# A study of cooperative control of self-assembling robots in space with experimental validation

**ABSTRACT** Modular self-assembling on-orbit robotic and satellite systems can be more reliable, have lower launch costs, and be more easily repaired and refueled. However, when individual modules assemble, many challenges and opportunities make the control of the assembled system complex. These issues include changes in inertial properties, and redundancy of actuators and sensors. Optimal control methods may be used to coordinate the control of the modules after assembly, insure good performance, and best utilize the combined resources of the assembly of modules. Simulation and experimental results compare this Cooperative algorithm's performance to that of an approach in which the control of the individual modules is not coordinated. Cooperative optimal control methods prove well-suited for controlling redundant, modular space systems.

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**ABSTRACT:**This work introduces a novel approach and its experimental verification for propellant sub-optimal multiple spacecraft assembly via a Linear Quadratic Regulator (LQR). The attitude dynamics of the spacecraft are linearized at each time step, about the current state vector, and the relative dynamics between two spacecraft are assumed as a double integrator. This allows for implementation in real-time of a LQR that computes the optimal gain matrix depending on the current phase of the spacecraft's mission. As a result, both the attitude and position are sub-optimally controlled. The presented logic compensates for the structural evolution related to an incremental assembly by updating the system's dynamics matrices. The actuators' reallocation and command of the assembled structure is dealt with through inter-robot wireless ad-hoc communication. Each spacecraft runs symmetric algorithms, differing only in the number of docking ports that each possesses for the mission, which are related to the number of assembling spacecraft and the final structure's desired shape. Once the spacecraft are assembled, one acts as master by performing the required navigation and control of the new structure through real-time wireless commanding of the other spacecraft's actuators. The improved third generation (3G-i) of spacecraft simulators developed at the Spacecraft Robotics Laboratory SRL of the Naval Postgraduate School (NPS) is presented to demonstrate experimental verification of the proposed methodology. Features of the (3G-i) robots include an unique customized construction of rapid prototyped thermoplastic (polycarbonate) that incorporates a lightweight modular design with a small footprint, thus maximizing the entire surface of the SRL robotic testbed.08/2009; - SourceAvailable from: scripts.mit.edu[Show abstract] [Hide abstract]

**ABSTRACT:**Modular self-assembling on-orbit robots have the potential to reduce mission costs, increase reliability, and permit on-orbit repair and refueling. Modules with a variety of specialized capabilities would self-assemble from orbiting inventories. The assembled modules would then share resources such as power and sensors. As each free-flying module carries its own attitude control actuators, the assembled system has substantial sensor and actuator redundancy. Sensor redundancy enables sensor fusion that reduces measurement error. Actuator redundancy gives a system greater flexibility in managing its fuel usage. In this paper, the control of self-assembling space robots is explored in simulations and experiments. Control and sensor algorithms are presented that exploit the sensor and actuator redundancy. The algorithms address the control challenges introduced by the dynamic interactions between modules, the distribution of fuel resources among modules, and plume impingement.Autonomous Robots 01/2011; 31:209-221. · 1.91 Impact Factor

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Abstract—Modular self-assembling on-orbit robotic and

satellite systems can be more reliable, have lower launch

costs, and be more easily repaired and refueled.

However, when individual modules assemble, many

challenges and opportunities make the control of the

assembled system complex. These issues include changes

in inertial properties, and redundancy of actuators and

sensors. Optimal control methods may be used to

coordinate the control of the modules after assembly,

insure good performance, and best utilize the combined

resources of the assembly of modules. Simulation and

experimental results compare

algorithm’s performance to that of an approach in which

the control of the individual modules is not coordinated.

Cooperative optimal control methods prove well-suited

for controlling redundant, modular space systems.

this Cooperative

I. INTRODUCTION

modular approach to self-assembling on-orbit robotic

systems and spacecraft has great potential for reducing

costs, increasing long-term reliability, and providing the

means for rapid repair and refueling. Such a spacecraft or

space robot would consist of an assembly of a number of

modules, each designed for specific tasks such as propulsion,

payload, fuel storage, etc. See Fig. 1. The modules within a

system could share resources such as power, sensors,

computational capabilities, and data. The modules’ small

size and the maintenance of an on-orbit module inventory

enables launching flexibility. Moreover, the possibility of

mass producing simple modules rather than individually

crafting unique satellites offers great potential for design and

production savings [1]. The modular approach also provides

redundancy. The ability to completely replace failed modules

will greatly increase robustness to failure [2], [3].

In this concept, system assembly takes place in orbit.

Hence, each module is required to carry sufficient attitude

control actuators (thrusters and reaction wheels) and sensors

to permit free-flying control and docking. These multiple

sensors give the assembled system substantial sensor

redundancy that can be used with sensor fusion techniques to

minimize sensor errors. The actuator redundancy can provide

C. Toglia is a PhD Candidate at “La Sapienza”, Universita’ di Roma,

Rome, Italy (e-mail: chiara.toglia@uniroma1.it).

D. Kettler is a Masters student at MIT, Cambridge, MA 02139 USA (e-

mail: kettler@mit.edu).

F. Kennedy is a Lt Col in the US Air Force and has been attached to the

Defense Advanced Research Projects Agency.

S. Dubowsky is a Professor in the Mechanical Engineering Department,

MIT, Cambridge, MA 02139 USA (e-mail: dubowsky@mit.edu).

the systems with greater agility and flexibility in fuel usage

management. Moreover it enables the introduction of

additional control constraints, for example those related to

plume impingement.

Consequently, modular orbiting satellites and robotics

systems have the potential to be simultaneously less

expensive and more responsive, adaptable, and robust to the

failure of one of its module. With these benefits come

significant, and largely unaddressed, control challenges.

These challenges result from the dynamic interactions

between modules, the changing inertial properties, structural

compliance, and sensor and actuator redundancy.

The control of formation flying orbital systems has been

well studied, as well as the control of spacecraft and space

robots maneuvering in close proximity for rendezvous and

docking procedures. Orbital formation flying concepts

distribute the functionality of large spacecraft over a set of

cooperative, smaller, less expensive spacecraft which do not

physically contact each other. Work in this field has focused

on modeling, coordination and control, simulation, and

autonomous formation reconfiguration [4], [5], [6].

However, the elements in these systems do not face the

challenges of physical interaction found in the modular space

robot concept addressed here.

Substantial work has also been done on the dynamics and

control of the rendezvous and docking of spacecraft and

space robots [7], [8], [9], [10]. These works have focused on

the period just before docking when the docking elements

are free-flying or free-floating, or the first few moments after

docking. The assumption generally made in these works is

that, after docking, the control of the system is stable and

well behaved. Moreover, the control of the system after

docking is not adjusted or optimized to account for changed

A Study of Cooperative Control of Self-Assembling Robots in Space

with Experimental Validation

Chiara Toglia, Daniel Kettler, Fred Kennedy, and Steven Dubowsky, Fellow, IEEE

A

A

B

Fig. 1. Image A - two modules assembling. Image B -

assemblies capturing a satellite

Page 2

mass properties of the joined system. These problems must

be investigated to properly control self-assembling modular

space robots and spacecraft. One of the few studies that have

considered the control of assemblies uses the SPHERES

platform [11].

In this paper, the analytical development of a Cooperative

Control approach, in which control efforts are coordinated

between the modules using linear quadratic regulator (LQR)

methods, is presented. This approach uses optimal control

methods to coordinate the control of the modules after

assembly to insure good stable system performance, and to

best utilize the combined resources. The algorithm balances

trajectory error, plume impingement, total fuel consumption,

as well as the distribution of fuel consumption among

modules in determining actuator commands and thruster

selection. It is important that the system balances the fuel

usage between modules since fuel is difficult to redistribute.

Simulation and experimental results are presented for the

algorithm’s performance and compared to those of a control

approach in which the control of the individual modules is

not coordinated after assembly, called here Independent

Control. The simulations are done for a representative

system consisting of an assembly of nanosatellite modules.

The experimental results are obtained using the MIT Field

and Space Robotics Laboratory (FSRL) Free-Flying Space

Robot (FFR) Testbed [12], [13]. Both the simulations and

experimental results show the effectiveness of the proposed

control approach. The Cooperative Control approach

performs better than Independent Control, yielding lower

trajectory errors, and lower fuel consumption.

II. SYSTEM MODEL AND ASSUMPTIONS

A number of simplifying assumptions are made in

modeling the system. For clarity, a 2D planar case is

considered, and in both simulations and experiments,

trajectories consist of sequential, not simultaneous,

translation and rotation elements. More complex trajectories

have been tested in simulation but are beyond the scope of

this paper [14]. The small effects of solar pressure, gravity

gradient, and thermal warping are neglected. The orbit

altitude is assumed sufficiently high so that aerodynamic

effects are also negligible. Further, it is assumed that the time

scale for an assembly’s operations is much shorter than the

orbital period, so that the effects of orbital mechanics are not

included. The compliance of the system elements is also

neglected. Finally, the modules are assumed to have thrusters

but not reaction wheels. As a result, the dynamics of the

assembly, or an individual module, may be approximated by

the simple linear equation

Axx

+=

&

where x is the n × 1 state vector and u is the r × 1, where

r = p × m, p is the number of thrusters per module and m is

the number of modules composing the assembly.

Since there is no damping, the A matrix contains simple

integrator dynamics, i.e. zeros and ones. All the information

related to the assembly configuration is contained in the B

matrix: inertial characteristics, number of thrusters, direction

Bu

(1)

of each thrust, and the relative distance of the thrusters from

the global center of mass, i.e. geometry of the thruster

placement. Using such a compartmentalized approach, the

dynamics and the control strategies can be easily adapted to

any configuration, by updating the B matrix. Simulations

demonstrating this adaptability to multiple modules have

been done and will be covered in future publications.

A. The Control Problem

For a fixed system configuration, a stable and effective

attitude and maneuvering control system can be designed

using well known methods. However, when the individual

modules assemble themselves, a number of factors enter,

making this problem more complex. These include changes

in inertial properties and redundancy of actuators and

sensors. Each module could control itself as if it were

independent, so that control would be distributed and not

cooperative. However, this control algorithm would clearly

be suboptimal and would potentially have robustness issues.

For example, measurement errors and noise, as well as

uncertainty in actuator thrusts could produce control errors

that would cause the individual modules to “fight” against

each other. This results in increased fuel consumption,

higher trajectory errors, and higher internal constraint forces

in the docking mechanism between modules. In extreme

cases the result could be destabilization of the system.

B. The Control Concept and Performance metrics

A more effective way to control the assembled modules,

and minimize the above problems, is Cooperative Control,

i.e. a single well integrated architecture that reflects the

current configuration of modules. Developed below, a

Cooperative Controller provides good performance and

stability while exploiting sensor and actuator redundancy.

Several metrics are used to develop and evaluate this

control approach. The first is the trajectory error on a

selected reference maneuver. The second is the controller’s

fuel consumption. The total amount of fuel consumed and

the control algorithm’s ability to direct fuel usage among

modules are considered. Finally, the magnitude of forces

between connected modules indicates the degree to which

the thrust commands of modules conflict. Coordination of

module control will minimize these forces.

C. Proposed Optimal Cooperative Control Algorithm

Since the system is time-varying linear, a linear quadratic

regulator (LQR) optimal controller is used to minimize

errors during a maneuver while minimizing total fuel usage

and balancing the fuel usage among modules. The cost

function J to be minimized is:

(

∫

t

0

)

++=

ft

TT

ff

T

dt RuutxQtxtxHtxJ

)()()()(

δδδδ

(2)

where δx = xdes - x is the trajectory error, δxT(tf) H δx(tf) is the

cost at the terminal time tf, Q, and R are square matrices. The

first term in the integral penalizes errors in following the

Page 3

trajectory command, while uTRu is the cost on the fuel

consumed by the thrusters. The control strategy has to be

computed ahead of time. The optimal solution is [15]:

−=

)()(

r

xtWBRtu

+

−

)(

2

1

)(

1

r

T

tVt

(3)

where tr = tf – t is the remaining time. The matrix W(tr) is

obtained integrating the Riccati equation in (4) .

dW

rr

r

−

)()(

QtWB BRtW

tWAAtW

dt

r

T

r

T

+

−+=

)()(

1

(4)

The matrix V(tr) can be found by integrating equation (5).

dV

)()(

−=

desr

T

rr

T

r

QxtVB BRtWtVA

dt

A closed loop control can thus be obtained from (3), using

the time-varying gains W(tr) and V(tr) that are computed a

priori. This controller automatically selects thrusters form

redundant sets to minimize fuel consumption.

In order to address such issues as plume impingement and

the balancing of fuel consumption, the proposed algorithm

adjusts the B matrix. B is a n × r matrix, that can be

decomposed as follows

BBB

⋅⋅=

B is a n × r matrix which translates thruster inputs into net

forces and torques about the principle axes. It describes the

behavior of the system including all thrusters without any

special adjustments. BPIC is a r × r matrix that introduces the

Plume Impingement Constraint (PIC), by removing the

contribution of those thrusters that are poorly positioned.

Thus BPIC acts as a selection matrix: all the non diagonal

elements are zero. The diagonal elements related to unusable

thrusters are zero, while those diagonal elements related to

well positioned thrusters are one. Note that the methods

developed for PIC may also be used to compensate for

malfunctioning thrusters. BFB is a r × r matrix that introduces

a weighting based on a Fuel Balancing (FB) constraint. This

insures balanced fuel consumption among the included

modules so that resource levels stay evenly apportioned. It is

a diagonal matrix whose elements are weights based on

relative fuel levels related to each module. The weighting is

the same for all the thrusters belonging to the same module

and has to be computed at every instant, taking into account

possible leaks of fuel or differences between actual and

commanded thrusts. A different approach to fuel balancing

can be followed if the difference in fuel levels is known a

priori: the fuel usage distribution can be regulated using

different weights in the LQR gains computation: in particular

the R matrix, which is a diagonal matrix, can be written using

weighted gains for each robot, so the fuel distribution is

optimal and computed ahead of time.

Cooperative Control enables the easy incorporation of

additional modules and constraints in an optimal fashion

through the B matrix. An alternative approach is developed

2)(

1

−

−

(5)

FB PIC

B

(6)

in previous literature [11]. It searches through the thrusters to

assign one thruster to supply each component of force or

torque. The forces and torques to be applied at the center of

mass are computed and the components commanded from

the specified thrusters. This approach is simple, reliable, and

does not require an expensive gain calculation, with a

consequent saving in computational cost. However, it is not

optimal and does not enable the easy addition of constraints

such as Fuel Balancing. Consequently, it is less general than

the Cooperative LQR approach and does not provide means

of minimizing fuel consumption.

III. SIMULATIONS

A. Case Study: Description and assumption

To facilitate the design of

controllers, the case of two

nanosatellite modules

considered. See Fig. 2. Each

module is equipped with two

manipulators and

thrusters, resulting in a total of

16 thrusters for the assembly.

Commands are continuous.

The modules have a mass of

10 kg, dimensions on the order

of 0.5m, and a maximum

manipulator reach of 20 cm

An assembly composed of two

nanosatellites with identical

characteristics (mass, moment

of inertia, thrusters location

and saturation threshold) is

considered. The assembly

configuration is symmetric,

although the method extends to asymmetric assemblies and

non-identical modules. Manipulators connect the modules.

These manipulators are locked and are assumed rigid. For

simplicity, modules are equipped only with thrusters in these

simulations. The reference trajectory is planar and composed

of two sequential parts: a translation in the y direction,

starting and ending with zero velocity, and a rotational

motion around the axis normal to the plane, Fig. 2. The

mission duration is 30 s, each parts lasts 15 s.

is

eight

B. Simulation Results

The Cooperative Controller and the Independent

Controller were investigated and compared. Both the control

strategies had good trajectory tracking performance, with the

Cooperative Controller producing slightly better results. Fig.

3 shows a comparison of the tracking performance. The

Independent Controller produces small oscillations when the

system is commanded to keep constant attitude angle,

because of the antagonism of the two independent

controllers. Similarly, a small drift along X and oscillations

about the reference trajectory in the Y direction can be seen

in Fig. 3. Fig. 4 shows that the total fuel consumption for the

Fig. 2. Reference Trajectory.

Page 4

two controllers is very different: the Independent control

used 82% more fuel than Cooperative Controller. In the

figure, the fuel consumption has been normalized with

respect to the total fuel used under the Cooperative Control.

Adding the Plume Impingement Constraint (PIC) to the

Cooperative Controller did not change performance

significantly. Fuel consumption was roughly the same.

Applying the fuel balancing constraint to the controller

produced the desired inequality in fuel consumption rates as

well as in an increase of the total fuel usage of approximately

6%. This result is not unexpected, since Fuel Balancing

constraints can alter the penalty on overall fuel consumption.

IV. EXPERIMENTAL VALIDATION

To validate the simulation results, an experimental study

of the Cooperative Controller was performed, using the MIT

Field and Space Robotics Laboratory (FSRL) Free-Flying

Space Robotics (FFR) Testbed [12], [13]. See Fig. 5.

The FFR Testbed consists of two multi-arm, 6.5 kg robots

floating on CO2 bearings to emulate microgravity in two

dimensions. For these tests the robots floated on a 1.3 m x

2.2 m polished granite table. The robots are equipped with

two Scara-type two-joint manipulators, eight thrusters, two

module position sensors that provide position and velocity

data, four manipulator joint angle encoders, and two

force/torque sensors. The robots have 7 DOF in total (2

module DOF’s in translation and 1 DOF in rotation, and 4

DOF’s for the manipulators’ joint motions), all of which are

controllable and observable. Throttled thrust commands are

achieved by commanding individual thrusters with a pulse

width modulated signal. All actuators and sensors are

controlled by an on-board computer and powered by

onboard batteries, so that the robots can work without any

externally connected cables. Under Cooperative Control,

both modules in an assembly are controlled by the

processor on one module. While not currently used, the

additional processor of the remaining module is available

for computationally expensive tasks such as gain

recalculation. The experimental operator can access the

onboard computer using wireless LAN adapters. The

maximum thruster force is approximately 0.1 N.

For the experiments discussed here, the robots’

manipulator end-effectors were magnetically connected

during the tests. The manipulators attempted to maintain

constant module internal geometry during the tests.

However, compliance in the manipulators and in the

connections introduced some flexibility.

A. Cases studied

Two controllers were investigated: Cooperative Control,

and Independent Control. A number of variations on

Cooperative Control, including

Constraint (PIC) and Fuel Balancing (FB) were tested.

For the majority of tests, the reference trajectory is a short

translational maneuver of a distance of 0.75 m using a

constant-velocity step in the Y direction, starting from rest.

When testing the Cooperative Control with, and without

Plume Impingement Constraints, a 90° rotation trajectory

was used, as the simple translational maneuver created

minimum plume impingement potential. In this case, the

robot assembly started at rest and was commanded to follow

a step command in rotational velocity.

Plume Impingement

B. Performance metrics

The performance of the investigated control methods were

compared using the following metrics.

Fig. 4. Simulation total fuel consumption. The vertical line indicates

the switch from translation to rotation

Fig. 3. Simulation Trajectory Tracking Performance

Fig. 5. FFR Testbed showing two robot modules in an assembly.

Page 5

Fuel consumption: The total amount of the fuel (CO2

gas) consumed by the individual robots during the test was

estimated from the thruster command history. These values

do not include the CO2 gas used to float the robots.

Trajectory Error: The root mean square error (RMS error

or RMSE) that each controller achieves on the commanded

trajectory was also considered. Position and orientation

errors were collected in addition to their derivatives. The

velocity error metrics were calculated using data from only

the final ⅔ of the duration of a test in order to allow initial

transients to decay.

To evaluate each controller, a total of 10 trials were run

using the appropriate trajectory. The means of collected fuel

usage and trajectory RMS error values were compared for

statistically significant differences using t-tests. In all cases,

statistical significance corresponds to p < 0.05, indicating

that there is a 5% probability that the observed results could

occur when there is no difference in the means.

C. Results

Table I lists mean fuel consumption and trajectory RMS

error for experimental results on the Cooperative and

Independent Controllers. These results show that the

Cooperative approach is superior. The Cooperative Control

used 43% less fuel (45 g) than the Independent Control

(79 g). See Fig. 6. The change in slope indicates where the

Cooperative Controller reduces fuel consumption after initial

acceleration. Much of this savings resulted because under

Cooperative Control antagonism between the module

propulsion systems is avoided, allowing the assembly to

enter a coasting mode and fire thrusters only occasionally to

maintain trajectory. Under Independent Control, small miss-

alignments and compliance in the connecting links foster

small oscillating fluctuations in assembly geometry and

wasted thrust. The Cooperative Control also had better

trajectory tracking performance (see Fig. 7) with significant

improvements in X RMS error and, most notably, Θ RMS

error where the Cooperative LQR controller reduced mean

error by 91%.

Table II lists mean fuel consumption and trajectory RMS

error for experimental results on the Cooperative and

Cooperative with Plume Impingement Constraint (PIC)

Controllers. While the additional constraint effectively

prevented the use of poorly-positioned thrusters as desired,

there were negligible reductions in trajectory tracking

performance. A significant difference was only found

between the mean Θ RMS errors. However, this difference

was only 3%.

Unexpectedly, the addition of the plume impingement

constraint reduced total fuel consumption by 18%. This

occurs because with PIC the assembly no longer directs

thruster plumes against its own surfaces thereby wasting

thrust and fuel.

Table III lists mean fuel consumption and trajectory RMS

error for experimental results on the Cooperative and

Cooperative with Fuel Balancing (FB) Controllers.

Experimental results demonstrate, with statistical

Slope Change

Fig. 6. Median fuel consumption for Cooperative and Independent Control.

Note the distinctive reduction in slope of the Cooperative line at time 1.5 s.

Fig. 7. Median Y and Θ RMS error for Cooperative and Independent

Control. Y performance is equivalent. Cooperative Control has much

lower Θ error.

TABLE I

COOPERATIVE CONTROL VS INDEPENDENT CONTROL

COOPERATIVE INDEPENDENT

P VALUE

Total Fuel

Consumption

X RMSE

Y RMSE

Y’ RMSE

Θ RMSE

45 g 79 g <0.001

0.9 cm

6.2 cm

2.4 cm s-1

0.164°

1.2 cm

6.3 cm

2.4 cm s-1

1.900°

<0.001

=0.456

=1.000

<0.001

Note p < 0.05 corresponds to statistical significance.

TABLE II

COOPERATIVE CONTROL VS COOPERATIVE PIC CONTROL

COOPERATIVE

COOPERATIVE

PIC

120 g

P VALUE

Total Fuel

Consumption

X RMSE

Y RMSE

Θ RMSE

Θ’ RMSE

147 g <0.001

0.7 cm

0.2 cm

2.86°

0.05 °s-1

0.7 cm

0.3 cm

2.95°

0.05 °s-1

=0.255

=0.253

=0.387

=0.003

Note p < 0.05 corresponds to statistical significance.