KR101564888B1 - Decentralized fault compensation method and apparatus of large-scale nonlinear systems - Google Patents

Decentralized fault compensation method and apparatus of large-scale nonlinear systems Download PDF

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KR101564888B1
KR101564888B1 KR1020150094724A KR20150094724A KR101564888B1 KR 101564888 B1 KR101564888 B1 KR 101564888B1 KR 1020150094724 A KR1020150094724 A KR 1020150094724A KR 20150094724 A KR20150094724 A KR 20150094724A KR 101564888 B1 KR101564888 B1 KR 101564888B1
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control
failure
error
controller
performance
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유성진
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중앙대학교 산학협력단
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    • GPHYSICS
    • G05CONTROLLING; REGULATING
    • G05BCONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
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    • GPHYSICS
    • G05CONTROLLING; REGULATING
    • G05BCONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
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    • G05B23/02Electric testing or monitoring

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Abstract

A distributed fault control method and apparatus for large-scale nonlinear systems are disclosed. Each sub-system included in a large-scale non-linear system includes: a controller for controlling the sub-system according to a control input; And a compensation unit for compensating the control error without a failure diagnosis such that a control error based on a measurable state variable value of the subsystem according to the control input is included within a predetermined performance inheritance range.

Description

[0002] Decentralized fault compensation methods and apparatuses for large-scale nonlinear systems [

The present invention relates to a distributed fault control method and apparatus for a large scale nonlinear system including a physical interconnect structure and a failure in a dead-zone actuator.

Distributed control of large systems involving nonlinear interconnection structures between subsystems has been studied steadily over the past few years. The main advantage of this distributed control is that if a new sub-system is added, there is no need to redesign the control system again, and the controller type of each sub-system can be designed simply.

However, conventional distributed control research has a disadvantage in that it can not compensate for an unknown time delayed failure in a nonlinear interconnect structure due to communication delay between subsystems.

In addition, the conventional distributed control research has a disadvantage in that it can not adaptively compensate for the failure phenomenon even though the dead-zone nonlinearity in the actuator frequently occurs.

SUMMARY OF THE INVENTION The present invention is directed to a distributed fault control method and apparatus that can compensate for faults without fault / anomaly diagnosis in a large scale nonlinear system that includes physical interconnect structures and faults in dead-zone actuators.

According to an aspect of the present invention, there is provided an apparatus for distributed fault control capable of compensating faults without fault / anomaly diagnosis in a large-scale nonlinear system including physical interconnect structures and faults in dead-zone actuators.

According to the first embodiment, in each sub-system included in a large-scale non-linear system, a controller for controlling the sub-system according to a control input; And a compensation unit for compensating the control error without a failure diagnosis so that a control error based on the measurable state variable value of the subsystem according to the control input is included within a predetermined performance inheritance range.

The failure includes a failure in a dead-zone actuator failure and a connection structure between the subsystems.

The compensation unit may compensate the failure without diagnosing the failure based on a function approximation model and error conversion to compensate for the dynamic change due to the failure so that the control error is included within the predetermined performance range.

Wherein the control error is limited to within the range of the performance metric according to the equation,

Figure 112015064468446-pat00001

here,

Figure 112015064468446-pat00002
Lt; / RTI > represents a control error,
Figure 112015064468446-pat00003
Represents a design constant,
Figure 112015064468446-pat00004
Is the relatived performance function and t is the time.

The controller includes a nominal controller and an adaptive fault distribution controller. Even if a failure occurs, the control error is present within the predetermined performance range, so that control performance can be ensured even after a failure occurs.

The controller passes the virtual control input to the first order filter to avoid repeated derivative calculations.

The large-scale non-linear system has a plurality of subsystems physically interconnected.

According to another aspect of the present invention, there is provided a distributed fault control method capable of compensating faults without fault / anomaly diagnosis in a large-scale nonlinear system including a fault in a physical interconnect structure and a dead-zone actuator.

According to a first embodiment, there is provided a method of compensating for the failure of each subsystem included in a large-scale non-linear system, the method comprising the steps of: Obtaining a measurable state variable value according to a control input of each subsystem including a fault; And compensating the control error without a failure diagnosis such that a control error based on the state variable value is included within a predetermined performance range even if a failure occurs.

Performing nominal control on the large scale nonlinear system such that the control error does not deviate from the performance metric range irrespective of failure occurrence;

And performing the performance-based adaptive compensation control on the large-scale nonlinear system when a failure occurs.

Performing the adaptive compensation control comprises: modeling a function approximation model and an error transform to compensate for a dynamic change due to the failure, and modeling the function approximation model; And compensating for the control error based on the modeling to be included within the predetermined performance metric range.

It is an object of the present invention to provide a performance guarantee type distributed fault control method and apparatus for a large scale nonlinear system according to an embodiment of the present invention to provide a faultless and trouble- Can be compensated.

BRIEF DESCRIPTION OF THE DRAWINGS Fig. 1 is a view schematically showing a configuration of each sub system included in a large scale nonlinear system according to the first embodiment; Fig.
2 is a flowchart showing a method for compensating for a failure of a large-scale nonlinear system according to the first embodiment;

BRIEF DESCRIPTION OF THE DRAWINGS The present invention is capable of various modifications and various embodiments, and specific embodiments are illustrated in the drawings and described in detail in the detailed description. It is to be understood, however, that the invention is not to be limited to the specific embodiments, but includes all modifications, equivalents, and alternatives falling within the spirit and scope of the invention. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS Hereinafter, the present invention will be described in detail with reference to the accompanying drawings.

The terms first, second, etc. may be used to describe various components, but the components should not be limited by the terms. The terms are used only for the purpose of distinguishing one component from another.

The terminology used in this application is used only to describe a specific embodiment and is not intended to limit the invention. The singular expressions include plural expressions unless the context clearly dictates otherwise. In the present application, the terms "comprises" or "having" and the like are used to specify that there is a feature, a number, a step, an operation, an element, a component or a combination thereof described in the specification, But do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, or combinations thereof.

The present invention relates to distributed fault control capable of compensating for faults with guaranteed control performance without fault diagnosis (i.e., fault detection) for faults in large scale nonlinear systems consisting of N subsystems. In an embodiment of the present invention, it is assumed that a failure occurs in the interconnect structure between the actuator and the subsystem of the N subsystems. Also, a large-scale nonlinear system according to an embodiment of the present invention is assumed to have a failure including a dead-zone period in the actuator. Here, the dead zone refers to a section until the actual control input is input, but the output of the actuator is accordingly output.

Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings.

As already mentioned above, a large-scale non-linear system according to an embodiment of the present invention includes N subsystems. It is assumed that a large nonlinear system is an interconnected structure with an unknown time delay between subsystems and that each subsystem includes dead-zone actuator failures.

FIG. 1 is a view schematically showing a configuration of each subsystem included in a large-scale nonlinear system according to the first embodiment.

Referring to FIG. 1, each subsystem included in a large-scale nonlinear system includes a controller 110 and a compensation unit 115.

The controller 110 is a means for controlling the subsystem according to the control input.

As will be described in greater detail below, the controller 110 is a means for controlling the subsystem to follow the desired signal at the output of each subsystem.

For example, the controller 110 may generate a control input such that the control error does not deviate regardless of whether the performance range is faulty. In addition, the controller 110 includes a nominal controller in a state in which no failure has occurred, and a failure compensation controller that performs adaptive control by compensating the failure after occurrence of a failure. This will be more clearly understood from the following description.

The compensation unit 115 compensates for the control error based on the measurable state variable value of the subsystem according to the control input without any failure diagnosis so that the control error is always included within the predetermined performance inheritance range.

As already mentioned above, a large-scale nonlinear system according to an embodiment of the present invention includes interconnected structures with unknown time delays between each of the subsystems and failures in the dead-zone actuators of each subsystem. Generally, systems that are physically interconnected operate have dead-zone nonlinearities in the actuator and an unknown delayed interconnect structure. Thus, the unknown interconnect structure between each subsystem and the failure in the dead-zone actuator must be compensated locally without delay.

Therefore, the large-scale nonlinear system according to an embodiment of the present invention can be configured to have a physically interconnected structure having an unknown time delay between each of the subsystems, and a control error set in advance without diagnosing faults / faults in the dead- Failures can be compensated without leaving the range. This will be more clearly understood by the following.

2 is a flowchart illustrating a method of compensating for a failure of a large-scale nonlinear system according to the first embodiment.

In step 210, each sub-system obtains a measurable state variable value according to the control input.

Each subsystem in step 215 compensates for the control error without anomaly / failure diagnosis so that the control error based on the state variable value is always included within the predetermined performance metric range.

Here, each subsystem can perform nominal control such that the control error is included within the range of the performance tolerance regardless of whether a failure has occurred or not. In addition, each subsystem can perform adaptive compensation control for the large nonlinear system when a failure occurs.

For example, each subsystem uses a function approximation model to compensate for dynamic changes due to failure, modeling it as a function approximator, and then compensating the control error to be included within a predetermined performance range based on the result, can do.

This will be more clearly understood from the following description.

Modeling a large nonlinear system that includes physically interconnected structures between the N subsystems and failures in the dead-zone actuators of each subsystem is shown in Equation (1).

Figure 112015064468446-pat00005

here,

Figure 112015064468446-pat00006
,
Figure 112015064468446-pat00007
,
Figure 112015064468446-pat00008
,
Figure 112015064468446-pat00009
Figure 112015064468446-pat00010
,
Figure 112015064468446-pat00011
Represents the state variable and the control variable of the i-th subsystem, respectively.

Figure 112015064468446-pat00012
Figure 112015064468446-pat00013
Represents a delayed state variable,
Figure 112015064468446-pat00014
0.0 > a < / RTI > unknown,

Figure 112015064468446-pat00015
and
Figure 112015064468446-pat00016
. here,
Figure 112015064468446-pat00017
,
Figure 112015064468446-pat00018
,
Figure 112015064468446-pat00019
Represents an unknown positive constant.
Figure 112015064468446-pat00020
Represents the initial condition of the state variable.

Figure 112015064468446-pat00021
and
Figure 112015064468446-pat00022
Known
Figure 112015064468446-pat00023
Represents a non-linear function,
Figure 112015064468446-pat00024
Lt; RTI ID = 0.0 > time-delayed < / RTI >
Figure 112015064468446-pat00025
Represents a nonlinear function.

Unknown

Figure 112015064468446-pat00026
Nonlinear term
Figure 112015064468446-pat00027
Represents the change in the time delayed interconnect effect in the ith subsystem due to failure.
Figure 112015064468446-pat00028
Unknown time (
Figure 112015064468446-pat00029
) Of the failure.
Figure 112015064468446-pat00030
Can be regarded as the time between the initial failure time just started and the time the failure is detected. Here, in the case of an incipient fault,
Figure 112015064468446-pat00031
If
Figure 112015064468446-pat00032
ego,
Figure 112015064468446-pat00033
If
Figure 112015064468446-pat00034
Lt; / RTI > increases monotonically. In the case of an abrupt fault,
Figure 112015064468446-pat00035
If
Figure 112015064468446-pat00036
ego,
Figure 112015064468446-pat00037
If
Figure 112015064468446-pat00038
to be.
Figure 112015064468446-pat00039
and
Figure 112015064468446-pat00040
To ensure control stability of feedback control technology
Figure 112015064468446-pat00041
,
Figure 112015064468446-pat00042
For each, the distance from the origin is assumed and assumed to be a positive number.

Dead-zone actuator of the i-th sub-system that contains an unknown fault (

Figure 112015064468446-pat00043
) Can be modeled as shown in Equation (2).

Figure 112015064468446-pat00044

here,

Figure 112015064468446-pat00045
Represents an unknown constant representing a valid partial loss,
Figure 112015064468446-pat00046
Is a constant indicating an unknown actuator bias.
Figure 112015064468446-pat00047
Is a non-symmetric dead-zone nonlinearity, as shown in Equation (3).

Figure 112015064468446-pat00048

here,

Figure 112015064468446-pat00049
and
Figure 112015064468446-pat00050
Represents a left-right slope characteristic of a dead-zone.
Figure 112015064468446-pat00051
and
Figure 112015064468446-pat00052
Represents the break-point of input nonlinearity.

Equation (3) can be rewritten as Equation (4).

Figure 112015064468446-pat00053

here,

Figure 112015064468446-pat00054
to be.

From the equations (2) and (4), the dead-zone actuator model of the ith subsystem having an unknown failure can be redefined as shown in equation (5).

Figure 112015064468446-pat00055

Remark 1: If there is no failure, the dead-zone actuator model

Figure 112015064468446-pat00056
ego,
Figure 112015064468446-pat00057
Lt; / RTI > Here, i = 1, ..., N.

Remark 2: Equation 1 shows the actual nonlinear time-delay effect of time-delayed interconnections and dead-zone actuator failures, such as interconnected recycling storage tanks, interconnected wind tunnels, interconnected cooling rollers with transmission delay, System. Most interconnected machine-operated systems have unknown delayed interconnections and dead-zone non-linearities in the actuators. Thus, failures in these unknown interconnections and dead-zone nonlinearities must be compensated locally without delay information.

Assumption 1: Output of dead-zone actuator (

Figure 112015064468446-pat00058
) Are not available as feedback. Here, i is 1, ..., N.

Assumption 2: Dead-zone parameters

Figure 112015064468446-pat00059
,
Figure 112015064468446-pat00060
,
Figure 112015064468446-pat00061
,
Figure 112015064468446-pat00062
Is an unknown positive number,
Figure 112015064468446-pat00063
,
Figure 112015064468446-pat00064
There is an unknown constant. here,
Figure 112015064468446-pat00065
ego,
Figure 112015064468446-pat00066
Figure 112015064468446-pat00067
to be. Also, i = 1, ..., N.

Remark 3: By assumption 2, unknown constants (

Figure 112015064468446-pat00068
), Control input (
Figure 112015064468446-pat00069
) ≪ / RTI >
Figure 112015064468446-pat00070
) Was not always known. It is very difficult to deal with this term for which there is an unknown failure.

Assumption 3: Unknown time delayed interconnection function (

Figure 112015064468446-pat00071
) And failure
Figure 112015064468446-pat00072
)silver
Figure 112015064468446-pat00073
and
Figure 112015064468446-pat00074
Figure 112015064468446-pat00075
Respectively. here,
Figure 112015064468446-pat00076
ego,
Figure 112015064468446-pat00077
Lt;
Figure 112015064468446-pat00078
Unknown
Figure 112015064468446-pat00079
Class-K function.

This assumption is widely used in the field of control of nonlinear time-delay systems.

The purpose of a large-scale nonlinear system according to an embodiment of the present invention is to detect the presence of an unknown time delayed interconnect structure and a dead-zone actuator failure while the transient performance of all error surfaces is preserved within a given set- Delayed independent, distributed fault compensation controller for the system.

Remark 4: In one embodiment of the present invention, a failure in a time-delayed interconnect structure between each sub system of a large-scale nonlinear system and an unknown failure in a dead-zone actuator are detected in a distributed fault control problem Will be described. Also, the time delayed nonlinear interconnect structure is unmatched within the control input.

Remark 5: Distributed fault-compensated control in these large-scale nonlinear systems can be extended to a number of failures occurring within dead-zone actuators and time-delayed interconnect structures in the local subsystem.

However, in order to facilitate understanding and explanation, a single failure in the interconnect structure that will be delayed with the dead-zone actuator will be described below.

The controller 110 according to an exemplary embodiment of the present invention applies a function approximation technique used in a neural network to compensate for an unknown nonlinear function according to a control input.

Hereinafter, the function approximation technique will be briefly described as follows.

Figure 112015064468446-pat00080
The
Figure 112015064468446-pat00081
in
Figure 112015064468446-pat00082
Lt; RTI ID = 0.0 > unknown < / RTI > smooth function. The function approximator is a compact set (
Figure 112015064468446-pat00083
Lt; RTI ID = 0.0 > (e. G., ≪ / RTI &
Figure 112015064468446-pat00084
) Can be approximated.

Nonlinear function

Figure 112015064468446-pat00085
Figure 112015064468446-pat00086
) Is approximated by Equation (6).

Figure 112015064468446-pat00087

here,

Figure 112015064468446-pat00088
Represents the input of a function approximator,
Figure 112015064468446-pat00089
Represents the output of the function approximator. Also,
Figure 112015064468446-pat00090
Indicates a restoration error,
Figure 112015064468446-pat00091
Is an optimal weight vector
Figure 112015064468446-pat00092
), ≪ / RTI >
Figure 112015064468446-pat00093
.

Assumption 4: The reconstruction error and the optimal weight vector are

Figure 112015064468446-pat00094
and
Figure 112015064468446-pat00095
Respectively. here,
Figure 112015064468446-pat00096
Wow
Figure 112015064468446-pat00097
Is a positive constant,
Figure 112015064468446-pat00098
Represents the Euclidian.

Stated value (

Figure 112015064468446-pat00099
) Is not required for failure compensation control in a large scale nonlinear system according to an embodiment of the present invention, which is only used for stability analysis of the system.

For learning of all weights of large nonlinear systems

Figure 112015064468446-pat00100
On by
Figure 112015064468446-pat00101
(Taylor series) of equation

Figure 112015064468446-pat00102

here,

Figure 112015064468446-pat00103
,
Figure 112015064468446-pat00104
And
Figure 112015064468446-pat00105
Is a high-order term. Substituting Equation (7) into Equation (6), Equation (8) is obtained.

Figure 112015064468446-pat00106

Figure 112015064468446-pat00107

here,

Figure 112015064468446-pat00108
Represents a residual approximation error,
Figure 112015064468446-pat00109
Is an unknown value that can be predicted.

Remark 6: Neural networks, wavelet neural networks, and fuzzy systems can be used as function analyzers.

As described above, a large-scale nonlinear system according to an embodiment of the present invention can compensate for failures in a time-delayed interconnect structure that is unmatched with an unknown dead-zone actuator failure within a predetermined performance metric range without any diagnosis have.

The large-scale nonlinear system according to an embodiment of the present invention can compensate the transient performance of the error surface based on the performance curve through the preset performance-based distributed dynamic surface design.

The performance-based error surface is used to compensate for distributed dynamic surface failures.

This is explained as follows.

The error surface vector of the ith subsystem is

Figure 112015064468446-pat00110
Figure 112015064468446-pat00111
Can be defined as follows. here,
Figure 112015064468446-pat00112
,
Figure 112015064468446-pat00113
,
Figure 112015064468446-pat00114
,
Figure 112015064468446-pat00115
Figure 112015064468446-pat00116
Represents the kth filtered virtual control input of the ith sub-system. The preset performance of the error surface depends on each error (
Figure 112015064468446-pat00117
) Is generated in a predetermined flow system as shown in Equation (10).

Figure 112015064468446-pat00118

here,

Figure 112015064468446-pat00119
,
Figure 112015064468446-pat00120
Figure 112015064468446-pat00121
Represents a design constant,
Figure 112015064468446-pat00122
Lt; RTI ID = 0.0 > smoothed < / RTI > function
Figure 112015064468446-pat00123
to be.
Figure 112015064468446-pat00124
Is a constant. The performance function
Figure 112015064468446-pat00125
And so on. here,
Figure 112015064468446-pat00126
,
Figure 112015064468446-pat00127
And
Figure 112015064468446-pat00128
Is a positive constant,
Figure 112015064468446-pat00129
Lt;
Figure 112015064468446-pat00130
The
Figure 112015064468446-pat00131
It is selected when the condition is satisfied.

In addition,

Figure 112015064468446-pat00132
) Is in a steady-state condition that can be artificially adjusted to a small value
Figure 112015064468446-pat00133
≪ / RTI > Reduction rate (
Figure 112015064468446-pat00134
)silver
Figure 112015064468446-pat00135
The lower limit of convergence speed.
Figure 112015064468446-pat00136
The maximum overshoot of
Figure 112015064468446-pat00137
. Therefore, the performance function (
Figure 112015064468446-pat00138
) And constant (
Figure 112015064468446-pat00139
,
Figure 112015064468446-pat00140
) Is the error surface (
Figure 112015064468446-pat00141
) Can be appropriately determined within the performance curve.

For compensation of the compensation unit 115, the performance-based-based error surface of Equation (10) can be modified as shown in Equation (11).

Figure 112015064468446-pat00142

here,

Figure 112015064468446-pat00143
,
Figure 112015064468446-pat00144
,
Figure 112015064468446-pat00145
Lt; / RTI > represents a modified error,
Figure 112015064468446-pat00146
Is the shear mapping (
Figure 112015064468446-pat00147
) ≪ / RTI > By shearing thought
Figure 112015064468446-pat00148
.

The candidate distortion function can be selected as shown in Equation (12).

Figure 112015064468446-pat00149

here,

Figure 112015064468446-pat00150
,
Figure 112015064468446-pat00151
ego,
Figure 112015064468446-pat00152
Represents a positive design constant. if
Figure 112015064468446-pat00153
If so,
Figure 112015064468446-pat00154
Due to
Figure 112015064468446-pat00155
to be.

Lemma 1: Error surface (

Figure 112015064468446-pat00156
) And deformation error (
Figure 112015064468446-pat00157
). here,
Figure 112015064468446-pat00158
ego,
Figure 112015064468446-pat00159
to be. if
Figure 112015064468446-pat00160
, The set performance of the error surface is
Figure 112015064468446-pat00161
Lt; / RTI > In other words, equation (10) is satisfied.

Proof:

Figure 112015064468446-pat00162
After that,
Figure 112015064468446-pat00163
About
Figure 112015064468446-pat00164
to be.
Figure 112015064468446-pat00165
Figure 112015064468446-pat00166
Lt; RTI ID = 0.0 >
Figure 112015064468446-pat00167
Set performance of
Figure 112015064468446-pat00168
To prove that
Figure 112015064468446-pat00169
Can be obtained.

Performance-based error surface (

Figure 112015064468446-pat00170
) Is expressed by Equation (11), and the boundary layer error
Figure 112015064468446-pat00171
) Is defined as Equation (13).

Figure 112015064468446-pat00172

here,

Figure 112015064468446-pat00173
,
Figure 112015064468446-pat00174
,
Figure 112015064468446-pat00175
Represents a virtual control,
Figure 112015064468446-pat00176
Represents the filtered virtual control. Thus, the kth virtual control law of the ith subsystem (
Figure 112015064468446-pat00177
)silver
Figure 112015064468446-pat00178
Can be rewritten as shown in FIG. here,
Figure 112015064468446-pat00179
Is a nominal control part,
Figure 112015064468446-pat00180
Shows an approximation-based adaptive control part for compensating for unknown functions and faults.

Step 1: Consider the first error surface based on the performance metric. In Equations (1), (11), (12) and

Figure 112015064468446-pat00181
The difference between
Figure 112015064468446-pat00182
Figure 112015064468446-pat00183
Respectively.

here,

Figure 112015064468446-pat00184
Figure 112015064468446-pat00185
.

Figure 112015064468446-pat00186
The term is not zero and is well defined according to the condition of equation (10).

First Virtual Control Law (

Figure 112015064468446-pat00187
) Of the nominal control part (
Figure 112015064468446-pat00188
) Is designed as shown in Equation (14).

Figure 112015064468446-pat00189

here,

Figure 112015064468446-pat00190
ego,
Figure 112015064468446-pat00191
and
Figure 112015064468446-pat00192
Is a design parameter,
Figure 112015064468446-pat00193
,
Figure 112015064468446-pat00194
to be. Lyapunov Candidate Function (
Figure 112015064468446-pat00195
)
Figure 112015064468446-pat00196
, The following equation
Figure 112015064468446-pat00197
Is the time derivative of equation (15).

Figure 112015064468446-pat00198

Equations 16 through 18 can be derived using Assumption 3.

Figure 112015064468446-pat00199

Figure 112015064468446-pat00200

Figure 112015064468446-pat00201

Figure 112015064468446-pat00202

Substituting Equations (16) to (18) into Equation (15) yields Equation (19).

Figure 112015064468446-pat00203

Here,

Figure 112015064468446-pat00204
Is used. Also,
Figure 112015064468446-pat00205
Is a variable,
Figure 112015064468446-pat00206
Is a positive constant.

Filtered virtual control (

Figure 112015064468446-pat00207
), The virtual control input (
Figure 112015064468446-pat00208
Pass a low-pass first-order filter. here,
Figure 112015064468446-pat00209
Is a time constant. This can be expressed by the following equation (20).

Figure 112015064468446-pat00210

Step k (k = 2, ...,

Figure 112015064468446-pat00211
): Considering the kth equation of the i < th > subsystem of equation (1), the kth error surface in equations (1),
Figure 112015064468446-pat00212
)
Figure 112015064468446-pat00213
Figure 112015064468446-pat00214
Figure 112015064468446-pat00215
to be. here,
Figure 112015064468446-pat00216
Figure 112015064468446-pat00217
to be.

The kth virtual control (

Figure 112015064468446-pat00218
) Of the nominal control law (
Figure 112015064468446-pat00219
) ≪ / RTI >

Figure 112015064468446-pat00220

here,

Figure 112015064468446-pat00221
,
Figure 112015064468446-pat00222
and
Figure 112015064468446-pat00223
Are design parameters, respectively,
Figure 112015064468446-pat00224
,
Figure 112015064468446-pat00225
to be.

Lyapunov Candidate Function (

Figure 112015064468446-pat00226
), The following equations (21) and
Figure 112015064468446-pat00227
,
Figure 112015064468446-pat00228
Figure 112015064468446-pat00229
,
Figure 112015064468446-pat00230
Figure 112015064468446-pat00231
If you use,
Figure 112015064468446-pat00232
≪ EMI ID = 22.0 >

Figure 112015064468446-pat00233

Figure 112015064468446-pat00234
Pass through the k-th low-pass first-order filter to generate a k-th filtered virtual control vector (
Figure 112015064468446-pat00235
) Is obtained, which can be expressed by the following equation (23).

Figure 112015064468446-pat00236

here,

Figure 112015064468446-pat00237
Is a time constant.

Step ni: Considering the ni th equation of the subsystem of Equation (1)

Figure 112015064468446-pat00238
Is the ni-th error surface.

Using Equations (1) and (5)

Figure 112015064468446-pat00239
Can be expressed as follows.

Figure 112015064468446-pat00240

here,

Figure 112015064468446-pat00241
,
Figure 112015064468446-pat00242
Figure 112015064468446-pat00243
to be.

Consequently, as a result,

Figure 112015064468446-pat00244
) ≪ / RTI >
Figure 112015064468446-pat00245
) ≪ / RTI >

Figure 112015064468446-pat00246

here,

Figure 112015064468446-pat00247
Is a design parameter.

Lyapunov Candidate Function (

Figure 112015064468446-pat00248
)
Figure 112015064468446-pat00249
As a result,
Figure 112015064468446-pat00250
≪ / RTI > here,
Figure 112015064468446-pat00251
Is an unknown constant.

Figure 112015064468446-pat00252

Figure 112015064468446-pat00253

here,

Figure 112015064468446-pat00254
to be.

Using the inequality, Equation (26) and Equation (27) are obtained.

Figure 112015064468446-pat00255

Figure 112015064468446-pat00256

Applying this to Equation (25), Equation (28) is obtained.

Figure 112015064468446-pat00257

Remark 7: Fault-compensated control can be extended using Beck stepping design for nonlinear feedback systems where actuator failure is considered. In addition, a predetermined performance metric can be used for the initial error definition of the backstepping design process.

However, a large-scale nonlinear system according to an embodiment of the present invention relates to failure compensation including failures in a structure in which N subsystems are physically interconnected and failures in a dead-zone actuator.

Thus, the predetermined performance metric is used for the definition of the error surface in the dynamic surface design process, such as Equation 11, and in the virtual control law of Equation 21

Figure 112015064468446-pat00258
Lt; RTI ID = 0.0 >
Figure 112015064468446-pat00259
. ≪ / RTI >

The controller 110 according to one embodiment of the present invention performs a function approximation model and adaptive control to compensate for changes in the dynamic due to time delayed interconnections and dead-zone actuator failures.

Hereinafter, this will be described.

Remark 8: The performance-based error surface of Equation 11 can be summarized as follows.

Figure 112015064468446-pat00260
Here, i = 1, ..., N,
Figure 112015064468446-pat00261
to be. Assumption 3 and decreasing function (
Figure 112015064468446-pat00262
),
Figure 112015064468446-pat00263
and
Figure 112015064468446-pat00264
Figure 112015064468446-pat00265
Can be easily obtained. here,
Figure 112015064468446-pat00266
and
Figure 112015064468446-pat00267
Is a filtered virtual control (
Figure 112015064468446-pat00268
),
Figure 112015064468446-pat00269
and
Figure 112015064468446-pat00270
Unknown
Figure 112015064468446-pat00271
Class-K function.

Also,

Figure 112015064468446-pat00272
and
Figure 112015064468446-pat00273
Figure 112015064468446-pat00274
Lt; / RTI >

The functional approximation-based distributed virtual controller and the real controller to compensate for the failure are defined as shown in equations (29) and (32).

Figure 112015064468446-pat00275

Figure 112015064468446-pat00276

Figure 112015064468446-pat00277

Figure 112015064468446-pat00278

Figure 112015064468446-pat00279

Figure 112015064468446-pat00280

Figure 112015064468446-pat00281

here,

Figure 112015064468446-pat00282
,
Figure 112015064468446-pat00283
,
Figure 112015064468446-pat00284
Wow
Figure 112015064468446-pat00285
Is a tuning parameter,
Figure 112015064468446-pat00286
and
Figure 112015064468446-pat00287
The
Figure 112015064468446-pat00288
- a positive constant for adjustment,
Figure 112015064468446-pat00289
Figure 112015064468446-pat00290
,
Figure 112015064468446-pat00291
and
Figure 112015064468446-pat00292
The
Figure 112015064468446-pat00293
and
Figure 112015064468446-pat00294
. Also,
Figure 112015064468446-pat00295
Is a nonlinear function
Figure 112015064468446-pat00296
Figure 112015064468446-pat00297
, ≪ / RTI >
Figure 112015064468446-pat00298
Is a nonlinear function
Figure 112015064468446-pat00299
Figure 112015064468446-pat00300
Figure 112015064468446-pat00301
.

Figure 112015064468446-pat00302
,
Figure 112015064468446-pat00303
,
Figure 112015064468446-pat00304
Figure 112015064468446-pat00305
,
Figure 112015064468446-pat00306
,
Figure 112015064468446-pat00307
to be. here,
Figure 112015064468446-pat00308
Figure 112015064468446-pat00309
.
Figure 112015064468446-pat00310
and
Figure 112015064468446-pat00311
Is presented in the stability analysis process described in the proof of theory 1.

For adaptive control through stability analysis and function approximation based fault compensation, the Lyapunov-Krasovskii function term (

Figure 112015064468446-pat00312
), The following equation (36) is obtained.

Figure 112015064468446-pat00313

here,

Figure 112015064468446-pat00314
,
Figure 112015064468446-pat00315
,

Figure 112015064468446-pat00316
.

Theorem 1: Large-scale system with fault and dead-zone actuator failures in unknown time-varying delayed interconnect structures, distributed nominal control of equations 14, 21 and 24, decentralized control based on functionally approximated models of equations 29-34 Consider a closed loop fault compensation control consisting of an adaptive controller.

Under Home 1-4,

Figure 112015064468446-pat00317
, All signals of the closed-loop system are uniformly distributed, and the error surface (< RTI ID = 0.0 >
Figure 112015064468446-pat00318
) Converge to near the origin adjustably.

In addition, the transient performance of all system states, despite failure in the unknown time delayed nonlinear interconnect structure and dead-zone actuator failure,

Figure 112015064468446-pat00319
Within a predetermined performance metric.

Proof: Assume that the dynamics of the boundary error surface are as shown in equations (37) to (38).

Figure 112015064468446-pat00320

Figure 112015064468446-pat00321

here,

Figure 112015064468446-pat00322
,
Figure 112015064468446-pat00323
,
Figure 112015064468446-pat00324
and
Figure 112015064468446-pat00325
Is a continuous function representing the derivative of the virtual controller.

The time variations of V can be expressed by Equation (39) by Equations (19), (22), (28), (37)

Figure 112015064468446-pat00326

Figure 112015064468446-pat00327

Figure 112015064468446-pat00328
Figure 112015064468446-pat00329
And Remark 8, Equation (39) can be summarized as Equation (40).

Figure 112015064468446-pat00330

Using Equations (29), (32) and (40), Equation (41) can be obtained.

Figure 112015064468446-pat00331

here,

Figure 112015064468446-pat00332
Is derived from Equations (34) and (35)
Figure 112015064468446-pat00333
Is used.

From equations (8), (9), (30), (31), and (33), this inequality can be summarized as follows.

Figure 112015064468446-pat00334

set

Figure 112015064468446-pat00335
The
Figure 112015064468446-pat00336
Figure 112015064468446-pat00337
. here,
Figure 112015064468446-pat00338
,
Figure 112015064468446-pat00339
to be.
Figure 112015064468446-pat00340
end
Figure 112015064468446-pat00341
Because it is a compact set within,
Figure 112015064468446-pat00342
Lt; / RTI >
Figure 112015064468446-pat00343
in
Figure 112015064468446-pat00344
to be. here,
Figure 112015064468446-pat00345
The
Figure 112015064468446-pat00346
.

Figure 112015064468446-pat00347
Figure 112015064468446-pat00348
≪ / RTI >
Figure 112015064468446-pat00349
and
Figure 112015064468446-pat00350
Can be summarized as follows.

Figure 112015064468446-pat00351

here,

Figure 112015064468446-pat00352
Figure 112015064468446-pat00353
to be.

Figure 112015064468446-pat00354
top
Figure 112015064468446-pat00355
Therefore, the inequality
Figure 112015064468446-pat00356
. here,
Figure 112015064468446-pat00357
The
Figure 112015064468446-pat00358
Lt; / RTI >
Figure 112015064468446-pat00359
,
Figure 112015064468446-pat00360
,
Figure 112015064468446-pat00361
to be.

The inequality above

Figure 112015064468446-pat00362
If so,
Figure 112015064468446-pat00363
in
Figure 112015064468446-pat00364
. therefore,
Figure 112015064468446-pat00365
Is an invariant set. E.g,
Figure 112015064468446-pat00366
If so,
Figure 112015064468446-pat00367
About
Figure 112015064468446-pat00368
to be. Integrating the inequality for time can yield equation (42).

Figure 112015064468446-pat00369

Figure 112015064468446-pat00370
If you use,
Figure 112015064468446-pat00371
Figure 112015064468446-pat00372
Is satisfied.

Thus, as time increases, the modified error surface exponentially becomes a compact set

Figure 112015064468446-pat00373
Figure 112015064468446-pat00374
. ≪ / RTI >

Compact set (

Figure 112015064468446-pat00375
)
Figure 112015064468446-pat00376
To be arbitrarily small. From Lemma 1
Figure 112015064468446-pat00377
And the predetermined performance of all states of the system is guaranteed. Thus, the temporary performance is guaranteed within the performance metric preset in Equation (10). Also,
Figure 112015064468446-pat00378
The
Figure 112015064468446-pat00379
, It can be arbitrarily reduced to a small size.

Remark 9: From the proof of theory 1, the design parameters can be chosen to ensure a preset performance. Performance function (

Figure 112015064468446-pat00380
)for
Figure 112015064468446-pat00381
Figure 112015064468446-pat00382
The parameters can be appropriately selected to adjust the transient and stable state performance curve.
Figure 112015064468446-pat00383
and
Figure 112015064468446-pat00384
Determines the transient response performance curve
Figure 112015064468446-pat00385
Represents the steady-state performance curve. From equation (42)
Figure 112015064468446-pat00386
The satisfaction of the transient / steady-state performance of
Figure 112015064468446-pat00387
Which is related to the clear design parameters for Therefore, the initial error is set small and the large attenuation rate (
Figure 112015064468446-pat00388
) To obtain
Figure 112015064468446-pat00389
,
Figure 112015064468446-pat00390
,
Figure 112015064468446-pat00391
Is appropriately selected. Furthermore
Figure 112015064468446-pat00392
In order to make small,
Figure 112015064468446-pat00393
and
Figure 112015064468446-pat00394
Can be fixed.

In conclusion, from Lemma 1,

Figure 112015064468446-pat00395
May be made arbitrarily small while in the preset performance metric.

Remark 10: Distributed adaptive beck stepping control technique is proposed for large nonlinear time delay systems with dead-zone nonlinearity. Since the delayed outputs between subsystems are interacted, the dead - zone inverse model is required for controller design, and the derivative functions and unknown nonlinearities for delayed interactions are known.

However, a large nonlinear system according to an embodiment of the present invention considers interconnection between subsystems associated with all delayed state variables, and knowledge of the dead-zone inverse model and the derivative functions is not required for controller design Do not.

In addition, the time-delayed interconnect structure and the dead-zone actuator dispersion fault compensation method of a large-scale nonlinear system according to an embodiment of the present invention is characterized in that the transient / steady-state performance having a predetermined performance- And is simpler than the back stepping controller since it does not require repeated derivatives of the virtual controller.

The distributed fault compensation control of equations (29) and (32) can be used to compensate for dead-zone nonlinearity and to compensate for the failure of the time delayed interconnect structure, despite the unmatched time delayed interconnections associated with the state variables of the subsystem , A function approximator with one or three inputs. In particular, the absolute value of the function approximator of Equation (32) and the condition of Equation (35) for the adaptation law of Equation (34) are the unknown control term caused by the dead-zone nonlinearity of Remark 3

Figure 112015064468446-pat00396
) Processing.

It will be apparent to those skilled in the art that various modifications and variations can be made in the present invention without departing from the spirit or scope of the invention as defined in the appended claims. It will be understood that the invention may be varied and varied without departing from the scope of the invention.

110:
115:

Claims (11)

For each subsystem included in a large nonlinear system,
A controller for controlling the sub-system according to a control input; And
And a compensation unit for compensating the control error without a failure diagnosis such that a control error based on a measurable state variable value of the subsystem according to the control input is included within a predetermined performance inheritance range,
Wherein the compensation unit comprises:
Compensating the control error to be included within a predetermined performance range based on a function approximation model and error distortion to compensate for the dynamic change due to the failure,
The controller comprising a nominal controller and an adaptive fault distribution controller,
Wherein the nominal controller performs nominal control on the large scale nonlinear system such that the control error is included within the performance range regardless of whether a failure has occurred and the adaptive failure distribution controller is configured to control the large non- And performs adaptive compensation control on the sub-system.
The method according to claim 1,
Wherein the failure includes a failure in a dead-zone actuator failure and a time delayed connection between subsystems.
delete The method according to claim 1,
Wherein the control error is limited within a range of performance ratios according to the following equation.
Figure 112015064468446-pat00397

here,
Figure 112015064468446-pat00398
Lt; / RTI > represents a control error,
Figure 112015064468446-pat00399
Represents a design constant,
Figure 112015064468446-pat00400
Is the relatived performance function, and t is the time.
delete The method according to claim 1,
Wherein the controller passes the control input through a first filter.
The method according to claim 1,
Wherein the large nonlinear system is a plurality of subsystems physically interconnected.
A method of compensating for a failure in each subsystem included in a large nonlinear system, the subsystem including a failure in a time delayed connection structure between a dead-zone actuator failure and subsystems,
Obtaining a measurable state variable value according to a control input of each sub system;
Compensating the control error without a fault diagnosis such that a control error based on the state variable value is included within a predetermined performance range;
Performing nominal control on the large-scale nonlinear system such that the control error is included within the range of the performance metric, irrespective of the occurrence of a fault; And
Performing adaptive compensation control on the large nonlinear system when a failure occurs,
Wherein performing the adaptive compensation control comprises:
Modeling a function approximation model by applying a function approximation model to compensate for the dynamic change due to the failure; And
And compensating for the control error to be included within the predetermined performance range based on the modeling.
delete delete A computer-readable recording medium having recorded thereon a program code for performing the method according to claim 8.

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Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN106444653A (en) * 2016-08-19 2017-02-22 苏州大学 Fault detection method and system
KR101873938B1 (en) * 2016-07-07 2018-07-04 중앙대학교 산학협력단 Control apparatus and method for Uncertain interconnected systems with time-delayed nonlinear faults
CN111953009A (en) * 2019-05-17 2020-11-17 天津科技大学 Fault diagnosis method for island multi-inverter parallel sensor

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JP2008307640A (en) 2007-06-14 2008-12-25 Honda Motor Co Ltd Motion control system, motion control method, and motion control program

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* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2008307640A (en) 2007-06-14 2008-12-25 Honda Motor Co Ltd Motion control system, motion control method, and motion control program

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
KR101873938B1 (en) * 2016-07-07 2018-07-04 중앙대학교 산학협력단 Control apparatus and method for Uncertain interconnected systems with time-delayed nonlinear faults
CN106444653A (en) * 2016-08-19 2017-02-22 苏州大学 Fault detection method and system
CN111953009A (en) * 2019-05-17 2020-11-17 天津科技大学 Fault diagnosis method for island multi-inverter parallel sensor

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