Mechatronics 24 (2014) 168–176

Contents lists available at ScienceDirect

Mechatronics

journal homepage: www.elsevier .com/ locate /mechatronics

Human–robot cooperation control based on a dynamic modelof an upper limb exoskeleton for human power amplification

0957-4158/$ - see front matter � 2014 Elsevier Ltd. All rights reserved.http://dx.doi.org/10.1016/j.mechatronics.2014.01.007

⇑ Corresponding author. Tel.: +82 31 400 4062.E-mail address: cshan@hanyang.ac.kr (C.-S. Han).

Hee-Don Lee a, Byeong-Kyu Lee a, Wan-Soo Kim a, Jung-Soo Han b, Kyoo-Sik Shin c, Chang-Soo Han c,⇑a Department of Mechanical Engineering, Hanyang University, Seoul, Republic of Koreab Department of Mechanical System Engineering, Hansung University, Seoul, Republic of Koreac Department of Robot Engineering, Hanyang University, Gyeonggi-do, Republic of Korea

a r t i c l e i n f o

Article history:Received 8 November 2012Accepted 12 January 2014Available online 5 February 2014

Keywords:Upper limb exoskeletonHuman–robot cooperation controlDynamic model based controlPower assistive robotPhysical human–robot interaction

a b s t r a c t

In this paper, we propose a human–robot cooperation controller for the motion of the upper limb exo-skeleton. The system permits three degrees of freedom using an electrical actuator that is mainly con-trolled by force sensor signals. These signals are used to generate the torque required to drive theexoskeleton. However, singularities exist when the force signals in the Cartesian coordinate system aretransformed into torques in the joint coordinate system. Therefore, we apply the damped least squaresmethod. When handling a load, torque compensation is required about its mass. Therefore, we installeda force sensor at the point of the robot’s end-effector. It measures the forces between the exoskeleton andthe load. Then, these forces are used to compensate within a static model for handling loads. Weperformed control stability and load handling experiments to verify the effectiveness of the controller.Via these, we confirmed the effectiveness of the proposed controller.

� 2014 Elsevier Ltd. All rights reserved.

1. Introduction

The robot exoskeleton system is a human–robot cooperationsystem that enhances the power of the wearer in various environ-ments. The human operator remains in charge of the position con-trol, contextual perception, and motion signal generationsupported by the robot’s artificial intelligence [1–3]. This systemis in the form of a mechanical structure that is combined withthe exterior of the human body to improve the muscular powerof the wearer [4]. Robot exoskeletons have been actively studiedin the United States, Japan, and Europe since the 1990s and theyare now being researched for applications in various industries,including the military, medicine, and rehabilitation.

The purpose and application target determine the appropriatecontrol method. Various control methods have been proposed toachieve these purposes. For example, Kazerooni developed a six-degree of freedom (DOF) upper-limb muscle enhancer that useda hydraulic system. This system measures the force between thehuman and the robot using a multi-axis force sensor and uses itas an input for the robot controller [5]. Later, Caldwell developeda seven-DOF upper-limb exoskeleton, and it acquired the intentionof the wearer’s motion by using a force sensor [6]. Additionally, Na-gai developed an eight-DOF upper-limb exoskeleton that was dri-ven by an electric motor. It measures the force and moment

generated between the human operator and the exoskeleton byusing a force sensor; this information is then used for control [7].Other investigations include that of Cavallaro, who estimated thetorque of the upper limb joint by using an electromyography(EMG) sensor and applied it to a seven-DOF upper-limb joint mus-cular strength supplement robot [8]. Kiguchi also developed a four-DOF upper-limb exoskeleton by applying a motion intention signalacquired from an EMG sensor and a force sensor to a neuro-fuzzycontroller [9]. Andreasen later developed a controller that gener-ates motion torque with one DOF to supplement muscular strengthwith an EMG sensor and a torque sensor signal [10].

As robot exoskeletons are combined with humans to form aclosed loop in terms of control, both the wearer and the robot exo-skeleton must be considered when developing a controller. Thecontrol method for a robot exoskeleton system is based on the hu-man–robot cooperation control, which is, in turn, based on hu-man–robot interaction (HRI). HRI can be divided into cognitiveHRI (cHRI) and physical HRI (pHRI). cHRI refers to interactions thatoccur via the influence of the robot during human cognitive pro-cesses. pHRI refers to the physical interaction with a robot that oc-curs via human motions as a result of an action during cognitiveprocesses [11]. Fig. 1 illustrates the human–robot interaction with-in an exoskeleton with forces and signals.

The cHRI-based system measures the electric signals from thecentral nervous system (CNS) to the musculoskeletal system ofthe human and subsequently uses them as input information forthe robot controller [8–10]. Thus, since it can identify the human’s

Fig. 1. Concept of the human–robot interaction and the human–robot interface.

Fig. 2. HEXAR system for human power augmentation.

H.-D. Lee et al. / Mechatronics 24 (2014) 168–176 169

intent before the occurrence of an actual motion of the wearer andpredict the required torque for the motion of human joints, it isprimarily used in robot exoskeletons for power assistance. On theother hand, the pHRI-based system measures force or positionchanges that result from the motions of the human musculoskele-tal system and uses them as input information for the robotcontroller [1,5–7,12–14]. Thus, pHRI-based controllers are advan-tageous in that they are simpler than cHRI signal measurements,ensure signal reproducibility, and can acquire reliable signals.

In general, pHRI-based upper-limb exoskeleton systems directlymeasure human–robot interaction force using a force sensor andthen use the information as input for a controller [1,12–14]. Leeproposed a control method through generation of desired positionsof the exoskeleton by modeling human–robot interaction into vir-tual mechanical impedance [1]. Yu conducted modeling of humansinto impedance and proposed PID admittance control that gener-ated desired positions with interaction force [12]. Miranda alsoconducted modeling of human–robot interactions using a springto measure a wearer’s motion intentions; subsequently, Mirandaproposed an exoskeleton controller based on sliding model control[13]. As another example, Ozhul generated reference motions ofthe exoskeleton using an admittance filter and controlled the exo-skeleton using a robust position controller [14]. These systems usemodels of human–robot interaction or humans to create desiredmotions to move the exoskeleton through human–robot interac-tion force. However, modeling of humans by generalizing is impos-sible. Parameters of human models with experimental methodsusing virtual models such as an impedance or admittance filterare required. For industrial application, the method of using humanmodels, which are difficult to generalize, is not appropriate. Manyexisting controllers of the upper-limb exoskeleton have been stud-ied for rehabilitation purposes, but few considerations have beenmade of their application to industry.

As the purpose of this study is to enhance an industrial worker’smuscular strength, the object system must secure a reliable motionintention signal. Lee proposes a dynamic model-based controllerfor the upper limb exoskeleton that is suitable for this purpose[15], and this study expands the performance verification, fromthe wearer’s viewpoint, of this controller. Therefore, this studydoes not use a human model to generate the desired motions ofthe exoskeleton. Instead, this paper proposes a force controllerbased on a dynamic model. This controls the exoskeleton so thatthe interaction force between a human and the exoskeleton be-comes zero. Compensation for external load and safety at a singu-lar point of the exoskeleton was considered during the designprocess in order to make the controller industry-suitable. Prior to

the controller design, Section 2 introduces the upper-limb exoskel-eton to which the controller will be applied and performs modelingof the controller. Section 3 examines the design of a human–robotcooperation controller based on a dynamic model that uses inter-action force between a wearer and the exoskeleton as control feed-back. Finally, Section 4 verifies the performance of the controllerthrough an experiment.

2. Upper limb exoskeleton model

2.1. Exoskeleton system

The robot exoskeleton used in this study has two DOFs in theshoulder and one DOF in the elbow. The actuators support shoul-der Extension/Flexion (E/F), shoulder Abduction/Adduction (Ab/Ad), and an elbow E/F DOF. Moreover, the exoskeleton is manufac-tured to fit the general body size of males aged 20–29. The shoul-der and elbow Range Of Motion (ROM) of the proposed robotexoskeleton was based on the data for a standard Korean male.The exoskeleton robot used in this research is designed so that itcan connect to the human–robot interface. In addition, the linklength of this robot is able to adjust to the rotation center of thehuman joint and the exoskeleton joint so that the two may inte-grate. A lack of integration may be caused by a difference in a per-son’s physical dimensions or by disparities between the rotationcenters of the human and robot joints. The proposed system hastwo points of contact: the back and the hand of the user. A well-de-signed human–robot interface is important for the convenienceand effectiveness of the device. The control signal of the exoskele-ton uses the relative force between the user and the exoskeleton,which is measured by the three-axes force sensor (Fig. 2). Table

Fig. 3. Coordination system and link parameters of the HEXAR.

Table 2Denavit-Hartenberg parameter of the HEXAR

i ai�1 ai�1 di qi

1 0 0 0 q1

2 �90� 0 0 q2

3 0 l1 0 q3

170 H.-D. Lee et al. / Mechatronics 24 (2014) 168–176

1 shows the specifications of our design, which is called Hanyanguniversity EXoskeleton Assistive Robot (HEXAR).

2.2. Exoskeleton modeling

Handling heavy loads in industrial settings is mostly executedby the front side of the human body, and the exoskeleton hassimilar degrees of freedom to those of the human body. Therefore,we set the exoskeleton’s DOF arrangement as shown in Fig. 3 forthe shoulder E/F, shoulder Ab/Ad, and elbow E/F motions that aremostly used when handling objects. The position at which theexoskeleton is joined with the human body is Ph, while Pe isthe position where the exoskeleton is joined with the object. TheDenavit-Hartenberg parameter for robot kinematics analysis isshown in Table 2, and we performed a forward kinematics analysisto calculate the positions of Ph and Pe in a base frame.

The dynamic model of the exoskeleton is designed usingLagrangian dynamics [16]. Eq. (1) is the general form of Lagrangiandynamics, where s is 3� 1 vector of actuator torques, q is 3� 1vector of generalized coordinates, k is kinetic energy, and p ispotential energy.

The equation for the robot exoskeleton’s kinetic energy isshown as Eq. (2), and the potential energy is shown in Eq. (3). Here,i is the link index, mri

is the mass of the exoskeleton link, PCiis the

center of mass, and Ci Ii is the inertia tensor. All these elementsare derived from the CAD data. In addition, vCi

is the velocity ofthe link, and ixi is the angular velocity of the link. Eq. (4) is derivedby expressing Eq. (1) with the robot’s inertia matrix MðqÞ torque byCoriolis and centrifugal force Cðq; _qÞ, and torque by gravity GðqÞ.

s ¼ ddt

@k@ _q

� �� @k

@q

� �þ @p@q

ð1Þ

k ¼Xn

i¼1

ki ði ¼ 1;2;3Þ

where ki ¼12

mrivT

CivCiþ 1

2ixT

iCi Ii

ixi

ð2Þ

p ¼Xn

i¼1

pi ði ¼ 1;2;3Þ

where pi ¼ �mri0gT 0PCi

ð3Þ

s ¼ MðqÞ€qþ Cðq; _qÞ _qþ GðqÞ ð4Þ

3. Controller design

3.1. Human–robot cooperation controller

The purpose of this study is to develop an upper limb robot exo-skeleton controller that enables the operation of the robot with thesame force regardless of the weight of the object handled. The con-troller requires joint torque compensation for the object weight inorder to accomplish this [5,17]. The upper limb exoskeleton is at-tached only to the human operator’s back and hands. Motion inten-

Table 1Specifications of the HEXAR.

Specification Value Unit

Degree of freedom 6 DOFExoskeleton weight 26 kgPayload 300 NPower consumption 1.2 kWMaximum joint velocity 35 RPM

tion is identified by using a 3-axis force sensor mounted at thehandle, which is used as the controller input. Motion intentionmeasures the interaction force between the human and the robot,and the robot controller operates the robot so that this force con-verges to zero. The controller compensates for the influence ofthe load by installing a 3-axis force sensor at the object joint sec-tion in order to compensate for the torque of the object load.

Fig. 4 is the block diagram of the dynamic model based forcecontroller. From this figure, fhr is the interaction force betweenthe exoskeleton robot and the wearer, and fd is the value of thecontrol goal. fd is set to zero because it is believed that the robotfollows the wearer’s motion well when the interaction force be-tween the robot and the wearer is zero. The position pc that theend effector should move to in order to compensate for the differ-ence between fd and fhr , is as follows.

pc ¼ K�1P KFDf þ KI

Z t

0Dfdt

� �where Df ¼ fd � fhr

ð5Þ

Here, KF is the force sensor gain that is controllable by the wearer,and KP and KI are the proportional gain and integral gain of theforce controller, respectively. The linear acceleration in Eq. (6) isconverted to an angular acceleration for each joint by using Eq.(7). This, in turn, is used as a torque input for the end effector in or-der to move to the target position by using the inverse dynamics ofEq. (8).

a ¼ �KD _pe þ KPDpce þ KIP

Z t

0Dpcedt

where Dpce ¼ pc � pe

ð6Þ

a ¼ J�1h ðqÞða� _Jhðq; _qÞ _qÞ ð7Þ

s ¼ MðqÞ€qþ Cðq; _qÞ _qþ GðqÞ þ JThfhr ð8Þ

Fig. 4. The block diagram of the human–robot cooperation controller.

Fig. 5. The block diagram of the human–robot cooperation controller for the external load compensation

Fig. 6. Experimental setup for the verification of the human–robot cooperationcontroller.

H.-D. Lee et al. / Mechatronics 24 (2014) 168–176 171

3.2. Kinematic singularity avoidance

When the exoskeleton approaches the kinematic singularity, J�1h

of Eq. (7) becomes the singular matrix, which makes it unable tocalculate and causes the system to become unstable. In this case,the exoskeleton malfunctions, vibrating and possibly of collidingwith nearby people or objects. To overcome this singularity, J�1

h isreplaced with Jacobian’s pseudo-inverse. Eq. (9) is acquiredthrough the Singular Value Decomposition (SVD) of the Jacobianmatrix in order to calculate Jacobian’s pseudo-inverse, and theJacobian matrix’s pseudo-inverse can be calculated using Eq. (10).

Jh ¼ URVT ¼Xr

i¼1

riuivTi ð9Þ

where U is the m�m orthogonal matrix, R is m�m diagonal ma-trix, V is n� n orthogonal matrix, r is rank of Jacobian matrix, ui isith column of U, ri is ith singular value on the main diagonal of Rand v i is the ith column of V.

Jyh ¼Xr

i¼1

r�1i v iuT

i ð10Þ

However, r�1i returns a very large value when the singularity

approaches zero, which still indicates system instability. Hence,the DLS (Damped Least Squares) method is proposed in order toavoid this problem [18].

The DLS method prevents J�1h from becoming very large by

replacing it with the Singularity Robust Inverse (SRI), and it com-pensates for the disadvantage of the pseudo-inverse method. TheSRI can be acquired by calculating the smallest vector Dh that min-imizes Eq. (11) [19].

kJhDek2 þ kkDhk2 ð11Þ

Here, k is a non-zero damping constant. It helps the system to oper-ate better when its value is larger, but it has characteristics thatlower the converging speed when its value is too large. Eq. (11) isthe same as calculating the minimum value of Eq. (12).

Jh

kE

� �Dh�

e

0

� ��������� ð12Þ

Jh

kE

� �T Jh

kE

� �Dh ¼

Jh

kE

� �T e

0

� �ð13Þ

Dh ¼ JTh JhJT

h þ k2E� ��1

e ð14Þ

Therefore, Eq. (14) is acquired by solving Eq. (12) after trans-forming it into a normal equation using the same method as wasused with Eq. (13). Here, JhJT

h þ k2E is non-singular. Finally, theSRI is shown as in Eq. (15).

Fig. 7. Human-following experimental results of the human–robot cooperation controller.

172 H.-D. Lee et al. / Mechatronics 24 (2014) 168–176

J�h ¼ JTh JhJT

h þ k2E� �2

ð15Þ

Here it can be expressed as Eq. (16) by performing an SVD for J�h.When comparing Eq. (13) with Eq. (10), the r2

i =r2i þ k of Eq. (13)

returns a relatively small value even though the singularity ap-proaches 0, and can therefore avoid system instability. Eq. (16)can be derived by substituting the calculated J�h instead of J�1

h intoEq. (14).

a ¼ J�hðqÞ a� _Jhðq; _qÞ _q� �

ð16Þ

Here, the damping constant k is set to increase its value as theexoskeleton’s end effector approaches the singular point as shownin Eq. (17) [20]. x is the manipulability measure, and it is definedby Eq. (18). x0 and kmax are the threshold value and the maximumvalue of the manipulability measure and damping constant,respectively.

k ¼ kmax 1� xx0

� �if x < x0

0 otherwise

(ð17Þ

Fig. 8. The performance comparison between the controller using the pseudo-inverse method and the controller using the DLS method at a singular point.

H.-D. Lee et al. / Mechatronics 24 (2014) 168–176 173

x ¼ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffidetðJJTÞ

qð18Þ

3.3. External load compensation

Torque compensation is needed for the external load when therobot exoskeleton handles an object load, so we added a load com-

pensator to the existing force controller. Fig. 5 is the block diagramof the dynamic model-based controller that enables load compen-sation for the object. The interaction force between the object loadand the robot exoskeleton, fe, can be expressed as the sum of theforce caused by the object’s self load and the force caused by iner-tia, as shown in Eq. (19).

fe ¼ fload þ finertia ð19Þ

Fig. 9. The experimental results of handling 0 kg, 2.5 kg, and 5 kg objects to verify the effects of external load compensation.

174 H.-D. Lee et al. / Mechatronics 24 (2014) 168–176

Therefore, the torque needed to compensate for the influence ofthe object load, sload is shown in Eq. (20).

sload ¼ �JTe fe ð20Þ

Here, Je is the Jacobian matrix for the position pe where the objectload is joined to the robot exoskeleton. By applying the calculatedcompensation torque sload to the inverse dynamics, we can obtainthe final torque required for each joint of the robot exoskeletonwhen handling the object load as shown in Eq. (21).

s ¼ MðqÞ€qþ Cðq; _qÞ _qþ GðqÞ þ JThfhr þ sload ð21Þ

4. Experiment and results

4.1. Human–robot cooperation controller

Here, the human–robot cooperation controller is based on forcecontrol, and it controls the exoskeleton to create the interactionforces ðfhrÞ, produced by the motions of a wearer and his/her robot.To verify this, an experiment was performed to measure ph and fhr

when an exoskeleton wearer moves from Target 1 to Target 2.Fig. 6 displays the experimental system and methods in diagramform. Here, ph is the location at which the wearer’s hand and exo-skeleton are combined, and the joint angle ðqÞ that is measured by

the encoder attached to the exoskeleton is calculated via the baseframe using forward kinematics.

In addition, fhr is the human–robot interaction force and is mea-sured using the three-axis load cell attached at the point, ph. There-fore, fhr is a force vector provided by the wearer’s motions to therobot exoskeleton, and information on the wearer’s motions maybe determined through the magnitude and direction of this forcevector. The controller proposed by this study controls the exoskel-eton such that fhr becomes zero. Therefore, the exoskeleton movesin the direction of the measured fhr vector, and the distance for theexoskeleton to move is determined by the size of the fhr vector.Therefore, this experiment hypothesized that the direction of thefhr vector is the same as the direction in which the wearer intendsto move. Additionally, it posited that the magnitude of the fhr vec-tor is the magnitude that is intended by the wearer for the motion.

Fig. 7 presents the experimental results for the exoskeleton towhich the human–robot cooperation controller was applied. Panels(a) and (b) indicate both the location of ph and the direction of fhr

when the exoskeleton wearer moves from Target 1 to Target 2.As shown in (a), ph moves in a similar direction to that of fhr . Themagnitude of the fhr vector and the velocity of the exoskeletonare indicated by (b). About 0.05 s after the first occurrence of fhr ,the exoskeleton started moving, and it was found that an increasedfhr resulted in a corresponding increase in the velocity of theexoskeleton.

Fig. 10. Human power of 0 kg, 2.5 kg, and 5 kg objects at the point of lifting.

H.-D. Lee et al. / Mechatronics 24 (2014) 168–176 175

4.2. Kinematic Singularity Avoidance

To verify the effect of the DLS method, we experimented byoperating both the DLS method-applied controller and the pseu-do-inverse method-applied controller in the range that includedthe singular point. The experiments were performed by liftingthe arm so as to pass the singular point, where the joint angle ofthe arm was 0� and return to the original point. Fig. 8(a) showsthe result that measures the angles of each joint when using thepseudo-inverse method-applied controller. During the experiment,system instability appeared. Each joint angle vibrated after 2.5 s,when the end effector approached the singular point, where alljoint angles become 0�. Fig. 8(b) shows the result that measuresthe angles of each joint when using the DLS method-applied con-troller. When compared with Fig. 8(a) and (b) indicates that thereis almost no vibration when the robot’s end effector approachesthe original point. This verifies that the DLS method-applied con-troller ensures control stability at the singular point.

4.3. External load compensation

To verify the load handling performance of the dynamic model-based controller, we compared the behavior of the robot end effec-tor, with the same input, when the external load was compensatedfor and not compensated for at different weight loads. Experi-ments were performed with load weights of 0 kg, 2.5 kg, and5 kg, and the input signal was a 10 N step signal in the z-directionfrom the base frame of the robot exoskeleton. Fig. 9 shows the x-axis and the z-axis change in the robot end effector with referenceto the base frame: Fig. 9(a) is without compensation for the exter-nal load, and Fig. 9(b) is with compensation for the external load.By comparing the two cases, we could verify that they show al-most the same behavior for various external loads when the exter-nal load is compensated for. This means that the wearer canoperate the robot with the same force, regardless of the weightof the object. As a result, it indicates that the wearer’s muscularstrength has been increased because all forces, except the manip-ulating force, came from the robot. In other words, when the robotwas operated with the same force as the inputs, the wearer wasable to move different object loads for the same distance. There-fore, we can conclude that the robot augmented the muscularstrength of the wearer. In order to verify the effects of the increasein the muscular strength of the exoskeleton wearer, the wearer’spower P ¼ fhr vhð Þwas measured when the wearer lifted objectswhose weights were 0 kg, 2.5 kg, and 5 kg. Here, fhR was the inter-action force between the human and the exoskeleton, meaning themagnitude of the force required for the human to control the exo-skeleton. In addition, vh is the velocity at the point at which thehuman arm and the exoskeleton are fastened. Fig.10(a) show theresult of the measurement of human power according to theweights of the external loads. It is impossible for the wearer tomanipulate the exoskeleton so as to have identical values of fhr

and vh across individual experiments. Therefore, the overall ten-dencies were examined by calculating the peak power Pmax ¼ðmax½PðtÞ�Þ and average power Pavg ¼ 1

T

R T0 PðtÞdt

� �.

Fig. 10(b) shows the peak power and average power at the pointof lifting 0 kg, 2.5 kg, and 5 kg objects. As the weights of the exter-nal objects increased from 0 kg to 2.5 kg to 5 kg, the average powerrose from 3.04 W to 4.16 W to 6.06 W. In addition, the peak powerrose from 6.34 W to 7.79 W to 10.23 W.

5. Conclusion

In this paper, we developed a human–robot cooperation con-troller for upper limb exoskeletons, the purpose of which is to aug-ment the wearer’s power. We verified its effects throughexperiments. This controller is based on a dynamic model of forcecontrol and was designed to easily reflect the motion intent of thewearer by enabling the control to create an interaction force ofzero between the human and the exoskeleton. In the experimentsto verify the direction and magnitude of the human–robot interac-tion force vector, the trajectory and velocity of the exoskeletonend-effector were compared. As a result, the exoskeleton movedin a similar direction to that of the force vector, and a force vectorof greater magnitude resulted in a correspondingly faster move-ment. In addition, for the stability of the exoskeleton’s control ata singular point, singularity avoidance was applied by using theDLS method. To verify its effects, a comparative experiment wasconducted by applying the pseudo-inverse method and the DLSmethod to the same controller. Consequently, it was found thatwhen the pseudo-inverse method was employed, the exoskeleton’sjoint vibration was produced at a singular point. However, whenthe DLS method was employed, vibrations were restrained due todamping effects. Additionally, in order to enable the wearer to

176 H.-D. Lee et al. / Mechatronics 24 (2014) 168–176

handle objects of various weights while applying the same magni-tude of force, compensation for external loads was applied to thecontroller by using the value measured by a force sensor. To verifyits effects, a comparison was made between the non-compensatedand compensated exoskeleton after attaching a separate object tothe robot. As a result, when no load compensation was applied,the parameter of the exoskeleton’s dynamic model was changeddue to the weight of the attached object. Therefore, the same con-trol pressures did not generate the same control performances. Onthe other hand, when load compensation was applied, the steady-state error between the exoskeleton end-effector position with noobject attached and the exoskeleton end-effector position with2.5 kg and 5 kg objects attached was reduced to 0.01 m or less. Inconclusion, the human–robot cooperation controller suggested inthis study is considered appropriate for upper limb exoskeletonsthat are developed for the purpose of lifting heavy objects.

Acknowledgements

This research was partially supported by the robot R&D pro-gram of the Ministry of Knowledge and Economy (MKE), Korea.[No. 10035431, Development of wearable robot for industrial laborsupport] and partially supported by the Ministry of KnowledgeEconomy (MKE, Korea), under the Advanced Robot ManipulationResearch Center support program supervised by National IT Indus-try Promotion Agency (NIPA), NIPA-2013-H1502-13-1001.

References

[1] Lee H, Lee B, Kim W, Gil M, Han J, Han C. Human–robot cooperative controlbased on phri (physical human–robot interaction) of exoskeleton robot for ahuman upper extremity. Int J Prec Eng Manuf 2012;13(6):985–92.

[2] Lee H, Kim W, Han J, Han C. The technical trend of the exoskeleton robotsystem for human power assistance. Int J Prec Eng Manuf 2012;13(8):1491–7.

[3] Kim W, Lee S, Kang M, Han J, Han C. Energy-efficient gait pattern generation ofthe powered robotic exoskeleton using dme. In: 2010 IEEE/RSJ internationalconference on Intelligent Robots and Systems (IROS). IEEE; 2010. p. 2475–80.

[4] Yang C, Zhang J, Chen Y, Dong Y, Zhang Y. A review of exoskeleton-typesystems and their key technologies. Proc Inst Mech Eng Part C: J Mech Eng Sci2008;222(8):1599–612.

[5] Kazerooni H, Guo J. Human extenders. Trans–Am Soc Mech Eng J Dyn SystMeas Control 1993;115(2B):281.

[6] Caldwell D, Tsagarakis N, Kousidou S, Costa N, Sarakoglou I. soft exoskeletonsfor upper and lower body rehabilitation–design, control and testing. Int JHumanoid Rob 2007;4(3):549–74.

[7] Nagai K, Nakanishi I, Hanafusa H, Kawamura S, Makikawa M, Tejima N.Development of an 8 dof robotic orthosis for assisting human upper limbmotion. In: Proceedings 1998 IEEE international conference on robotics andautomation, 1998. IEEE; 1998. p. 3486–91.

[8] Cavallaro E, Rosen J, Perry J, Burns S. Real-time myoprocessors for a neuralcontrolled powered exoskeleton arm. IEEE Trans Biomed Eng2006;53(11):2387–96.

[9] Kiguchi K, Tanaka T, Fukuda T. Neuro-fuzzy control of a robotic exoskeletonwith emg signals. IEEE Trans Fuzzy Syst 2004;12(4):481–90.

[10] Andreasen D, Alien S, Backus D. Exoskeleton with emg based active assistancefor rehabilitation. In: 9th International Conference on Rehabilitation Robotics,2005. ICORR 2005. IEEE; 2005. p. 333–6.

[11] Pons J et al. Wearable robots: biomechatronic exoskeletons. Wiley OnlineLibrary; 2008.

[12] Yu W, Rosen J, Li X. Pid admittance control for an upper limb exoskeleton. In:American Control Conference (ACC), 2011. IEEE; 2011. p. 1124–9.

[13] Miranda ABW, Forner-Cordero A. Upper limb exoskeleton control based onsliding mode control and feedback linearization. In: Biosignals and BioroboticsConference (BRC), 2013 ISSNIP. IEEE; 2013. p. 1–6.

[14] Ozkul F, Barkana DE. Design and control of an upper limb exoskeleton robotrehabroby. In: Towards Auton Rob Syst. Springer; 2011. p. 125–36.

[15] Lee B, Lee H, Lee J, Shin K, Han J, Han C. Development of dynamic model-basedcontroller for upper limb exoskeleton robot. In: 2012 IEEE InternationalConference on Robotics and Automation (ICRA). IEEE; 2012. p. 3173–8.

[16] Craig J. Introduction to robotics: mechanics and control. Prentice Hall; 2004.[17] Hayashibara Y, Tanie K, Arai H. Design of a power assist system with

consideration of actuator’s maximum torque. In: Proceedings 4th IEEEinternational workshop on robot and human communication, 1995. RO-MAN’95 TOKYO. IEEE; 1995. p. 379–84.

[18] Buss S. Introduction to inverse kinematics with jacobian transpose,pseudoinverse and damped least squares methods, University of California,San Diego, Typeset manuscript; 2004. p. 1–19. <http://math. ucsd. edu/~sbuss/ResearchWeb>.

[19] Deo A, Walker I. Overview of damped least-squares methods for inversekinematics of robot manipulators. J. Intell. Rob. Syst. 1995;14(1):43–68.

[20] Nakamura Y, Hanafusa H. Inverse kinematic solutions with singularityrobustness for robot manipulator control. J. Dyn. Syst. Meas. Control1986;108(3):163–71.