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.
Inhaltsverzeichnis (Table of Contents)
- Introduction
- Model of human balancing
- Mechanical model
- Controllers
- PD controllers
- PD controllers with dead-zone
- Model-predictive energy based controller
- Measurement data and comparison with the simulations
- Simulated and measurement data
- Comparison by means of the stabilometry measures
- Results and conclusions
Zielsetzung und Themenschwerpunkte (Objectives and Key Themes)
This research focuses on the mathematical modeling of human balancing, particularly the neural processes involved in standing still. The study investigates different control approaches for simulating this process, including linear compensators and model-based predictive controllers. The goal is to compare the performance of these approaches by analyzing stabilometry measures.
- Mathematical modeling of human balancing
- Comparison of linear compensators and model-based predictive controllers
- Analysis of stabilometry measures
- Understanding the neural processes involved in standing still
- Importance of human balancing in everyday life and for elderly individuals
Zusammenfassung der Kapitel (Chapter Summaries)
- Introduction: This chapter introduces the importance of feedback control in science and engineering, highlighting human balancing as a prime example. It discusses the complexity of balancing, the various sensory inputs involved, and the crucial role of the brain in controlling movement. It also emphasizes the significance of research in this area, particularly for addressing the needs of an aging population.
- Model of human balancing: This chapter details the mechanical model used to represent human balancing, focusing on the inverted pendulum as a simplified representation. It further explores different controller approaches, including PD controllers, PD controllers with dead-zone, and model-predictive energy-based controllers.
- Measurement data and comparison with the simulations: This chapter presents the simulated and measured data obtained from the experiments. It then analyzes and compares the data based on stabilometry measures, such as typical vibration frequencies, average velocity of the center of mass, and maximum/average tilt angle.
Schlüsselwörter (Keywords)
Human balancing, feedback control, mathematical modeling, neural processes, linear compensators, model-based predictive controllers, stabilometry measures, inverted pendulum, elderly population, aging, safety.
- Arbeit zitieren
- Mirroyal Ismayilov (Autor:in), 2018, Predictive Control Algorithms in Human Balancing, München, GRIN Verlag, https://www.grin.com/document/541268
