Control of Human Movement:

from Physiology to Engineering

Antonie J. (Ton) van den Bogert

Parker-Hannifin Endowed Chair in Human Motion and Control

Department of Mechanical Engineering

Cleveland State University

http://hmc.csuohio.edu

IEEE Control Systems Society, Cleveland, 4/3/2015

Cost of transport (COT)

distance x weight

used nergy eCOT

COT = 0.2 COT = 3.0

Non-human movement: Honda Asimo

Petman (Boston Dynamics)

cost of transport unknown -- designed for tethered operation

Big Dog (Boston Dynamics)

3 MPG

340 MPG

hydraulic actuators powered by

internal combustion enging

Indego Exoskeleton (Parker-Hannifin)

electric motors powered by rechargeable batteries

Humans and animals

Humans and animals use 10-100 times less energy than machines

perform much better than machines

Why?

What can engineers learn?

Physiology (sensing/actuation/control)

Structure of muscles

2 μm

Overlapping proteins in muscle fiber

ADP

actin filament

myosin filament &

myosin head (crossbridge)

ATP

ATP (adenosine tri-phosphate) is

energy source of muscle

contraction

10 nm stroke length

Control of muscle force

Luigi Galvani (1737-1798)

Hz

Hz

Hz

Hz

twitches

fused tetanus

Frequency-modulated pulse trains

Motor unit recruitment

http://nmrc.bu.edu/tutorials/motor_units/

Motor unit:

A set of muscle fibers that are controlled by the same motor neuron

When force increases gradually, smallest motor units are recruited first

Spring-like mechanical properties

fiber length

forc

e

Control system needs to know

that force is position-dependent

Velocity dependence of force

Muscle (like any motor) has an optimal speed of operation

typically about 0.3 m/s (depending on muscle architecture)

Muscles vs. electric motors

Muscles

50 ms response time

slower to turn off

20-25% efficiency

low speed

high torque

"direct drive"

Electric motors

instantaneous response

90% efficiency

high speed

low torque

requires gearbox for

human-like applications

Feedback control

Physiological sensors for motion control

skin (stretch and pressure)

inner ear (inertial sensors)

muscles (stretch and force)

Nerve conduction velocity is about 100 m/s

Reflex loop delay 50 ms

Animals vs. machines Muscles

slow (50 ms response time)

inefficient (20-25% efficiency)

inconsistent (fatigue, variability)

Sensory system slow (50 ms signal delay)

inconsistent

Sprint running: foot is on the ground for only 100 ms!

Why do humans and animals perform so well? mechanical design (anatomy)

control (brain and spinal cord)

Anatomy (mechanical design)

Muscles often cross multiple joints

motor

motor

arm bones and muscles

typical robotic design

0 0.5 1 1.5 20

0.5

1

1.5

2

2.5

LENGTH

FO

RC

E

passive

25% activated

50% activated

75% activated

100% activated

Muscles are like springs

• Nonlinear spring

• Activation moves you to a different force-length curve

isometric

isotonic

Horse limbs

muscle

tendon

This makes control easier and saves energy

Muybridge, 1878

A spring-based exoskeleton

23

www.cadencebiomedical.com

What about motors?

Hanz Richter, CSU Robotics Lab:

"Semiactive virtual control"

Motors can

transfer energy

store energy

Energy-efficient robots

courtesy of Sangbae Kim, MIT

MIT Cheetah robot

uses less energy than animal

at same size and speed

Control (brain and spinal cord)

Hierarchical system

Brain: 1011 neurons

Spinal cord: 109 neurons

~10,000 PCs

Proportional-derivative control

Seems to be used by humans for simple

movements (reaching)

Also known as

Equilibrium point control (human motor control)

Impedance control, compliance control (robotics)

position

force

actuator with

elastic properties or

proportional control

external load

Control of standing Proportional-derivative control works well for small

perturbations

ADRC works well also

Larger perturbations require stepping move away from the desired posture!

proportional control will never do that

𝐱 =𝜃𝑎𝑛𝑘 𝜃𝑎𝑛𝑘𝜃ℎ𝑖𝑝 𝜃ℎ𝑖𝑝

u =𝑇𝑎𝑛𝑘𝑇ℎ𝑖𝑝

= −𝐊2x4𝐱

Walking is even more complex

Nonlinear dynamics

High-dimensional state space and control space

Limit cycle

Proportional control is not always "smart" enough

1650 RR uxu)f(x,x

Proportional-derivative control

designed by linearization

Muscles receive feedback from joint angles and angular velocities

Simulation test Human response to tripping

Do we need different control laws?

Do we need additional sensors?

Identification of human control

Human-based control:

We "map" the control system of our

volunteers, so we can copy it to a

robotic system

gain-scheduled

PD control

neural networks?

Virtual muscles

Electric motors can behave and feel like real muscles

motor

motor

=

joint

rotations

joint

torques

Summary

Animals and humans can perform amazing movements

more efficient than most robots

Muscles and nerves inefficient, slow and sloppy

Mechanical design is important can be virtualized with electric motors

Control is important learn from human data

Thank You!