A novel hybridization design principle for intelligent mechatronics systems
W.J. Zhang1,2*, P.R. Ouyang3, Z.H. Sun1
1. School of Mechanical Engineering, Dong Hua University. P. R. China
2. Department of Mechanical Engineering, University of Saskatchewan, Canada
3. Department of Aerospace Engineering, Ryerson University, Canada
* Corresponding author ([email protected])
Abstract：
This paper presents a novel design principle as well as its
methodology for intelligent machine systems based on
learning of hybridization in biology. In particular, we
propose two principles for hybridization of physical
systems: complementary principle and compatibility
principle. The complementary principle states that two
systems (A, B) can be integrated when A’s strength
corresponds to B’s weakness, while B’s strength to A’s
weakness. The compatibility principle states that two
systems when they are integrated need to be adjusted at their
physical component level to minimize their side effects. It is
clear that the complementary principle gives a necessary
condition for two systems to be integrated; while the
compatibility principle gives a sufficient condition for
integration of two components. These two principles are
then elaborated with respect to a generic intelligent
mechatronic system model, leading to three distinct hybrid
physical systems: hybrid actuation system, hybrid
mechanism system, and hybrid control system. Examples of
the three hybrid physical systems are illustrated to highlight
the two principles. The contribution of this paper is the
proposed principles to design intelligent mechatronic
systems through hybridization.
1. INTRODUCTION
In biological systems, it is relatively well known that the
hybridization of different kinds of plants and animals could
enhance or improve the functions of a resulting biological
system. A biological system which is made through the
Intelligent Machine Systems
Hybrid
Biological
Systems
A
Sensors
B
End-effector
Hybrid Objective
A Strong
B Strong
A Weak
B Weak
Flies
hybridization process is hereafter called hybrid biological
systems. The basic idea of biological hybridization can be
explained with Fig. 1. In this figure, a biological hybrid
system consists of biological system (A) and biological
system (B). Both biological systems play at least two
functions, say Function 1 and Function 2. In particular, A’s
strength in Function 1 complements B’s weakness in
Function 1, while A’s weakness in Function 2 is
compensated by B’s strength in Function 2. The objective of
hybridization is to produce a system with the enhanced
functions (Function 1 and Function 2).
The hybridization process must follow some principles, and
they are rooted in biochemistry and physics. The
aforementioned function complementing to each other is one
of the principles, which may be called the complementary
principle. Furthermore, the complementary principle is
applied to the general architecture of biological systems; this
architecture defines the types of components, their roles and
their relations. For example, brain is a type of component,
which plays a control role and has connections to all other
components. When two systems, A and B, meet the
complementary principle, there is a process which fine-tunes
the integration of A and B to make them compatible, called
the compatibility principle.
Successful cases by means of the hybridization technology
in biological systems have inspired us to develop this
technology for designing engineered systems. In this paper,
the engineered system refers to the so-called intelligent
machine system. Fig. 2 shows the architecture of the
intelligent machine system. It can be seen from this figure
that intelligent behaviours (decision making, learning,
sensing, and adapting) are fulfilled by components such as:
Mechanism
Actuator
Energy
Energy
Material
Material
Information
Information
- Decision making
- Learning
- Sensing
Cats
Controller
- Adapting (soft and hard)
2
Fig. 1: Hybrid biological system
Fig. 2: Intelligent machine system
controller, actuator, mechanism, end-effector, and sensor. In
the upper right part of Fig. 2, we show that a machine serves
as a “transformer” which receives inputs and produces
outputs, while the types of inputs and outputs are energy,
material, and information. From the left side of the figure,
we can conclude that by applying the complementary
principle in a biological system to the intelligent machine
system, we can have: (i) hybrid actuation systems, (ii)
hybrid motion systems, (iii) hybrid control systems, (iv)
hybrid end-effector systems, and (v) hybrid sensing systems.
In the following, we will present three hybrid machine
systems: (1) hybrid mechanism system, (2) hybrid actuator
system, and (3) hybrid control system to demonstrate the
idea of hybridization in intelligent machine systems. In
particular, we demonstrate how to apply the aforementioned
two principles (complementary principle and compatibility
principle) to construct these hybrid intelligent machines.
of M, say  x M , y M  ,  x M , y M  , and xM , yM  , to
design the system (i.e., the dimension and mass of all its
moving objects) and the controller (for the servomotor) such
that the actual motion of point M will “track” the given
motion. For high accuracy in tracking, a servomotor with its
controller to follow the feedback control theory is necessary.
If both high accuracy and high power capacity are required,
the combination of the CV motor and servomotor is an
excellent solution. In the following, we outline the
methodology to fine-tune the two types of motors (i.e.,
servomotor and CV motor) to see how the compatibility
principle is applied to this specific hybridization situation).
Hybridization at the actuator level
M
Servo motor
Five-bar Mechanism
2. HYBRID ACTUATION SYSTEMS
To an actuator system, we mainly concern its power
capacity and its task flexibility. The former is clear, while
the latter refers to the adaptability of the actuator to a
different task both on-line and off-line. The on-line
adaptability means that the actuator changes its actuating
behaviour during an operation, while the off-line
adaptability means that the actuator changes its actuating
behaviour by stopping an operation. In this paper, we focus
on the on-line task flexibility.
In machine systems there are two types of actuation
systems or motor systems in terms of whether there is online task adaptability: “constant” velocity (CV) motor and
servomotor. The CV motor is known for high power
capacity yet not possible to change its actuating behaviour
on-line or real-time, while the servomotor is known for online task flexibility yet relatively low power capacity. Note
that the low power capacity with the servomotor or the high
power capacity with the CV motor should be viewed on a
relativity ground, in particular relative to cost and power
efficiency. In the servomotor, the variation in mechanical
inertia is completely attributed to the electric current which
is further converted to heat energy. The heat energy will
further leads to temperature rise, which thus establishes a
limit for the servomotor’s power capacity. On the other
hand, the CV motor does not have such a limit. Due to this
difference, for the same power capacity, more engineering
effort needs to be put on the servomotor than that on the CV
motor and thus, the cost of servomotor is much higher than
the CV motor. From the discussion of their strength and
weakness (respectively), combining of these two types of
actuators meets the complementary principle.
Fig. 3 shows an example of the machine that combines one
CV motor and one servomotor, and the whole system is
named as five-bar hybrid actuation mechanism. In this
system, the end-effector could be a point on any moving
body, for example, M, or a line on any moving body.
Suppose that we consider the end-effector as a point M (see
Fig. 3). The generic task for this hybrid actuation
mechanical system can be stated as: given a set of motions
Flexible
for task
CV motor
Weak in
power
Rigid for
task
Strong in
power
Fig. 3 Hybrid Actuation System
2.1 THE MAIN OBSTACLE FOR
HYBRID ACTUATION SYSTEM
THE
FINE -TUNING
OF THE
The main obstacle which challenges the compatibility of
the CV and servo actuators is the uncontrollability of the CV
motor. The CV motor provides a passive degree of freedom
to the system. When the inertia of the system is timevarying, there is speed fluctuation in the CV motor during
the operation of the system. This speed fluctuation in the CV
motor will propagate to the servomotor because the two
motors are coupled by the five-bar mechanism (Fig. 3).
Also, the motion at the end-effector is generated by the
contribution of both servo and CV motors (Fig. 4); therefore
the speed fluctuation in the CV motor will have a great
impact to the motion at the end-effector [3].
2.2 THE METHODOLOGY FOR THE FINE-TUNING
The general idea for the fine-tuning process is to make the
information of speed fluctuation in the CV motor known to
the servo motor’s controller. This can be done by sensing the
speed fluctuation in the CV motor and send this information
to the controller of the servo motor. In this case, the plant of
the controller of the servo motor will include the
mechanism, the CV motor and the servo motor. The other
way to let the controller of the servo motor know the speed
θcv
End-effector (xm, ym)
Inverse kinematics
v
t
CV motor (θcv)
Servo motor (θs)
3. HYBRID CONTROL SYSTEMS
Fig. 4: Determination of the trajectory of the servo motor
fluctuation in the CV motor is through the modeling, i.e.,
establishing a total dynamic model of the whole system (CV
motor, servo motor, and mechanism). Such a dynamic model
has the following general structure:
1 ( x1 , x1 , x1 , x2 , x 2 , x2 , 1 , 2 , { pi })  0

2 ( x1 , x1 , x1 , x2 , x 2 , x2 , 1 , 2 , { pi })  0
(2)
In the above equation, 1 and 2 denote equation
operators, xi , x i , xi (i=1,2) represent the displacement,
velocity, and acceleration, respectively.  i (i=1,2) represent
torques on the actuators; in particular  1 refers to the torque
2
to the torque in the servomotor.
Further in Eq. (2),
is a set of parameters
in the CV motor and
p i 
associated with the structure of the mechanism and the two
motors. For details of the model, we refer to [8,9].
Further,  1 will be set up as a constant (i.e., v) because it is
connected to a particular control law. Let the control law be
expressed in its general form as
 2  C (e, e,  e, pi , {ci })
3.1 THE BASIC CONCEPT OF CONTROL
The word “control” refers to the process of using external
forces or stimuli to make a plant (a target object being
controlled) behave as desired. The general configuration of a
control system, including a plant and a controller, is shown
in Fig. 5. In Fig. 5, we show the concept of the desired
behaviour and disturbance along with the plant and
controller. In order to effectively control a plant, it is better
to know the dynamics of the plant, which is represented by a
model called plant model. The control law can be generally
expressed by (rewriting Eq. (3) here)
 2  C (e, e,  e, pi , {Ci })
2
into Eq.
§1 ( x1 , x1 , x1 , x2 , x 2 , x2 , e, e,  e, o , pi , ci )  0

 §2 ( x1 , x1 , x1 , x2 , x 2 , x2 e, e,  e, o , pi , ci )  0
(4)
where §1 and §2 are function operators, respectively. The
fine-tuning process is then to determine a proper control law
with its parameters {ci} based on Eq. (4) such that the
servomotor follows its “trajectory” θs(t). It is noted that the
speed fluctuation in the CV motor due to the time-varying
inertia of the system has been included in Eq. (4).
In our previous study [5], we observed that the control law
based on PID [6] and CTC [4] does not work well when the
speed fluctuation in the CV motor is high (particularly, for
(5)
Further, Eq. (5) is in fact called feedback control because
the actual performance of a controlled plant is utilized to
generate the error which is the discrepancy between the
actual and the desired behaviours, and this error is given to
the controller which essentially makes decisions to “correct”
the error. The feed forward control law excludes this error.
Disturbance
(3)
Where C(.) may be called control law function, e is the error,
{Ci } is a set of parameters associated with a particular
control law. Substitution of  1 (a constant) and
(2) will lead to two new equations:
the controller of the servo motor the speed of the motor
would be greater than 100 ~ 200 rpm). Such a control law
may work for the low speed of the CV motor (i.e., ~15 rpm).
This means that the fine - tuning has not achieved its goal.
In our recent study [8,9,12], we employed the sliding mode
control (SMC) law because it is known to be more robust in
terms of noise rejection. The result has shown a very good
performance; the controlled system is of global stability.
This implies a successful fine-tuning process.
Dynamic
model
Plant
Desired
Controller
model or
control law
Controller
Fig. 5: General configuration of control
The control behaviour and performance are assessed by the
following attributes: stability, accuracy, robustness, control
speed, and control power. Stability refers to the change trend
of the error with respect to operating time. For example, the
type of the change trend could be: (1) the error diminishes to
zero, (2) the error tends to a constant, (3) the error tends to
be oscillating within a bound, (4) the error tends to be
infinitely large. Accuracy refers to the transient discrepancy
between the desired system behaviour and the actual system
behaviour. Robustness refers to a property of the control
system (including the plant) regarding the insensitivity of the
system behaviour with respect to the disturbance to the
system. Control speed refers to how fast the plant system
can run in fulfilling a certain task (e.g., trajectory tracking).
Control power refers to the energy needed to drive the plant
to fulfill its task. It is clear that the general goal of a control
system is of global stability, good robustness, fast control
speed, and low control power.
3.2 CONTROL SYSTEMS: CLASSIFICATION
3.3 GENERAL REMARKS
The above discussion of the adaptive control law may be
different from the current adaptive control literature [7]
where the adaptive control law usually refers to a control
Type of control law
(1) Learn and change during the task execution
(6)
During the control process, the control parameters kP and kD
change with respect to the operating time (notice that error
‘e’ will be a function of time as well). Quite naturally, we
can have a combined control law which is model-based and
at the same time, adaptive.
Further, we can divide the adaptive control law into the online adaptive law and the off-line adaptive law. In the former
the updating of the parameters {pi} and {ci} in the control
law is during the execution of a control task (see Fig. 6
along the time dimension); while in the latter, the updating
of the control law is after the completion of a period of
motion or control task (see Fig. 6 along the iteration
dimension).
In the adaptive control law, the change of the parameters in
the control law requires a pre-defined scheme. For example,
in Eq. (6), kP(e) and kD(e) are explicit form of the function of
the error e. This means that before the control process, we
have already known how the control parameters are going to
be changed. However, this requirement does not match the
real situation very well because the disturbance to a
controlled plant quite often changes during the control
process; in other words, the disturbance is essentially
unknown prior to the control process.
To address this problem, the change of the parameters in
the control law should follow the control process or should
be based on the performance of a controlled plant. Such a
control law is called learning control law. There are two
strategies of learning: the learning takes place during the
control process (on-line learning) and the learning takes
(2) Learn and change after the task execution
Time horizon
Iteration
Control laws can be classified based on the criterion of
whether the plant model is made use of. The PID control law
does not use the plant model, whereas the CTC control law
uses the plant model. Control laws are also classified into
the feedback and feed-forward control laws. The feedback
control law will include the information of discrepancy
between the plant’s actual behaviour and desired behaviour.
Adaptive control laws may refer to any control law where
either {ci} or {pi} or both are updated during the operation.
According to this understanding, the non-linear PD control
in the following is viewed as a kind of adaptive control law.
  k p (e)e  k p (e)e
place after the completion of the control process (off-line
learning). In the off-line learning, the scenario is such that
the control process is repeated in several times (say n times).
The control process at the i-th time learns the control process
prior to the i-th time.
Fig. 6 On-line and off-line iterative learning
law that updates the parameter set {pi}. We believe that the
definition of the adaptive control law to include the updating
of {ci} is more general. Another remark lies in the definition
of learning control laws. Usually, the learning control law is
defined along the iteration dimension [2] for those plants
that perform in a repetitive cyclic manner. We believe that
the definition of the learning control law to include the
learning along the time dimension is more general, as from
its nature, learning means to discover knowledge from the
past. The learning along the iteration dimension and the
learning along the time dimension only differ in the structure
of the past and thus its associated knowledge.
3.3 HYBRIDIZATION OF CONTROL LAWS
We show several hybridization schemes for control laws in
this section. Basically, we consider hybridization of the
learning control law and non-learning based control law.
The former has its strength in terms of accuracy (assuming
that plant is very complex and the dynamics of the plant is
unknown) yet has its weakness in terms of control speed
(because the learning takes time in general). The latter has
its strength in terms of control speed yet has its weakness in
terms of accuracy. Therefore, those learning control laws
and those non-learning control laws meet the
complementary principle. In the following, we show several
hybridization control laws.
The first hybridization control law consists of the PD
control law and the iterative learning control law (see Fig.
7). The iterative learning control law means that the learning
takes place after the completion of the control task, 1st time,
2nd time, …, until the n-th time or the learning takes place
along the iteration dimension (see Fig. 6); the control at the
n-th iteration learns the controls prior to it. In Fig. 7, the
current iteration number is k which learns the k-1 control
law (which is indicated by having the uk-1) in the expression
of the control law for uk.
Iteration index
Switch takes place in the iteration domain
PD + (Iteration) Learning
Plant Model: State Space Equation Representation
Fig. 9 Switching + learning control law
Adaptive + (Iteration) Learning
Controller law (k is the iteration number)
16
Fig. 7 PD + learning control law
Fig. 8 shows the second hybridization control law which
consists of the non-linear PD law and the iterative learning
control law. Further, in Fig. 9, we show the third
hybridization control law which consists of a switch control
law and the iterative learning control law. Different from the
conventional switch control law, in the proposed control
law, the switching takes place between iterations.
(a) Actuator 1
Adaptive + (Iteration) Learning
Adaptive + (Iteration) Learning
Angular error of Actuator 2
Non-linear PD (NPD). In particular
its gain is the function of the error
Control law
Fig. 8 Adaptive + learning control law
We applied these hybrid control laws to the five-bar
mechanism with two degrees of freedom (Fig. 3). Details of
the application are referred to [9,10]. Fig. 10 shows the
result of the hybrid control law (the adaptive law and
iterative learning law) where Fig. 10a is the position error of
actuator 1, and Fig. 10b is the position error of actuator 2.
(b) Actuator 2
Fig. 10: The positioning errors of the two actuators
of the five-bar mechanism system
Fig. 11 shows an experimental comparison between the
second and third hybrid control laws. These results show
different performances with different control laws that are
designed through a hybridization process, which further
implies that the hybridization concept works for control laws
development.
see the right part of Fig. 12.
There are two types of motion generation methods that
have their complementary strength and weakness. With the
RBM method, the motion range is larger (strength) but the
motion accuracy is poorer (weakness); whereas, with the
CBM method, the motion range is smaller (weakness) but
the motion accuracy is higher (strength). Therefore, the
mechanisms designed based on the RBM method and CBM
method, respectively, can be hybridized into a hybrid
mechanism system.
4.2 THE COMPATIBILITY PRINCIPLE
MECHANISM SYSTEM
WITH THE
HYBRID
In the literature, the so-called macro-micro system can be
viewed as a specialized type of the hybrid mechanism
system here. Typically, the macro-micro system in the
literature takes a serial structure (see Fig. 13: the left part),
where a macro (based on the RBM) and a micro (based on
the CBM) are connected in serial.
Fig. 11 Comparison of two hybrid control laws
(ASL-PD: Switching PD +Learning; Adaptive NPD
+ Learning).
Others
Serial structure
Ours
Parallel structure
4. HYBRID MECHANISM SYSTEMS
4.1 THE COMPLEMENTARY PRINCIPLE WITH THE HYBRID
MECHANISM SYSTEM
There are two types of methods for constructing
mechanisms in terms of how the motion is created: rigid
body mechanism (RBM) and compliant body mechanism
(CBM) (see Fig. 12). In the former, the motion is generated
based on the articulated joint or so-called kinematic pair in
which two rigid bodies can have a relative motion (the left
part of Fig. 12). In the latter, the motion makes sense in that
one portion (portion 1) of the material deforms significantly
with respect to another portion (portion 2) of the material;
Hybrid Mechanism
Compliant
mechanism
Rigid body
mechanism
Joint
Joint
Portion 2
Portion 1
Weakness: poor accuracy
Strength: good accuracy
Strength: large motion
range
Weakness: small motion
range
Fig. 12: Complementary principle for hybrid
mechanism system
Fig. 13: The serial structure vs the parallel
structure
The fine-tuning process concerns how to integrate them
properly. As we implied before, the traditional approach was
to integrate them in serial. There are several problems with
the serial connectivity. First, there is a backlash and friction
at interfaces between the micro- and macro-systems. Second,
the error is accumulating at the end-effector. Third, the
system is relatively poor in stiffness and thus poor in motion
accuracy in a general sense.
Another way to integrate them is based on the parallel
structure (see the right part of Fig. 13). The parallel structure
can overcome the problems with the serial structure. Fig. 14
shows a particular design of the parallel structure of the
hybrid mechanism system. In this structure, the mechanism
called CMA is shown in Fig. 15. Fig. 16 explains why the
CMA is a parallel structure. In general, the benefit of the
parallel hybrid mechanism over the serial hybrid mechanism
is the same as that of the parallel robot over the serial robot.
5. RELATED WORK
Micro motion
CMA
Macro motion
Fig. 14: The parallel structure of the integration
of the RBM and CBM.
CMA
Output
Five Bar Topology
Inputs
It is worth to mention that in the control literature there is
name called hybrid control [1]. However, the hybrid control
in the control literature and hybrid control in this paper do
not share the same meaning. The hybrid control in the
control literature refers to the mixed continuous driven
discrete event driven control problem, while the hybrid
control in the present paper refers to combine two different
types of control laws based on the hybridization process.
On a general note, a hybrid actuation system concept was
first proposed by Tokuz [11], but it was not quite rooted in
the hybridization concept as described in this paper.
Furthermore, Tokuz [11] used the term of hybrid machine,
which is not adequate according to the definition in the
present paper. Further, in Tokuz’s implementation of the
hybrid actuation concept, he replaced the CV motor with the
servomotor which has a constant velocity profile. This
replacement has actually destroyed the hybrid actuation
concept, because the dynamics of the CV motor and that of
the servomotor with a constant velocity profile are
inherently different [3]. It is only recent years that we have
corrected this mistake and demonstrated this point in several
occasions [5,8-10,12].
6. CONCLUSIONS
Fig. 15: A novel CMA mechanism.
The parallel structure: Explanation
B
Output
B
Output
PZT motor
(micro)
PZT motor
DC motor
(macro)
This paper proposed a new way to construct an intelligent
system – i.e., through the hybridization process in biology.
We concluded that there were two principles for
hybridization: complementary principle and compatibility
principle. In particular, the first one should be considered
before the second one. Along the general idea of
hybridization, we proposed three hybrid machines, namely
(1) hybrid actuation system, (2) hybrid control system, and
(3) hybrid mechanism (or motion system).
We demonstrated the process by applying the two
principles. Because each of these hybrid machine systems
can be very complex, we merely outlined the general
procedures to build them while leaving more details to our
previous work reported elsewhere [8-10].
ACKNOWLEDGEMENTS: All the authors would like to thank
Natural Science and Research Council of Canada (NSERC)
for a financial support to this research through a discovery
grant program.
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DC
motor
Fig. 16: The note for the parallel structure of the
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