Velocity Estimation for Ultralightweight Tendon-Driven Series Elastic Robots

Kirchhoff, Jérôme; Stryk, Oskar von

doi:10.1109/lra.2017.2729663

Cited by 10 publications

(3 citation statements)

References 12 publications

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“…Usually, R is set as a constant value based on the measurement accuracy of the sensor and Q is also kept constant using a trialand-error approach to reduce complexity. To obtain a smooth output signal from Kalman filter without delay time, R should be tuned [21]. In order to get the optimal output results with a small-time delay in a wide range of input frequency, the noise variances have to change accordingly.…”

Section: Kalman Filter Without Using Kinematic Modelmentioning

confidence: 99%

An Adaptive Kalman Filtering Algorithm Without Using Kinematic Models

Wah,

Thant Sin

2023

AEJ

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The performance and accuracy of Kalman filter depends on its gain value related to the process noise covariance and the measurement noise variance which may vary according to experimental settings such as noise and sampling time. Thus, setting the appropriate values for the noise variances that fit for a wide range of experimental setting is a challenge for conventional Kalman filter. This paper proposes an adaptive Kalman filter with the adaptive noise variance for velocity estimation without using kinematic model. By applying only the quantized position measurement signal generated from the optical incremental encoder, an adaptive process noise variance is proposed. The experimental results show that the proposed method outperforms the conventional Kalman filter in achieving accurate and smooth velocity estimation without large time delay.

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Section: Kalman Filter Without Using Kinematic Modelmentioning

confidence: 99%

An Adaptive Kalman Filtering Algorithm Without Using Kinematic Models

Wah,

Thant Sin

2023

AEJ

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“…In addition, it can reflect the effect of external disturbances. The Kalman filter also similarly operates as a velocity observer [20], [39], [40]. However, these methods are sensitive to the accuracy of the model.…”

Section: Introductionmentioning

confidence: 99%

Neural Network-Based Joint Velocity Estimation Method for Improving Robot Control Performance

Kim,

Hwang,

Lim

et al. 2023

IEEE Access

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Joint velocity estimation is one of the essential properties that implement for accurate robot motion control. Although conventional approaches such as numerical differentiation of position measurements and model-based observers exhibit feasible performance for velocity estimation, instability can be occurred because of phase lag or model inaccuracy. This study proposes a model-free approach that can estimate the velocity with less phase lag by batch training of a neural network with pre-collected encoder measurements. By learning a weighted moving average, the proposed method successfully estimates the velocity with less latency imposed by the noise attenuation compared to the conventional methods. Practical experiments with two robot platforms with high degrees of freedom are conducted to validate the effectiveness of the proposed method.

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“…Thus, since incremental encoders can only generate a pulse train with its frequency related to the shaft velocity, a suitable estimation algorithm should be applied in order to obtain smooth velocity information. Although there has been extensive past research related to this issue over the past few decades [26,27], new emerging applications in robotics and other areas with cutting-edge requirements foster further intensive research in this field, e.g., the motion control of hydraulic actuators [28], pneumatic actuators [29], haptic interfaces [30], mobile robots [31], robot manipulators [32], humanoid robots [33], and collaborative robots for safe physical human-robot interaction [34], automotive applications [35], etc. Since the performance of a velocity control loop is related strongly to the quality of the velocity feedback information, achieving a smooth, high-bandwidth velocity estimate, which is required for oscillation-free high-bandwidth control loops, still presents quite a challenge.…”

Section: Introductionmentioning

confidence: 99%

The Improved Division-Less MT-Type Velocity Estimation Algorithm for Low-Cost FPGAs

Hace

2019

Electronics

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Advanced motion control applications require smooth and highly accurate high-bandwidth velocity feedback, which is usually provided by an incremental encoder. Furthermore, high sampling rates are also demanded in order to achieve cutting-edge system performance. Such control system performance with high accuracy can be achieved easily by FPGA-based controllers. On the other hand, the well-known MT method for velocity estimation has been well proven in practice. However, its complexity, which is related to the inherent arithmetic division involved in the calculus part of the method, prevents its holistic implementation as a single-chip solution on small-size low-cost FPGAs that are suitable for practical optimized control systems. In order to overcome this obstacle, we proposed a division-less MT-type algorithm that consumes only minimal FPGA resources, which makes it proper for modern cost-optimized FPGAs. In this paper, we present new results. The recursive discrete algorithm has been further optimized, in order to improve the accuracy of the velocity estimation. The novel algorithm has also been implemented on the experimental FPGA board, and validated by practical experiments. The enhanced algorithm design resulted in improved practical performance.

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Velocity Estimation for Ultralightweight Tendon-Driven Series Elastic Robots

Cited by 10 publications

References 12 publications

An Adaptive Kalman Filtering Algorithm Without Using Kinematic Models

An Adaptive Kalman Filtering Algorithm Without Using Kinematic Models

Neural Network-Based Joint Velocity Estimation Method for Improving Robot Control Performance

The Improved Division-Less MT-Type Velocity Estimation Algorithm for Low-Cost FPGAs

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