Adaptive Balance Control of Inverted Pendulum with Unknown Parameters

Author(s):  
Akihiro HARA ◽  
Guillermo ENRIQUEZ ◽  
Huei Ee YAP ◽  
Shuji HASHIMOTO
Robotics ◽  
2019 ◽  
Vol 8 (4) ◽  
pp. 89 ◽  
Author(s):  
Giuseppe Menga ◽  
Marco Ghirardi

The zero moment point ( Z M P ) and the linearized inverted pendulum model linking the Z M P to the center of gravity ( C O G ) have an important role in the control of the postural equilibrium (balance) of biped robots and lower-limb exoskeletons. A solution for balance real time control, closing the loop from the joint actual values of the C O G and Z M P , has been proposed by Choi. However, this approach cannot be practically implemented: While the Z M P actual value is available from the center of pressure ( C o P ) measured under the feet soles, the C O G is not measurable, but it can only be indirectly assessed from the joint-angle measures, the knowledge of the kinematics, and the usually poorly known weight distribution of the links of the chain. Finally, the possible presence of unknown external disturbance forces and the nonlinear, complex nature of the kinematics perturb the simple relationship between the Z M P and C O G in the linearized model. The aim of this paper is to offer, starting from Choi’s model, a practical implementation of closed-loop balance control fusing C o P and joint-angle measures, eliminating possible inconsistencies. In order to achieve this result, we introduce a model of the linearized inverted pendulum for an extended estimation, not only of C O G and Z M P , but also of external disturbances. This model is then used, instead of Choi’s equations, for estimation and balance control, using H ∞ theory. As the C O G information is recovered from the joint-angle measures, the identification of a statistically equivalent serial chain ( S E S C ) linking the C O G to the joint angles is also discussed.


2020 ◽  
Vol 15 (9) ◽  
Author(s):  
Kyle W. Siegrist ◽  
Ryan M. Kramer ◽  
James R. Chagdes

Abstract Understanding the mechanisms behind human balance has been a subject of interest as various postural instabilities have been linked to neuromuscular diseases (e.g., Parkinson's, multiple sclerosis, and concussion). This paper presents a method to characterize an individual's postural stability and estimate of their neuromuscular feedback control parameters. The method uses a generated topological mapping between a subject's experimental data and a dataset consisting of time-series realizations generated using an inverted pendulum mathematical model of upright balance. The performance of the method is quantified using a set of validation time-series realizations with known stability and neuromuscular control parameters. The method was found to have an overall sensitivity of 85.1% and a specificity of 91.9%. Furthermore, the method was most accurate when identifying limit cycle oscillations (LCOs) with a sensitivity of 91.1% and a specificity of 97.6%. Such a method has the capability of classifying an individual's stability and revealing possible neuromuscular impairment related to balance control, ultimately providing useful information to clinicians for diagnostic and rehabilitation purposes.


Author(s):  
Akihiro HARA ◽  
Guillermo ENRIQUEZ ◽  
Huei Ee YAP ◽  
Shuji HASHIMOTO

2009 ◽  
Vol 19 (2) ◽  
pp. 026110 ◽  
Author(s):  
John Milton ◽  
Juan Luis Cabrera ◽  
Toru Ohira ◽  
Shigeru Tajima ◽  
Yukinori Tonosaki ◽  
...  

2021 ◽  
Vol 1 (1) ◽  
pp. 84-89
Author(s):  
Ümit Önen ◽  
Abdullah Çakan

In this study, modeling and LQR control of a reaction wheel inverted pendulum system is described. The reaction wheel inverted pendulum model is created by using a 3D CAD platform and exported to Simscape Multibody. The multibody model is linearized to derive a state-space representation. A LQR (Linear-quadratic regulator) controller is designed and applied for balance control of the pendulum. The results show that deriving a state-space representation from multibody is an easy and effective way to model dynamic systems and balance control of the reaction wheel inverted pendulum is successfully achieved by LQR controller. Results are given in the form of graphics.


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