Stabilization of an Inverted Pendulum via Human Brain Inspired Controller Design

Abstract

The human body is mechanically unstable, while the brain as the main controller, is responsible to maintain our balance. However, the mechanisms of the brain towards balancing are still an open research question and thus in this article, we propose a novel modeling architecture for replicating and understanding the fundamental mechanisms for generating balance in the humans. Towards this aim, a nonlinear Recurrent Neural Network (RNN) has been proposed and trained that has the ability to predict the performance of the Central Nervous System (CNS) in stabilizing the human body with high accuracy and that has been trained based on multiple collected human based balancing data and by utilizing system identification techniques. One fundamental contribution of the article is the fact that the obtained network, for the balancing mechanisms, is experimentally evaluated on a single link inverted pendulum that replicates the basic model of the human balance and can be directly extended in the area of humanoids and balancing exoskeletons.

A Robust Nonlinear Control Strategy for Unsupported Paraplegic Standing Using Functional Electrical Stimulation: Controller Synthesis and Simulation

“…While applying the positive external pulse train, the parameters of two adaptive controllers were selected as follows: While applying the negative external pulse train, the parameters of two adaptive controllers were selected as follows: show that while applying the positive pulse trains, the contraction of the extensor muscle happens sooner, or the value of the extensor torque was more. Also, Figures 19,21,and 23 show that while applying the negative pulse, the contraction of the flexor muscle happens sooner, and the value of the flexor torque is more. Also, the frequency of variation of the torque which has seen in Figure 22 and 23 is near to what has been reported by Callahan et al, 44 some differences between real muscle and muscle model can attribute to the inevitable unmolded dynamics of the chosen muscle model as the virtual muscle.…”

“…[5][6][7] During recent years, different control approaches, applicable to restoring the arm-free standing in paraplegia via FES, have been proposed. [8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23] Masani et al 8,9 introduced a feedback proportional-derivative controller to stabilize the body during quiet standing. The designed controller could facilitate stable standing even with closed-loop time delays longer than 75 ms. 8,9 Masani et al 10 also used a proportional integral derivative (PID) controller for balance control during standing due to FES.…”

Lyapunov Stability of a Nonlinear Bio-inspired System for the Control of Humanoid Balance

“…Human posture dynamics in the sagittal plane is usually modelled as an inverted pendulum. Depending on the scenario, the model can be a single inverted pendulum (SIP), see, e.g., (Mergner et al, 2003;Jafari et al, 2019), or a multiple inverted pendulum, see, e.g., (Alexandrov et al, 2017;Hettich et al, 2013;Lippi et al, 2013;Lippi and Mergner, 2017;Abedi and Shoushtari, 2012). The number of degrees of freedom (DoF) representing the body dynamics is in general linked to the intensity of external stimuli, see (Atkeson and Stephens, 2007) for further details.…”

The Importance of Models in Data Analysis with Small Human Movement Datasets: Inspirations from Neurorobotics Applied to Posture Control of Humanoids and Humans

“…The red marks represent the number of samples or subjects used in recent applications (on the left plot) and the size of the neural network in the respective solution (right plot). Specifically, the star represents(Jafari et al, 2019), the crossLippi (2018), and the dot(Lippi et al, 2020). It is evident how the three posture control examples rely on smaller datasets and smaller architectures compared to the possibilities of deep learning at the state of the art.…”