Iterative Learning Control of an Industrial Robot for Neuromuscular Training
01 Aug 2019-pp 926-932
TL;DR: A norm-optimal iterative learning control algorithm for the robot-assisted training is developed that aims at minimizing the external knee joint moment, which is commonly used to quantify the loading of the medial compartment.
Abstract: Effective training requires high muscle forces potentially leading to training-induced injuries. Thus, continuous monitoring and controlling of the loadings applied to the musculoskeletal system along the motion trajectory is required. In this paper, a norm-optimal iterative learning control algorithm for the robot-assisted training is developed. The algorithm aims at minimizing the external knee joint moment, which is commonly used to quantify the loading of the medial compartment. To estimate the external knee joint moment, a musculoskeletal lower extremity model is implemented in OpenSim and coupled with a model of an industrial robot and a force plate mounted at its end-effector. The algorithm is tested in simulation for patients with varus, normal and valgus alignment of the knee. The results show that the algorithm is able to minimize the external knee joint moment in all three cases and converges after less than seven iterations.
Citations
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TL;DR: In this paper, an iterative learning control algorithm is proposed to track the desired trajectory of human hip and knee joints, which leads to poor follow-up performance of the human-machine system.
Abstract: At present, the motion control algorithms of lower limb exoskeleton robots have errors in tracking the desired trajectory of human hip and knee joints, which leads to poor follow-up performance of the human-machine system. Therefore, an iterative learning control algorithm is proposed to track the desired trajectory of human hip and knee joints. In this paper, the experimental platform of lower limb exoskeleton rehabilitation robot is built, and the control system software and hardware design and robot prototype function test are carried out. On this basis, a series of experiments are carried out to verify the rationality of the robot structure and the feasibility of the control method. Firstly, the dynamic model of the lower limb exoskeleton robot is established based on the structure analysis of the human lower limb; secondly, the servo control model of the lower limb exoskeleton robot is established based on the iterative learning control algorithm; finally, the exponential gain closed-loop system is designed by using MATLAB software. The relationship between convergence speed and spectral radius is analyzed, and the expected trajectory of hip joint and knee joint is obtained. The simulation results show that the algorithm can effectively improve the gait tracking accuracy of the lower limb exoskeleton robot and improve the follow-up performance of the human-machine system.
9 citations
TL;DR: An adaptive norm-optimal iterative learning control algorithm to minimize the knee joint loadings during the leg extension training with an industrial robot is proposed.
Cites background or methods from "Iterative Learning Control of an In..."
...For further details regarding the musculoskeletal lower extremity model refer to (Ketelhut et al., 2019)....
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...…rejection of the proposed adaptive ROILC algorithm is tested and compared to the results of the conventional NOILC algorithm presented by Ketelhut et al. (2019), the HOILC algorithm described in Section 2.2 and the ROILC algorithm with a constant uncertainty weighting W by Son et al.…...
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...In this case, G approximates the relation between plate angle α(t) and external knee joint moment M(t) and between Cartesian coordinate in ydirection y(t) and knee joint flexion angle Θ(t) as firstorder systems equivalent to Barton and Alleyne (2010) and Ketelhut et al. (2019)....
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...In previous work, Ketelhut et al. (2019) proved that a NOILC algorithm is able to minimize knee joint loadings during the leg extension training with an industrial robot....
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...B.1 includes the parameters of the HOILC and AOILC algorithm described in Section 2.2 and Section 2.3 as well as the parameters of the conventional NOILC algorithm presented by Ketelhut et al. (2019)....
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TL;DR: In this paper , an iterative learning control algorithm is proposed to track the desired trajectory of human hip and knee joints, which leads to poor follow-up performance of the human-machine system.
Abstract: At present, the motion control algorithms of lower limb exoskeleton robots have errors in tracking the desired trajectory of human hip and knee joints, which leads to poor follow-up performance of the human-machine system. Therefore, an iterative learning control algorithm is proposed to track the desired trajectory of human hip and knee joints. In this paper, the experimental platform of lower limb exoskeleton rehabilitation robot is built, and the control system software and hardware design and robot prototype function test are carried out. On this basis, a series of experiments are carried out to verify the rationality of the robot structure and the feasibility of the control method. Firstly, the dynamic model of the lower limb exoskeleton robot is established based on the structure analysis of the human lower limb; secondly, the servo control model of the lower limb exoskeleton robot is established based on the iterative learning control algorithm; finally, the exponential gain closed-loop system is designed by using MATLAB software. The relationship between convergence speed and spectral radius is analyzed, and the expected trajectory of hip joint and knee joint is obtained. The simulation results show that the algorithm can effectively improve the gait tracking accuracy of the lower limb exoskeleton robot and improve the follow-up performance of the human-machine system.
16 Dec 2022
TL;DR: In this article , a sliding muscle surface controller (SMSC) is designed to suppress disturbances and to reduce uncertainty in the muscle-driven musculoskeletal system (MDMS).
Abstract: In this study, a sliding muscle surface controller (SMSC) is designed to suppress disturbances and to reduce uncertainty in the muscle-driven musculoskeletal system (MDMS). When performing manipulation tasks in unstructured environments, bio-inspired robots are able to exhibit more flexibility and safety. Although the model of MDMS can be solved by combining the muscle model and the joint-link dynamics, the influence of unknown external disturbances and dynamic uncertainties makes it difficult to describe the system perfectly in practice. In order to solve the problems, a sliding muscle surface controller with an integral power reaching law is designed to suppress the chattering problem in the control and improve the anti-interference ability of the system and reduce the integrates error between expected and simulation. Subsequently, the stability of musculoskeletal system was ensured using the principle of Lyapunov synthesis. Finally, the simulation results showed that the proposed design techniques could effectively improve the robustness of muscle model.
References
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TL;DR: OpenSim is developed, a freely available, open-source software system that lets users develop models of musculoskeletal structures and create dynamic simulations of a wide variety of movements to simulate the dynamics of individuals with pathological gait and to explore the biomechanical effects of treatments.
Abstract: Dynamic simulations of movement allow one to study neuromuscular coordination, analyze athletic performance, and estimate internal loading of the musculoskeletal system. Simulations can also be used to identify the sources of pathological movement and establish a scientific basis for treatment planning. We have developed a freely available, open-source software system (OpenSim) that lets users develop models of musculoskeletal structures and create dynamic simulations of a wide variety of movements. We are using this system to simulate the dynamics of individuals with pathological gait and to explore the biomechanical effects of treatments. OpenSim provides a platform on which the biomechanics community can build a library of simulations that can be exchanged, tested, analyzed, and improved through a multi-institutional collaboration. Developing software that enables a concerted effort from many investigators poses technical and sociological challenges. Meeting those challenges will accelerate the discovery of principles that govern movement control and improve treatments for individuals with movement pathologies.
3,621 citations
"Iterative Learning Control of an In..." refers methods in this paper
...To test the designed NOILC algorithm described in Section II-B, the musculoskeletal lower extremity model described in [19] is implemented in OpenSim [6]....
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...A setup consisting of the previously described experimental research platform, a motion capturing system and a musculoskeletal lower extremity model, implemented in OpenSim [6], has been found suitable to estimate joint loadings....
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TL;DR: Though beginning its third decade of active research, the field of ILC shows no sign of slowing down and includes many results and learning algorithms beyond the scope of this survey.
Abstract: This article surveyed the major results in iterative learning control (ILC) analysis and design over the past two decades. Problems in stability, performance, learning transient behavior, and robustness were discussed along with four design techniques that have emerged as among the most popular. The content of this survey was selected to provide the reader with a broad perspective of the important ideas, potential, and limitations of ILC. Indeed, the maturing field of ILC includes many results and learning algorithms beyond the scope of this survey. Though beginning its third decade of active research, the field of ILC shows no sign of slowing down.
2,645 citations
"Iterative Learning Control of an In..." refers background in this paper
...robot arm manipulators, chemical batch processes and injection-molding machines to improve the tracking performance over multiple repetitions [4], [37]....
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...For surveys of different ILC algorithms and applications in upper-limb stroke rehabilitation refer to [1], [4], [36] and [9], [8], respectively....
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01 Nov 2007
TL;DR: The iterative learning control (ILC) literature published between 1998 and 2004 is categorized and discussed, extending the earlier reviews presented by two of the authors.
Abstract: In this paper, the iterative learning control (ILC) literature published between 1998 and 2004 is categorized and discussed, extending the earlier reviews presented by two of the authors. The papers includes a general introduction to ILC and a technical description of the methodology. The selected results are reviewed, and the ILC literature is categorized into subcategories within the broader division of application-focused and theory-focused results.
1,417 citations
"Iterative Learning Control of an In..." refers background in this paper
...For surveys of different ILC algorithms and applications in upper-limb stroke rehabilitation refer to [1], [4], [36] and [9], [8], respectively....
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TL;DR: There is increasing interest in using robotic devices to assist in movement training following neurologic injuries such as stroke and spinal cord injury, and this review summarizes techniques for implementing assistive strategies, including impedance-, counterbalance-, and EMG- based controllers, as well as adaptive controllers that modify control parameters based on ongoing participant performance.
Abstract: There is increasing interest in using robotic devices to assist in movement training following neurologic injuries such as stroke and spinal cord injury. This paper reviews control strategies for robotic therapy devices. Several categories of strategies have been proposed, including, assistive, challenge-based, haptic simulation, and coaching. The greatest amount of work has been done on developing assistive strategies, and thus the majority of this review summarizes techniques for implementing assistive strategies, including impedance-, counterbalance-, and EMG- based controllers, as well as adaptive controllers that modify control parameters based on ongoing participant performance. Clinical evidence regarding the relative effectiveness of different types of robotic therapy controllers is limited, but there is initial evidence that some control strategies are more effective than others. It is also now apparent there may be mechanisms by which some robotic control approaches might actually decrease the recovery possible with comparable, non-robotic forms of training. In future research, there is a need for head-to-head comparison of control algorithms in randomized, controlled clinical trials, and for improved models of human motor recovery to provide a more rational framework for designing robotic therapy control strategies.
992 citations
"Iterative Learning Control of an In..." refers background in this paper
...Current robot-assisted rehabilitation systems can generally be divided in exoskeletons and end-effector based robots [26]....
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...For reviews of different control algorithms for the robotic movement training after neurologic injury and for lower limb rehabilitation robots in particular refer to [26], [28] and [27], respectively....
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TL;DR: The recent progress of upper limb exoskeleton robots for rehabilitation treatment of patients with neuromuscular disorders and the fundamental challenges in developing these devices are described.
475 citations
"Iterative Learning Control of an In..." refers background in this paper
...A review of different exoskeletons for lower [14] and upper limbs [24] as well as the corresponding control strategies can be found in [5]....
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