The development is described of event-based intelligent control system for a space-adapted mixing process by employing the DEVS (Discrete Event System Specification) formalism. In this control paradigm, the controller expects to receive confirming sensor responses to its control commands within definite time windows determined by its DEVS model of the system under control. The DEVS-based intelligent control paradigm was applied in a space-adapted mixing system capable of supporting the laboratory automation aboard a Space Station

Chi, Sung-Do

Zeigler, Bernard P.

English

NASA Technical Reports Server

• ° .
27 279
DEVS-based Intelligent Control of Space Adapted Fluid Mixing
Sung-Do Chi and Bernard P. Zeigler
AI-Simulation Group
Department of Electrical and Computer Engineering
University of Arizona, Tucson, AZ 85721
Abstract
This paper describes the development of event-based intelligent control system for a
space-adapted mixing process by employing the DEVS (Discrete Event System Specifi-
cation) formalism. In this control paradigm, the controller expects to receive confirming
sensor responses to its control commands within definite time windows determined by its
DEVS model of the system under control. We apply the DEVS-based intelligent con-
trol paradigm in a space-adapted mixing system capable of supporting the laboratory
automation aboard a Space Station.
I. Introduction
"Intelligent control", the intersection of artificial intelligence, conventional automatic
control, and operations research approaches, is receiving increasing attention in both the-
ory and application(3). An intelligent control system often employs a hierarchical control
structure in which a higher-level intelligent controller supervises a lower-level conventional
controller. The event-based control paradigm, introduced by Zeigler(4), realizes such in-
telligent control by employing a discrete eventistic form of control logic represented by
the DEVS formalism.
In this control paradigm, the controller expects to receive confirming sensor responses
to its control commands within definite time windows determined by its DEVS model of
the system under control. Since the DEVS formalism is at heart of event-based control
system design, such controllers can be readily checked by computer simulation prior to
implementation. Thus the DEVS formalism plays the same role to event-based control
that differential and difference equation formalism play to conventional control(4). An
advantage of an event-based control system using DEVS models includes its fault di-
agnostic capability supported by DEVS-Scheme, a LISP environment implementing of
DEVS formalism(2,7,8).
This paper describes the development of DEVS-based intelligent control system for a
space-adapted mixing process. The paper first reviews the concept of DEVS formalism,
then uses it to formalize the dynamics of a mixing process. It shows the hierarchical
architecture of the realized intelligent control system for mixing. Several simulation runs
illustrate the technique.
II. DEVS Concept for Event-based Control
The Discrete Event System Specification (DEVS) formalism introduced by Zeigler(5)
rovides a means of specifying a mathematical object called a system. Basically, a system
as a time base, inputs, states, and outputs, and functions for determining next states and
outputs, given current states and inputs(6). In the DEVS formalism, one must specify
1) basic models from which larger ones are built, and 2) how these models are connected
together in hierarchical fashion. Detail descriptions of how a basic model, called an atomic
model and the second form of model, called a coupled model, are specified are found in
(1,7).
DEVS-Scheme is an implementation of the DEVS formalism in SCOOPS, an object-
oriented superset of Scheme (a Lisp dialect), which enables the modeler to specify models
25
https://ntrs.nasa.gov/search.jsp?R=19900017963 2020-03-19T21:14:46+00:00Z
Syr,  0eB _ r,n0eC
A-filling-rate I I I I I I mixed-concentration
A-concentrationLm_J LmJ i Lm j emptying-rate
B: ,c,;l_;egnrta:_io n, ! I II I_
I la_uF,_.m I Container
Figure 1: Space Adapted Mixing System
directly in DEVS terms. DEVS-Scheme supports building models in a hierarchical, modu-
lar manner (successively putting systems together to form larger ones), a systems-oriented
approach not possible in conventional languages. Detail description of DEVS-Scheme in-
cluding its class hierarchy is available in (1,7).
The DEVS formalism is more than just a means of constructing simulation models.
It provides a formal representation of discrete event systems capable of mathematical
manipulation, just as differential equations serve this role for continuous systems. We
illustrate how mixing systems specified for the space environment may be advantageously
mapped into DEVS representations. Suitably operating on the structure of such DEVS
models provides a basis for the design of an event-based controller.
III. Space Adapted Mixing Process
The design of a space-adapted container would have an aluminium bottle containing
an inflatable bag, which is the actual liquid container; liquid is injected/extracted by
means of syringes; air pressure between the outside of the bag and the inside of the
bottle wall ensures that the bag remains "full" at all times. We treats a system with
space adapted container stirred by rotating propeller as shown in Figure 1. The mixing
process is batch - some quantity of fluid with a given concentration is added to a container
already filled with a liquid of another concentration. The container is fed with an incoming
liquid-A from the syringe-A with a flow rate r_ by the control command, FILL-A. Once the
level of liquid-A reaches to its pre-specified level, the flow of liquid-A might be stopped
by control command, STOP. Then, another liquid-B from the syringe-B is added into
the liquid-A in the container with a flow rate rb until it receives the control command,
STOP. Both feeds contain dissolved material with constant concentrations, C_ and Cb,
respectively. Assume that the propeller starts either at the same time as liquid-B starts to
be filled or after filling liquid-B is complete. The propeller should cease its operation by
the control command, STOP. When the propeller stops, the concentration of both liquids
should reach to the equilibrium value. The outgoing flow to the syringe-C has a flow rate
r e .
Figure 2 illustrates the relationship between dynamic characteristics and times. Fig-
ure 2(a) represents the various filling rates : A-filling-rate (r_), B- filling-rate (%), and
emptying-rate (rc). Figure 2(5) shows the normal and fault cases of mixing effect (_)
characteristics. In the fault case, broken propeller, o_ might be slowly decreased (propeller
speed is reduced). Figure 2(c) shows the volume characteristics : liquid-A volume(Va),
liquid-B volume(Vb), and total volume(Vc). The concentration characteristics of normal
and fault cases are shown in Figure 2(d). The concentrations of both liquids are expo-
nentially changed toward the equilibrium value. Here we adopted the 2% steady value
26
(a} Filling-
rate
(b) Alpha
(c) Volume
(d) Concent-
ration
rc
ra
rb
max
rain
Propeller Broken (reducing speed)
-1
• _,hl 4
Vc
Vb
Cb
EMPTY A-FULL B-FULL MIXED EMPTY
2% steady
/ Criterion
Propeller Broken
I--Normal]
[ ....... Error |
Figure 2: Dynamic Characteristics of Mixing System
criterion for reaching equilibrium level. Later, we will show how this system can be suc-
cessfully controlled and how the error can be diagnosed.
IV. DEVS Representation of Mixing Process
In the DEVS representation of event-based control, a DEVS model moves through
its checkstates in concert with external inputs, as long as those inputs arrive within the
expected time windows. Associated with each checkstate are a minimum time and a
window. In contrast to conventional sampled data systems, event-based logic does not
require sensor output precision. Sensors can have threshold-like characteristics. Only
two output states, for example, on/off, are needed although more may be employed.
However, to generate the time windows the output states of the sensor must be accurately
and reliably correlated with values of significant process variables. Figure 3(a) shows
possible threshold-type sensors. A visual sensor in Figure 3(b) can provide more precise
information for fault diagnosis. Figure 4 illustrates various data types of sensory inputs.
The indicator used by controller keeps track of the container state estimated by level-
sensors. However, the backup sensor, tube sensor and vision sensor provide more accurate
container state. Therefore, by checking these various sensory sources, the diagnoser can
do the fault diagnosis.
The control task is performed as the DEVS model of control system changes its state
from an initial position on a given threshold sensor boundary to a succession, possibly
cyclic, of boundaries. More concretely, this means we want the system to go through a
predetermined sequence of states as reported by sensor readings. Our control logic will,
as each boundary crossing is achieved, issue a control action, i.e., send an appropriate
input to the system, in order to move toward the next desired boundary. The controller
has a time window in which it expects the appropriate sensor states to change to confirm
the expected boundary crossing. The time windows are derived from the DEVS external
model of the system. Figure 5 presents the phase categorization in terms of the boundary
conditions of the dynamic chacteristics and their transition cycle with control commands.
The transitions labelled by () are expected to take place within given time windows, as
illustrated in the next section.
27
(a) Threshold-type
Sensors
(b) Visual
Sensor
Constr._ ngle
tion _ "_ Level
Angle sensor :
B-full sensor :
A-full sensor :
Empty sensor :
respond when angle
is between 89 and 91
respond when liquid
reached to specified
level.
respond when liquid
reached to specified
level.
respond when liquid
reached to empty
level.
Figure 3: Sensory Inputs of Space Adapted Mixing System
INDICATOR ISENSOR I TIMING I PHASE
empty-sensor too-early emptying
A-full-sensor too-late A-filling
B-full-sensor B-filling
mixing
I i v'si°NISyringe Constric- I Concent-
Level -angle tion I ration
-SENSO_
A-full
B-full
empty
IANGLE-SENSOR I
off-angle
normal
Figure 4: Data Types of Sensory Inputs
V. Simulation Approach of Mixing Process
Figure 6 shows a simulation structure for the mixing process control. The simulation
test is decomposed into a model representing the real bottle, MIX-E, and control-unit. The
centrol-unit is decomposed into an operator for filling, mixing and emptying a container
and a diagnoser for discovering the causes of any operational faults. There are three
sub-models in the simulation :
MIX-E : Model of a space adapted mixing system. MIX-E is able to respond to both
operational commands and diagnostic probes. It is external to the controller.
MIX-O : an operational model of the mixing system used by a controller, CONTRL, to
generate its commands and verify the received sensor responses. Table 1 presents the
operation table used in the MIX-O model. For example, the first column states that if
the current state is EMPTY and the input is FILL-A then the next-state is A-FILLING;
also the output in the current state is nil and its time-advance is infinity (ta = inf). The
time window of the next state is given by two fields : next-ta(20) gives its lower bound,
and next-wind(6) gives its width (so its upper bound is 26).
MIX-D : a classification, or expert-system-like model, employed by the diagnoser inference
engine, DIAGN, to determine the probable source of breakdown. As an example, an
informal presentation of some of the diagnostic rules is given below :
R1 : If backup-sensor is not A-full and A-full-sensor is true, then "A-full-sensor is bad".
28
STOP :ILL-B & MIX
FILL-A
(
A-fillin
()
()
EMPTY ()
STOP
Empty
A-filling
A-over-filling
A-burst
A-full
B-filling & mixing
B-over-filling
B-burst
B-full
Mixing
Over-mixing
Mixed
Emptying
: empty sensor on
: level < A-full level
: A-full-sensor on. level > A-full level
: Liquid-A overflow, level = TOP
: A-full-sensor on
A-full level < level < B-full-level. propeller sensor on.
: B-full-sensor on. level > B-full level
: Liquid overflow, level = TOP
: B-full-sensor on
: Concentration > 2% criterion
: Concentration < 2% criterion. Propeller on
: Concentration < 2% criterion. Propeller off
: empty level < level < B-full level
Figure 5: Phase Categorization and Phase Transition Diagram
R2 : If timing is too-late and phase is A-filling and level is less than 100, then "Tube is
off-angle or leaky".
R3 : If timing is too-late and phase is mixing and concentration-level is greater than 31,
then "Propeller is broken".
Figure 7 shows the hierarchical structure of the mixing control system. Level III is
the lowest level, where the real system under control exists. The external model, MIX-E,
receives input messages such as control commands and read-sensor commands from the
next higher level, Level II. It sends the sensor readings to the next higher level. Level II
corresponds to the control unit shown in Figure 6. It has an operational model, MIX-O,
and a rule-based model, MIX-D, to provide necessary information for the controller and
diagnoser, respectively. It also sends a result message to its next higher level, Level I,
which contains the goal agent, the highest unit of the control system. The agent may
represent a robotic or other autonomous decision maker.
VI. Simulation Results
To test the control logic for a mixing process, we have run several simulation ex-
periments of a possible mixing process. The simulation experiments concern two cases :
normal case and fault case. Initial values for an external model, MIX-E, under normal
operation are given as follows ; ra = 5, rb = 6.67, rc = 8.3, Va = 100, Vb = 66.6, Ca(0) =
50, Cb(O) = 0, angle = 90, constriction effect = 5, leakage rate = 0.001, and a = 0.005. We
also assume several time delays of sensor readings, for example, 1 sec for backup-sensor
29
I
MIX-E
TEST
I
TEST-DEC
I
i
CONTROL-UNIT
I
CONTROL-UNIT-DEC
I
I I
OPERATOR DIAGNOSER
I I
OPERATIOR_DE C DIAGNOSER-DECI
i i I I
CONTRL MIX-O DIAGN MIX-D
Figure 6: Hierarchical Simulation Structure for a Mixing System
state input next-state output ta next-ta next-wind
empty filI-A A-filling () inf 20 6
A-filling 0 A-over-filling A-full-sensor 20 1000 20
A-over-filling 0 A-burst 0 1000 inf ()
A-over-filling stop A-full 0 1000 inf ()
A-full filI-B&mix B-filling 0 inf 10 4
B-filling 0 B-over-filling B-full-sensor 10 1000 20
B-over-filling stop B-full 0 1000 inf ()
B-full mix mixing 0 inf 45 7
mixing 0 over-mixing () 45 1000 20
over-mixing () inf-mixing 0 1000 inf ()
over-mixing stop mixed () 1000 inf ()
mixed empty emptying 0 inf 20 6
emptying 0 empty empty-sensor 20 inf ()
Table 1: Table Specification of an internal model,MIX-O
reading and 100 sec for visual-sensor reading. In the future, these time delays could be
taken into account when deciding on sensors to interrogate.
The partial simulation results of normal and fault cases for a goal plan from B-full
to MIXED are illustrated in Table 2(a) and (b), respectively. In the normal case, the
controller, CONTRL, issues the control command, MIX, to the external model, MIX-E,
and also to the internal model, MIX-O, and then waits for the sensory response during the
scheduled time window (7 sec). If the sensory response arrives within the time window,
the controller generates the next command and so on, till the MIX-E reaches to its goal
state. But, in the fault case, where the propeller is broken during mixing (at clock time
35), the mixing effect, o_, decreases from 0.08 to 0.05 (the base level) with decreasing rate
0.005 at each time step (see Figure 2(5)). In this case, Table 2(5) shows that there is no
response from MIX-E before time step 86.3, the upper bound of the time window given by
MIX-O. Therefore, the controller generates the error command, ERROR, to the diagnoser.
The diagnoser, DIAGN, checks the data associated with the discrepancy, such as phase
in which it occurred, and its timing. It also gets sensor data from MIX-E. The expert-
system-like model, MIX-D, concludes "the propeller is broken" by using the data from
DIAGN. This is because the indicator shows that its timing is TOO-LATE and current
phase is MIXING but the visual sensor shows that the concentration has not reached to
3O
LEVEL, OoaFtI
1- Donq_ 4- Error-info
........ r:-:- b_fo_- - -,i,-- - -1-,-r-ic ........ b_,_,SS-E_:-:
I I --0 II_b_ MIX.D [_ CONTRL_.._ DIAGN _ MIX.D I
LEVELII F '-'If 'll'fl
............... C°mmand] , _ ] iSenst_ _ i _ . _ t Read.fiens_°r .....
M,x 
Figure 7: Block Realization of the Mixing Control System
clock
34.3
79.3
83.2
84.2
MIX-E MIX-O
state output state output
CONTRL
state output
(mixing 45) () (mixing) (mix 45 7) () (mix)
() ( ) () () (window 7) 0
(over-mixing 200) () (mixed) (stop) (check 1) 0
(mixed) ( ) 0 0 0 (stop)
(a) Goal Plan • B-FULL-> MIXED (normal case)
MIX-E MIX-O CONTRL
clock
state J output state output state I output
34.3 (mixing 45) () (mixing) (mix 45 7) 0 (mix) 0
79.3 0 0 0 0 (wind 7) 0 0
86.3 0 0 0 0 (,error)(too-late) 0
87.3 (reading 1) 0 0 0 0 0 0
88.3 (reading 10) (B-full) 0 0 0 0 0
98.3 (visual 100) (normal) 0 0 0 0 0
198.3 (finish-read)(vision-info)0 0 0 0 0
199.3 (passive) (done) 0 0 0 0 0
200.3 0 0 0 0 0
MIX-D DIAGN
I output state outputstate
0 0 0
0 0 0
0 (start 1) 0
0 (wait-sensor) 0
0 (wait-sensor) 0
0 (wait-sensor) 0
0 (wait-sensor) 0
0 (start-diagn) (sensor-
0 (passive) (propeller(passive) data)
-broken) (propeller
-broken)
(b) Goal Plan • B-FULL -> MIXED (error case )
Table 2: Simulation Results (partially shown)
31
its equibrium value.
VII. Conclusions
This paper has shown how the space-adapted mixing control system is advantageously
represented as discrete event models by employing techniques based on the DEVS formal-
ism. SeverM fluid handling models have been successfully testing in the DEVS-Scheme
environment. Suitably operating on the structure of such DEVS models provides a ba-
sis for design of event-based logic control. Since the DEVS formalism is at the heart of
event-based control system design, such controllers can be readily checked by computer
simulation prior to implementation. Thus the DEVS formalism plays the sa_ne role with
respect to event-based control that differentiM and difference equation formalisms play
to conventionM control. This principle and the applicability of the DEVS-based control
paradigm was illustrated here in the design of a fluid mixing system capable of supporting
laboratory automation aboard a Space Station. The inclusion of event-based control units
within robotic agents is discussed in (9, 10).
Acknowledgement
This research was supported by NASA-Ames cooperative agreement No. NCC 2-525,
"A Simulation Environment for Laboratory Management by Robot Organizations".
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