Human balancing is an important issue in many fields of everyday life, such as walking, running, cycling, carrying objects and even standing. The understanding of the balancing process is very important, especially from the point of view of elderly people. However, there are a lot of open questions about the working principle of the neural system.
We focus on the mathematical modelling of the neural process, which flows in the human neurotic system during standing still. There are several approaches in the literature. One approach is to apply a linear compensator (such as PD, PID, PIDA) in the model. Besides, model based predictive controllers are also feasible, when the human acts using a pre-learned control input pattern in the certain situations.
In our study, we compare the operation of the two control approaches, by comparing stabilometry measures, such as typical vibration frequencies, average velocity of the center of mass of the body and maximum/average tilt angle.
Table of Contents
1 Introduction
2 Model of human balancing
2.1 Mechanical model
2.2 Controllers
2.2.1 PD controllers
2.2.2 PD controllers with dead-zone
2.2.3 Model-predictive energy based controller
3 Measurement data and comparison with the simulations
3.1 Simulated and measurement data
3.2 Comparison by means of the stabilometry measures
4 Results and conclusions
Research Objective and Topics
This work aims to investigate and model the neural control processes involved in human postural balancing during quiet standing by comparing different mathematical control approaches—specifically proportional-derivative (PD) controllers and model-predictive energy-based controllers—against human measurement data.
- Mathematical modeling of human postural balancing using an inverted pendulum.
- Evaluation of linear compensator models, specifically PD controllers with sensory dead-zones.
- Development and analysis of a model-predictive energy-based control approach.
- Comparison of simulation results with empirical human stabilometry data (open vs. closed eyes).
- Assessment of control stability and resemblance to human balancing behavior via RMS values and frequency analysis.
Excerpt from the Book
2.2.3 Model-predictive energy based controller
It is possible to consider the human brain as a model-based controller. Good example can be babies, because most of the time, they can’t plan the output of their action. They learn it by trying all possible ways they can in order to perform the desired output, such as reaching an object. We can say that we build up the model of our body in our childhood. Similarly, we build the appropriate models when learning a new activity, like walking, running, skiing, etc. Postural balancing is a similar learning process and it is reasonable to try to find a model behind the neural processes.
Our model-based controller involves the issue of act-and-wait controllers. Some human movements are working with act-and-wait principle [8] because human brain is not continuously calculating all the parameters which it needs. Humans hindsight the certain events and decide the next moves based on the result which they got from the last one.
The principle of our model-predictive energy based control evaluates the potential energy U of the inverted pendulum and applies certain amplitude torque peak for certain amount of time in order to drive the pendulum near to the upward vertical position, where the potential energy U0 is known.
Summary of Chapters
1 Introduction: Provides an overview of the importance of feedback control in human balancing and introduces the inverted pendulum as the chosen mathematical model.
2 Model of human balancing: Details the mechanical model and the mathematical formulations for various controllers, including PD and energy-based approaches.
3 Measurement data and comparison with the simulations: Compares the simulation results with actual human stabilometry data using RMS values and frequency analysis.
4 Results and conclusions: Evaluates the effectiveness of the different models in mimicking human balancing behavior and discusses potential future research directions.
Keywords
Human balancing, Inverted pendulum, Feedback control, PD controller, Model-predictive control, Energy-based controller, Stabilometry, Neural process, Sensory dead-zone, Act-and-wait principle, Postural sway, RMS value, Frequency analysis, Mathematical modeling, Biomechanics.
Frequently Asked Questions
What is the fundamental focus of this research?
The research investigates the mathematical modeling of the neural processes that govern human postural balancing during standing still.
Which central topics are addressed in this paper?
The paper covers the mechanical modeling of the body as an inverted pendulum, various control strategies (PD and energy-based), and the comparison of these models against human experimental data.
What is the primary goal of the study?
The goal is to determine which control models best mimic the non-decaying, oscillatory nature of human postural balancing observed in real-world conditions.
Which scientific methods are employed?
The study uses mathematical derivation of equations of motion, control theory (PD and model-predictive control), numerical simulation, and stabilometry analysis (FFT and RMS calculation).
What is covered in the main body of the work?
The main body details the mechanical inverted pendulum model, specific configurations of PD and energy-based controllers, and a comparative analysis of simulation outputs versus human sway data.
Which keywords best characterize this work?
Key terms include human balancing, inverted pendulum, PD controller, model-predictive control, stabilometry, and neural processes.
Why are PD controllers with dead-zones used in this study?
They are used because human sensory organs exhibit dead-zones, and including these in the model helps simulate more realistic, non-decaying oscillations compared to basic PD controllers.
How does the model-predictive energy-based controller differ from the PD controller?
Unlike the linear PD approach, the energy-based controller mimics an "act-and-wait" principle, where the brain activates pre-learned torque impulses only when the deviation from the vertical becomes sensible.
What do the stabilometry measures reveal about the models?
The RMS values and frequency spectra from the simulations are compared to human experimental data (open vs. closed eyes) to assess the "human-likeness" of the balance control performance.
- Citar trabajo
- Mirroyal Ismayilov (Autor), 2018, Predictive Control Algorithms in Human Balancing, Múnich, GRIN Verlag, https://www.grin.com/document/541268
