem of control cycle and control target changing, which are inevitable in any soft lower limb exoskeleton.…”

Section: Introductionmentioning

confidence: 99%

Iterative Learning Control for a Soft Exoskeleton with Hip and Knee Joint Assistance

Chen

Zhang

et al. 2020

Sensors

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Walking on different terrains leads to different biomechanics, which motivates the development of exoskeletons for assisting on walking according to the type of a terrain. The design of a lightweight soft exoskeleton that simultaneously assists multiple joints in the lower limb is presented in this paper. It is used to assist both hip and knee joints in a single system, the assistance force is directly applied to the hip joint flexion and the knee joint extension, while indirectly to the hip extension also. Based on the biological torque of human walking at three different slopes, a novel strategy is developed to improve the performance of assistance. A parameter optimal iterative learning control (POILC) method is introduced to reduce the error generated due to the difference between the wearing position and the biological features of the different wearers. In order to obtain the metabolic rate, three subjects walked on a treadmill, for 10 min on each terrain, at a speed of 4 km/h under both conditions of wearing and not wearing the soft exoskeleton. Results showed that the metabolic rate was decreased with the increasing slope of the terrain. The reductions in the net metabolic rate in the experiments on the downhill, flat ground, and uphill were, respectively, 9.86%, 12.48%, and 22.08% compared to the condition of not wearing the soft exoskeleton, where their corresponding absolute values were 0.28 W/kg, 0.72 W/kg, and 1.60 W/kg.

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Iterative Learning Control for Fast and Accurate Position Tracking with an Articulated Soft Robotic Arm

Cited by 35 publications

References 19 publications

Learning Feedforward Control for Industrial Manipulators

Learning Feedforward Control for Industrial Manipulators

Feedforward Enhancement through Iterative Learning Control for Robotic Manipulator

Iterative Learning Control for a Soft Exoskeleton with Hip and Knee Joint Assistance

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