Control of Planar Spring–Mass Running Through Virtual Tuning of Radial Leg Damping

Secer, Gorkem; Saranlı, Uluç

doi:10.1109/tro.2018.2830394

Cited by 12 publications

(12 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Regarding this, different extensions to SLIP model has been proposed. In this paper, we consider the extended model in [15] since it was specifically proposed for energetically efficient control of locomotion on robotic platforms. Interested reader can see [16], [17], [18] for other extensions of the SLIP model.…”

Section: The Spring-mass Model Of Runningmentioning

confidence: 99%

A Momentum-Based Foot Placement Strategy for Stable Postural Control of Robotic Spring-Mass Running with Point Feet

Secer

Cinar

2020

2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

Self Cite

View full text Add to dashboard Cite

A long-standing argument in model-based control of locomotion is about the level of complexity that a model should have to define a behavior such as running. Even though goldilocks model based on biomechanical evidence is often sought, it is unclear what complexity level qualifies to be such a model. This dilemma deepens further for bipedal robotic running with point feet, since these robots are underactuated, while tracking center-of-mass (COM) trajectories defined by the spring-loaded inverted pendulum (SLIP) model of running allocates all control inputs, leaving angular coordinates of the robot's trunk uncontrolled. Existing work in the literature approach this problem either by trading off COM trajectories against upright trunk posture during stance or by adopting more detailed models that include effects of trunk angular dynamics. In this paper, we present a new approach based on modifying foot placement targets of the SLIP model. Theoretical analysis and numerical results show that the proposed approach outperforms these traditional strategies.

show abstract

Section: The Spring-mass Model Of Runningmentioning

confidence: 99%

A Momentum-Based Foot Placement Strategy for Stable Postural Control of Robotic Spring-Mass Running with Point Feet

Secer

Cinar

2020

2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

Self Cite

View full text Add to dashboard Cite

show abstract

“…It is also possible to develop controllers for the SLIP model without considering how human-like the resulting gait is. These studies typically solve the dynamics of the SLIP model, then derive a controller to create a closed system model [17][18][19][20]. Beyond this, work with both the inverted pendulum and SLIP models have shown that different controllers can drastically change important gait properties [14,21].…”

Section: Introductionmentioning

confidence: 99%

Comparing system identification techniques for identifying human-like walking controllers

Martin

2021

R. Soc. open sci.

View full text Add to dashboard Cite

While human walking has been well studied, the exact controller is unknown. This paper used human experimental walking data and system identification techniques to infer a human-like controller for a spring-loaded inverted pendulum (SLIP) model. Because the best system identification technique is unknown, three methods were used and compared. First, a linear system was found using ordinary least squares. A second linear system was found that both encoded the linearized SLIP model and matched the first linear system as closely as possible. A third nonlinear system used sparse identification of nonlinear dynamics (SINDY). When directly mapping states from the start to the end of a step, all three methods were accurate, with errors below 10% of the mean experimental values in most cases. When using the controllers in simulation, the errors were significantly higher but remained below 10% for all but one state. Thus, all three system identification methods generated accurate system models. Somewhat surprisingly, the linearized system was the most accurate, followed closely by SINDY. This suggests that nonlinear system identification techniques are not needed when finding a discrete human gait controller, at least for unperturbed walking. It may also suggest that human control of normal, unperturbed walking is approximately linear.

show abstract

“…The case of second-order transfer functions is unique since they represent a large number of physical systems. For example, investigations [ 11 , 12 , 13 , 14 , 15 ] work with systems described by a second-order transfer function.…”

Section: Introductionmentioning

confidence: 99%

Estimation of Transfer Function Coefficients for Second-Order Systems via Metaheuristic Algorithms

Rodríguez-Abreo

Rodríguez‐Reséndiz

Velásquez

et al. 2021

Sensors

View full text Add to dashboard Cite

The present research develops the parametric estimation of a second-order transfer function in its standard form, employing metaheuristic algorithms. For the estimation, the step response with a known amplitude is used. The main contribution of this research is a general method for obtaining a second-order transfer function for any order stable systems via metaheuristic algorithms. Additionally, the Final Value Theorem is used as a restriction to improve the velocity search. The tests show three advantages in using the method proposed in this work concerning similar research and the exact estimation method. The first advantage is that using the Final Value Theorem accelerates the convergence of the metaheuristic algorithms, reducing the error by up to 10 times in the first iterations. The second advantage is that, unlike the analytical method, it is unnecessary to estimate the type of damping that the system has. Finally, the proposed method is adapted to systems of different orders, managing to calculate second-order transfer functions equivalent to higher and lower orders. Response signals to the step of systems of an electrical, mechanical and electromechanical nature were used. In addition, tests were carried out with simulated signals and real signals to observe the behavior of the proposed method. In all cases, transfer functions were obtained to estimate the behavior of the system in a precise way before changes in the input. In all tests, it was shown that the use of the Final Value Theorem presents advantages compared to the use of algorithms without restrictions. Finally, it was revealed that the Gray Wolf Algorithm has a better performance for parametric estimation compared to the Jaya algorithm with an error up to 50% lower.

show abstract

Control of Planar Spring–Mass Running Through Virtual Tuning of Radial Leg Damping

Cited by 12 publications

References 28 publications

A Momentum-Based Foot Placement Strategy for Stable Postural Control of Robotic Spring-Mass Running with Point Feet

A Momentum-Based Foot Placement Strategy for Stable Postural Control of Robotic Spring-Mass Running with Point Feet

Comparing system identification techniques for identifying human-like walking controllers

Estimation of Transfer Function Coefficients for Second-Order Systems via Metaheuristic Algorithms

Contact Info

Product

Resources

About