Dynamic Simulation of Human Gait Model With Predictive Capability

2018 ◽  
Vol 140 (3) ◽  
Author(s):  
Jinming Sun ◽  
Shaoli Wu ◽  
Philip A. Voglewede
Keyword(s):  
Feedback Control ◽  
Predictive Control ◽  
Degrees Of Freedom ◽  
Human Subjects ◽  
Human Gait ◽  
Control Input ◽  
Plant Model ◽  
Control Approach ◽  
The Central Nervous System ◽  
Using Data

In this paper, it is proposed that the central nervous system (CNS) controls human gait using a predictive control approach in conjunction with classical feedback control instead of exclusive classical feedback control theory that controls based on past error. To validate this proposition, a dynamic model of human gait is developed using a novel predictive approach to investigate the principles of the CNS. The model developed includes two parts: a plant model that represents the dynamics of human gait and a controller that represents the CNS. The plant model is a seven-segment, six-joint model that has nine degrees-of-freedom (DOF). The plant model is validated using data collected from able-bodied human subjects. The proposed controller utilizes model predictive control (MPC). MPC uses an internal model to predict the output in advance, compare the predicted output to the reference, and optimize the control input so that the predicted error is minimal. To decrease the complexity of the model, two joints are controlled using a proportional-derivative (PD) controller. The developed predictive human gait model is validated by simulating able-bodied human gait. The simulation results show that the developed model is able to simulate the kinematic output close to experimental data.

The Development of a Human Gait Model With Predictive Capability and the Simulation of Able-Bodied Gait

2015 ◽  
Author(s):  
Jinming Sun ◽  
Shaoli Wu ◽  
Philip A. Voglewede
Keyword(s):  
Control Algorithm ◽  
Human Subjects ◽  
Human Gait ◽  
Control Input ◽  
Trial And Error ◽  
Predictive Capability ◽  
Gait Simulation ◽  
Plant Model ◽  
The Central Nervous System ◽  
The Cost

The development of current prostheses and orthoses typically follows a trial and error approach where the devices are designed based on experience, tried on human subjects and then redesigned iteratively. This design approach is costly, risky and time consuming. A predictive human gait model is desired such that prostheses can be virtually tested so that their performance can be predicted qualitatively, the cost can be reduced, and the risks can be minimized. The development of such a model is explained in this paper. The developed model includes two parts: a plant model which represents the forward dynamics of human gait and a controller which represents the central nervous system (CNS). The development of the plant model is explained in a different paper. This paper focuses on the control algorithm development and able-bodied gait simulation. The controller proposed in this paper utilizes model predictive control (MPC). MPC uses an internal model to predict the output in advance, compare the predicted output to the reference, and optimize control input so that the error between them is minimal. The developed predictive human gait model was validated by simulating able-bodied human gait. The simulation results showed that the controller is able to simulate the kinematic output close to experimental data.

scholarly journals Modeling and Control of Crane Overload Protection During Marine Lifting Operation Based on Model Predictive Control

2017 ◽  
Author(s):  
Zhengru Ren ◽  
Roger Skjetne ◽  
Zhen Gao
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Pid Controller ◽  
Degrees Of Freedom ◽  
Multiple Shooting ◽  
Control Input ◽  
Lumped Mass ◽  
Modeling And Control ◽  
Overload Protection ◽  
And Control

This paper deals with a nonlinear model predictive control (NMPC) scheme for a winch servo motor to overcome the sudden peak tension in the lifting wire caused by a lumped-mass payload at the beginning of a lifting off or a lowering operation. The crane-wire-payload system is modeled in 3 degrees of freedom with the Newton-Euler approach. Direct multiple shooting and real-time iteration (RTI) scheme are employed to provide feedback control input to the winch servo. Simulations are implemented with MATLAB and CaSADi toolkit. By well tuning the weighting matrices, the NMPC controller can reduce the snatch loads in the lifting wire and the winch loads simultaneously. A comparative study with a PID controller is conducted to verify its performance.

Improvement of a Forward Dynamic Predictive Human Gait Model

2019 ◽  
Author(s):  
Jessica B. Thayer ◽  
Philip A. Voglewede
Keyword(s):  
Predictive Control ◽  
Metabolic Cost ◽  
Metabolic Energy ◽  
Performance Criteria ◽  
Human Gait ◽  
Optimization Criteria ◽  
Optimization Framework ◽  
Normal Gait ◽  
The Central Nervous System ◽  
Improved Performance

Abstract Lack of understanding of human gait is detrimental to the development of gait related treatments and devices. This study improves a dynamic, predictive model of human gait which uses model predictive control (MPC) to replicate the control of the central nervous system (CNS). In this work, improved performance criteria, including metabolic cost and dynamic effort, are developed using an existing optimization framework to better mimic control of the CNS. Consistent with existing literature, incorporating dynamic effort and COM energy into the objective function improved gait simulations. This study also demonstrates COM energy and dynamic effort can both be used to predict metabolic energy consumption, which is likely the primary optimization criteria in normal gait generation.

scholarly journals Model Predictive Control of Robotic Grinding Based on Deep Belief Network

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-12
Author(s):  
Shouyan Chen ◽  
Tie Zhang ◽  
Yanbiao Zou ◽  
Meng Xiao
Keyword(s):  
Feedback Control ◽  
Model Predictive Control ◽  
Predictive Control ◽  
Model Simulation ◽  
Deep Belief Network ◽  
Model Parameters ◽  
Feed Forward ◽  
Belief Network ◽  
Control Approach ◽  
Robotic Grinding

Considering the influence of rigid-flexible dynamics on robotic grinding process, a model predictive control approach based on deep belief network (DBN) is proposed to control robotic grinding deformation. The rigid-flexible coupling dynamics of robotic grinding is first established, on the basis of which a robotic grinding prediction model is constructed to predict the change of robotic grinding status and perform feed-forward control. A rolling optimization formula derived from the energy function is also established to optimize control output in real time and perform feedback control. As the accurately model parameters are hard to obtain, a deep belief network is constructed to obtain the parameters of robotic grinding predictive model. Simulation and experimental results indicate that the proposed model predictive control approach can predict abrupt change of robotic grinding status caused by deformation and perform a feed-forward and feedback based combination control, reducing control overflow and system oscillation caused by inaccurate feedback control.

scholarly journals Perspective on musculoskeletal modelling and predictive simulations of human movement to assess the neuromechanics of gait

2021 ◽  
Vol 288 (1946) ◽  
pp. 20202432
Author(s):  
Friedl De Groote ◽  
Antoine Falisse
Keyword(s):  
Musculoskeletal System ◽  
Degrees Of Freedom ◽  
Human Movement ◽  
Human Gait ◽  
Complex Interactions ◽  
Musculoskeletal Models ◽  
The Central Nervous System ◽  
Fundamental Insight ◽  
Insight Into ◽  
Predictive Simulations

Locomotion results from complex interactions between the central nervous system and the musculoskeletal system with its many degrees of freedom and muscles. Gaining insight into how the properties of each subsystem shape human gait is challenging as experimental methods to manipulate and assess isolated subsystems are limited. Simulations that predict movement patterns based on a mathematical model of the neuro-musculoskeletal system without relying on experimental data can reveal principles of locomotion by elucidating cause–effect relationships. New computational approaches have enabled the use of such predictive simulations with complex neuro-musculoskeletal models. Here, we review recent advances in predictive simulations of human movement and how those simulations have been used to deepen our knowledge about the neuromechanics of gait. In addition, we give a perspective on challenges towards using predictive simulations to gain new fundamental insight into motor control of gait, and to help design personalized treatments in patients with neurological disorders and assistive devices that improve gait performance. Such applications will require more detailed neuro-musculoskeletal models and simulation approaches that take uncertainty into account, tools to efficiently personalize those models, and validation studies to demonstrate the ability of simulations to predict gait in novel circumstances.

Feed-forward frictional-order proportional–integral–derivative-based feedback control of a piezoactuated microposition stage using an extended unparallel Prandtl–Ishlinskii hysteresis compensator

2018 ◽  
Vol 233 (8) ◽  
pp. 2867-2878 ◽  
Author(s):  
Lina Hao ◽  
Jinhai Gao ◽  
Hongpeng Che
Keyword(s):  
Feedback Control ◽  
Degrees Of Freedom ◽  
Resonant Mode ◽  
Feedback Controller ◽  
Proportional Integral Derivative ◽  
Recent Past ◽  
Feed Forward ◽  
Proportional Integral Derivative Controller ◽  
Control Approach ◽  
Proportional Integral

In the recent past, it has been observed that flexure-based microposition stages with a large workspace and high motion precision are gaining popularity for performing practical micromanipulation tasks. Thus, a piezoactuated flexible two-degrees-of-freedom micromanipulator integrated with a pair of displacement amplifiers is developed. To enhance the practical positioning performance of the micromanipulator, this paper proposes a feed-forward frictional-order proportional–integral–derivative based feedback control approach to eliminate the undesired resonant mode of a piezoactuated microposition stage to satisfy the accuracy of the system. The control approach is composed of the integration inverse feed-forward compensator, the feedback controller, and the frictional-order proportional–integral–derivative controller. The integration inverse feed-forward compensator with an extended unparallel Prandtl–Ishlinskii model is introduced for addressing the nonlinearity of the piezoactuated microposition stage, leading to an approximately linear system. When all the roots of the system characteristic equation are negative real numbers or have negative real parts, the feedback controller is guaranteed to have tracking stability. Next, a frictional-order proportional–integral–derivative controller is designed to enhance the tracking performance of the microposition stage. Finally, comparative experiments with the conventional proportional–integral–derivative controller are performed, revealing that the practical positioning performance has been increased by nearly 35%. The experimental results demonstrate that the performance with the frictional-order proportional–integral–derivative+feedback controller is improved significantly.

Dynamic Simulation of Human Gait Using a Combination of Model Predictive and PID Control

2014 ◽  
Author(s):  
Jinming Sun ◽  
Philip A. Voglewede
Keyword(s):  
Control System ◽  
Predictive Control ◽  
Pid Control ◽  
Muscle Activity ◽  
A Priori ◽  
Human Gait ◽  
Proportional Integral Derivative ◽  
Biomechanical Models ◽  
Long Period ◽  
The Central Nervous System

Human gait studies have not been applied frequently to the prediction of the performance of medical devices such as prostheses and orthoses. The reason is most biomechanics simulations require experimental data such as muscle activity or joint moment information a priori. In addition, biomechanical models are normally too complicated to be adjusted and these simulations normally take a long period of time to be performed which makes testing of various possibilities time consuming; therefore they are not suitable for prediction purpose. The objective of this research is to develop a control oriented human gait model that is able to predict the performance of prostheses and orthoses before they are experimentally tested. This model is composed of two parts. The first part is a seven link nine degree-of-freedom (DOF) plant to represent the forward dynamics of human gait. The second part is a control system which is a combination of Model Predictive Control (MPC) and Proportional-Integral-Derivative (PID) control. The purpose of this control system is to simulate the central nervous system (CNS). This model is sufficiently simple that it can be simulated and adjusted in a reasonable time, while still representing the essential principles of human gait.

scholarly journals Model Predictive Control for Modeling Human Gait Motions Assisted by Vicon Technology

2020 ◽  
Vol 53 (5) ◽  
pp. 589-600
Author(s):  
Vu Trieu Minh ◽  
Mart Tamre ◽  
Victor Musalimov ◽  
Pavel Kovalenko ◽  
Irina Rubinshtein ◽  
...  
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Human Motion ◽  
Human Gait ◽  
Movement Trajectory ◽  
Tracking Errors ◽  
Human Movements ◽  
Study Results ◽  
The Central Nervous System ◽  
Human Muscles

Human muscles and the central nervous system (CNS) play the key role to control the human movements and activities. The human CNS determines each human motion following three steps: estimation of the movement trajectory; calculation of required energy for muscles; then perform the motion. In these three step tasks, the human CNS determines the first two steps and the human muscles conduct the third one. This paper efforts the use of model predictive control (MPC) algorithm to simulate the human CNS calculation in the case of gait motion. We first build up the human gait motion mathematical model with 5-link mechanism. This allows us to apply MPC to calculate the optimal torques at each joint and optimal trajectory for muscles. Outcomes of simulations simultaneously are compared with the real human movements captured by the Vicon motion capture technology which is the novelty of this study. Results show that tracking errors are not excessed 7%.

Sign in / Sign up

Export Citation Format

Share Document