Mathematics 08-00326

mathematics
Article
Improved Decentralized Fractional PD Control of
Structure Vibrations
Kang Xu 1, Liping Chen 2,∗, Minwu Wang 1, António M. Lopes 3, J. A. Tenreiro Machado 4
and Houzhen Zhai 5
1 College of Civil Engineering, Hefei University of Technology, Hefei 230009, China;
kangxu211@163.com (K.X.); wanglab307@foxmailcom (M.W.)
2 School of Electrical Engineering and Automation, Hefei University of Technology, Hefei 230009, China
3 UISPA–LAETA/INEGI, Faculty of Engineering, University of Porto, Rua Dr. Roberto Frias,
4200-465 Porto, Portugal; aml@fe.up.pt
4 Institute of Engineering, Polytechnic of Porto, Department of Electrical Engineering,
R. Dr. António Bernardino de Almeida, 431, 4249-015 Porto, Portugal; jtm@isep.ipp.pt
5 The 29th Research Institute of CETC, Chengdu 610036, China; zhaihouzhen@126.com
* Correspondence: lip_chen@hfut.edu.cn
Received: 6 November 2019; Accepted: 22 February 2020; Published: 2 March 2020
Abstract: This paper presents a new strategy for the control of large displacements in structures
under earthquake excitation. Firstly, an improved fractional order proportional-derivative (FOPD)
controller is proposed. Secondly, a decentralized strategy is designed by adding a regulator and
fault self-regulation to a standard decentralized controller. A new control architecture is obtained
by combining the improved FOPD and the decentralized strategy. The parameters of the control
system are tuned using an intelligent optimization algorithm. Simulation results demonstrate the
performance and reliability of the proposed method.
Keywords: fractional order controller; determined control strategy; vibration of building structures
1. Introduction
Building structures occasionally suffer from unpredictable earthquakes, strong winds, or
other natural hazards that may cause severe damage and threaten human lives. Thus, effective
control methods are needed to protect against structural vibration in buildings [1,2]. During the
past few decades, a variety of control techniques, including linear quadratic regulator (LQR) [3],
sliding-mode [4], neural network [5], fuzzy [6], neural terminal sliding-mode [7], disturbance
rejection [8], and proportional-derivative (PD) [9,10] algorithms were analyzed. For example, a
new scheme comprising a two-loop sliding system in conjunction with a dynamic state predictor
was proposed for controlling an active tuned mass damper in a high-rise building [4]. A neural
network for reducing the vibrations of a 3-story scaled structure exposed to the T¯ohoku 2011 and
Boumerdès 2003 earthquakes was tested [5]. A neural terminal sliding-mode controller, combining a
terminal sliding-mode and a hyperbolic tangent function, so that the controlled system could stabilize
in ﬁnite-time without chattering, was proposed [7].
The PD algorithm has been widely used in engineering practice due to its simple structure and
easy implementation. However, when confronted with high dynamical requirements, a classical PD is
not able to achieve satisfactory results. To improve the performance of classical PD algorithms, the
fractional order PD (FOPD) was introduced [11]. This scheme was applied to the control of motors [12],
machines [13], robots [14], and building structures [15], among others [16–19], but, in the case of some
complex systems, the FOPD still reveals some limitations and needs to be improved.
Mathematics 2020, 8, 326; doi:10.3390/math8030326 www.mdpi.com/journal/mathematics

Mathematics 2020, 8, 326 2 of 13
For controlling building structures, centralized strategies are often adopted. However, in a
centralized mode, once the controller fails, the vibration displacement of the building structure may
be large and cause cracking and even the collapse of the building. Decentralized strategies may
be used to enhance the reliability of the control system and to mitigate the consequences of system
failure [20–22]. A remarkable feature of a decentralized architecture is that there is no subordinate
relationship among decentralized controllers in the system. Since each controller has its own target,
the strategy for coordinating them effectively is an interesting and important issue.
Knowing the advantages of the FOPD and the centralized control strategy, and for ensuring
that all controllers work in a coordinated way, a new strategy is proposed to control unwanted large
displacements of building structures under earthquake excitation. In a ﬁrst phase, an improved FOPD
(IFOPD) is proposed, where the order of the fractional derivative can vary according to the system
dynamics. In a second phase, an improved decentralized scheme is designed by adding a regulator
and fault self-regulation to the traditional decentralized controller. In a third phase, we combine the
IFOPD and the decentralized scheme for implementing a new decentralized and regulated IFOPD
(DRIFOPD) controller. The new control strategy ensures that each subsystem controller not only
runs independently, but also is compatible with the others. Numerical simulations of a 9-ﬂoor steel
structure building model exemplify the proposed concepts. In addition, comparisons between the PD,
FOPD, and IFOPD under centralized and decentralized control illustrate the corresponding dynamical
behavior. It is shown that, in centralized control, the IFOPD performs better than the other controllers,
and, in a decentralized mode, the DRIFOPD is superior and more reliable.
The rest of this paper is organized as follows. Section 2 introduces the basic concepts of fractional
calculus and the vibration model of a building structure. Section 3 develops the new DRIFOPD for
controlling unwanted displacements of building structures. Section 4 presents simulation results that
illustrate the effectiveness of the DRIFOPD. Finally, Section 5 outlines the main conclusions.
2. Preliminaries and Model Description
In this section, the basic concepts of fractional calculus and the vibration model of a building
structure are introduced.
2.1. Fractional Calculus Theory
The Grünwald–Letnikov (G–L) fractional differential of a causal function, f (t), is given by [23]:
aD
µ
t f (t) = lim
h→0
1
Γ(µ)hµ
[(t−µ)/h]
∑
j=0
Γ(µ+j)
Γ(j+1)
f (t − jh)
= lim
h→0
1
hµ
[(t−µ)/h]
∑
j=0
(−1)j ×
µ
j
f (t − jh),
(1)
where µ denotes the order of fractional differential, Γ(·) is the gamma function and
µ
j
=
µ(µ − 1)(µ − 2) · · · (µ − j + 1)
j!
=
µ!
j!(µ − j)!
.
By approximating formula (1), the discrete form of the FOPD [24,25] is obtained as:
U(k) = Kp f (k) +
M1−1
∑
j=0
Kdh−µ
qj f (k − j) +
k−1
∑
j=M1
Kdh−µ
qM1
f (k − j), k > M1, (2)
where q0 = 1 and qj = (1 − (1 + µ)/j)qj−1. The parameters Kp and Kd represent the proportional
and differential gains, respectively, µ is fractional order, and f (k) stands for the input signal to the
controller at the kth time instant. Hereafter, we adopt M1 = 500.

From Equation (1), one can see that when the order µ = 1, the first derivative of the function
f (k) is recovered. Therefore, the calculation of integer derivatives in the G–L definition is done by
backward differens f (k) = f (k)−f (k−1)
h .
2.2. Vibration Control System of Building Structures
The vibration control system of a building structure is represented schematically in Figure 1, and
its dynamic model is given by [4,9]:
M ¨X(t) + C ˙X(t) + KX(t) = BU(t) + EV(t), (3)
where EV(t) = −MLä(t), L is a unit column vector, and ä(t) denotes the seismic wave acceleration.
The symbol B consists of the actuator location matrix, U denotes the actuator control force vector, E
stands for the external excitation location matrix, and V is the external excitation vector. The symbols
¨X, ˙X, and X represent the acceleration, velocity, and displacement vectors of the building, respectively.
The parameters M, C, and K represent the structural mass, damping, and stiffness matrix, respectively.
The Rayleigh damping matrix is given by C = αM + βK, where α, β ∈ R are constants with units of
s−1 and s, respectively [26].
Transforming model (3) into the state space form yields:
˙Y(t) = AY(t) + DU(t) + HV(t),
X(t) = FY(t),
(4)
where
Y(t) =
X(t)
˙X(t)
, A =
0 I
−M−1K −M−1C
D =
0
M−1B
, H =
0
M−1E
, F = I 0 .
The state space model (4) can be rewritten in the standard form,
˙X(t) = A1X(t) + B1Ud(t),
Y(t) = C1X(t),
(5)
where Ud(t) = −Lä(t) − M−1 p fd, fd is the force output generated by the actuator, p is a column vector
representing the position where the force acts, and
X(t) =
Y(t)
˙Y(t)
, A1 =
0 I
−M−1K −M−1C
, B1 =
0
I
, C1 = I 0 .
This article analyzes the horizontal vibration of the building structure and adopts active control
strategies to place actuators on each floor of the building structure. The Matlab/Simulink software is
used to simulate and analyze the behavior of the building. The input signal in the simulation is the
acceleration of the seismic wave ä(t), and the controller controls the output force fd of the actuator
according to the collected displacement signal. In structural vibration control, the actuator usually
adopts magnetic rheological (MR) dampers. Additionally, the particle swarm optimization (PSO)
algorithm is used to optimize the control parameters of the controller [27,28].

Actuator Tall building
Control system
Force
Input Signal
Structural
Responses
X(t)
Control Signal
a(t)
df
Figure 1. Schematic diagram of a vibration control system of a building structure.
3. Improved Control Strategy
This section presents the improved FOPD (IFOPD) and the decentralized control mode.
3.1. An Improved FOPD Controller
For improving the FOPD performance, the differential order of the controller is no longer ﬁxed
and, consequently, it is allowed to vary with time. The discrete IFOPD is given by:
U(k) = Kp f (k) +
M1−1
∑
j=0
Kdh−µ(k)
qj f (k − j) +
k−1
∑
j=M1
Kdh−µ(k)
qM1
f (k − j), k > M1, (6)
where µ(k) represents the differential variable order and f (k) is the displacement feedback signal that
usually corresponds to the displacement of the ﬂoor with the largest vibration.
Figure 2 illustrates the IFOPD control system under centralized control mode. For simplicity, a P
controller under centralized control mode will be referred to as CP in the follow-up.
Differential Value Optimizer
PK
( )
( )
n
d n
d
K
dt
Input
signal
Control
signal
Control system
Figure 2. Control system using the IFOPD under a centralized control mode.
3.2. An Improved Decentralized Control Strategy
The control system consists of a controller (electronic unit) and an actuator. The controller needs
to detect continuously whether an earthquake has occurred or not, but the long idle state may cause
the controller to fail in the presence of some sudden event. From Figure 2, we verify that, when
the controller fails, the system enters in an uncontrolled state and, therefore, potential safety issues
emerge. Decentralized control is usually employed to increase the reliability of the control system.

A decentralized architecture divides the control system into different subsystems. Each subsystem
consists of a controller (that outputs multiple control signals) and several actuators. When some
controllers fail, the remaining systems are able to guarantee that the control system still has some
relevant output action.
Herein, to improve the performance of the traditional decentralized strategy, a regulator
is introduced into the control architecture. The regulator is a controllable scale factor that can
adjust the amplitude of the feedback signal. Figure 3 depicts the structure of the proposed
decentralized controller.
Control System 1
Control system
Control System 2
Control System N
Input
signal
Control
signal
Regulator 1
Regulator 2
Regulator N
Signal 1
Signal 2
Signal N
1U
2U
NU
Figure 3. Decentralized control system with a regulator.
The control action of each controller in the control system can be calculated by the
following equation:
Ui(k) = KpiFi(k) +
M1−1
∑
j=0
Kdih−µi(k)
qjFi(k − j) +
k−1
∑
j=M1
Kdih−µi(k)
qM1
Fi(k − j), k > M1, i ∈ [1, N], (7)
where Fi(k) = Ri f (k) and Ri is a constant representing the scaling factor of the regulator. As before,
the symbol f (k) represents the displacement feedback signal, consisting of the displacement of the
ﬂoor with the largest vibration in the subsystem.
For decentralized systems with regulators, the controller output is the result of the synergies
between the various subsystems. However, when one particular subsystem fails, the parameters of the
other control subsystems may not be optimal. To solve this problem, a fault self-regulation strategy is
proposed, where all faults are analyzed beforehand, and the optimal parameters (for all faults) are
calculated and stored. When a fault is detected, the parameters of the control system are updated
according to the current detected fault.
Under an earthquake excitation, the implementation process of the fault self-regulation strategy
is as follows:
• Step 1: Check if there is a fault in the control subsystem. If “yes”, jump to step 2; otherwise, if
“no”, jump to step 3;
• Step 2: Update the control system parameters according to the fault situation;
• Step 3: Run the control system and output the corresponding control actions.
In the follow-up, a P-type controller, either under decentralized control with regulators and
self-regulation, or under traditional decentralized control, is referred as DRP or DP, respectively.
4. Simulation Analysis
We consider a 9-ﬂoor building structure with the parameters shown in Table A1. The excitation
signal is the El-centro seismic wave. Figure 4 depicts a diagram of the control system. When the

centralized strategy is adopted, the input signal of the control system is the displacement of the 9th
floor. Alternatively, when the decentralized control strategy is used, the input signals of the control
subsystems 1, 2, and 3 are the displacements of the 3rd, 6th, and 9th floors, respectively.
Control System
Control System 1
Control System 2
Control System 3
(a) Centralized control (b) Decentralized control
Figure 4. Schematic diagram of the control system of a 9-floor building structure.
When the control system is fault-free, the maximum displacement of the building structure is at
the top floor, and the fitness function g1 given in the formula (8) is selected to optimize the parameters
by means of a PSO algorithm:
g1 = ¯g19, g19(j) =
X19(j), X19(j) ≤ Xmax
ω1X19(j), X19(j) > Xmax
, j = 1, 2 · · · jmax. (8)
When the control system fails, the floor with the largest displacement may be, or may be not, the
top floor. In this case, the fitness function g2 is selected, given by:
g2 = ¯g23 + ¯g26 + ¯g29, g2i(j) =
X2i(j), X2i(j) ≤ Xmax
ω1X2i(j), X2i(j) > Xmax
, i = 3, 6, 9, j = 1, 2, · · ·, jmax, (9)
where the symbols ¯g1i and ¯g2i represent the average displacement of the floor i, g1i(j) and g2i(j) stand
for the displacement of floor i at the j-th time, Xmax is the maximum selected displacement, and
ω1 = 100.
In the following experiments, the El-centro seismic wave is used as the excitation signal. An
offline PSO optimization algorithm is used to optimize the control parameters Kp, Kd, µ and R. The
fitness function g1 is adopted in the optimization of the control parameters in the absence of any
failure. On the other hand, the fitness function g2 is used for the case when the controller fails. The
configuration of the PSO used in this paper is shown in Table A2, and the values of the parameters
obtained through optimization are listed in Table A3.
4.1. Analysis of the Centralized Control
The displacement of the 9-floor building under centralized control is shown in Figures 5 and 6. We
verify that the CFOPD and CIFOPD yield acceptable performance, while the CPD leads to poor results.
The maximum displacement obtained with the CFOPD is 0.0373 m and the average displacement
given by (6) is g1 = 0.0096 m. For the CIFOPD, the maximum displacement is 0.0274 m and the
average displacement is g1 = 0.0057 m. Therefore, the maximum displacement for the CIFOPD, when
compared to the CFOPD, is reduced by 36.1% and the average displacement g1 is reduced by 68.4%.
The control signals of the CPD, CFOPID, and CIFOPID are shown in Figure 7. It follows that the
CIFOPD is superior to the CFOPD.

0 1 2 3 4 5 6 7 8
−0.15
−0.1
−0.05
0
0.05
0.1
0.15
0.2
Time(s)
Topdisplacement(m)
Uncontrol
CPD
CFOPD
CIFOPD
Figure 5. Top displacement curve under centralized control.
0 0.05 0.1 0.15 0.2 0.25
0
1
2
3
4
5
6
7
8
9
Peak floor displacement(m)
Floor
Uncontrol
CPD
CFOPD
CIFOPD
Figure 6. Maximum displacement of each ﬂoor under centralized control.
0 1 2 3 4 5 6 7 8
−3
−2
−1
0
1
2
3
4
x 10
5
Times(s)
Forcefromactuator(N)
CPD
CFOPD
CIFOPD
Figure 7. Forces output generated by the actuators under the action of the CPD, CFOPID, and CIFOPID
controllers.
4.2. Analysis of the Decentralized Control
Figures 8 and 9 depict the displacement of the 9-ﬂoor building structure under decentralized
control. The maximum displacement obtained with the DRFOPD is 0.0279 m and the average
displacement is g1 = 0.0068 m. The maximum displacement for the DRIFOPD is 0.024 m and
the average displacement is g1 = 0.0054 m. Comparing both controllers, we verify that the maximum
displacement obtained with the DRIFOPD is reduced by 16.3%, and the average displacement g1 is
reduced by 20.6%. The control signals of the DRPD, DRFOPD, and DRIFOPD are shown in Figure 10.
We observe that the DRIFOPD is better than the DRFOPD.
In conclusion, we verify that the maximum and average displacements obtained with the
improved decentralized control are smaller than those yielded by the centralized control strategy.
At the same time, the comparative analysis shows that the DRIFOPD has the best performance.

0 1 2 3 4 5 6 7 8
−0.15
−0.1
−0.05
0
0.05
0.1
0.15
0.2
Time(s)
Topdisplacement(m)
Uncontrol
DRPD
DRFOPD
DRIFOPD
Figure 8. Top displacement curve under decentralized control.
0 0.05 0.1 0.15 0.2 0.25
0
1
2
3
4
5
6
7
8
9
Floor
Uncontrol
DRPD
DRFOPD
DRIFOPD
Figure 9. Maximum displacement of each ﬂoor under decentralized control.
0 1 2 3 4 5 6 7 8
−6
−4
−2
0
2
4
6
8
x 10
5
Times(s)
Forcefromactuator(N)
DRPD
DRFOPD
DRIFOPD
Figure 10. Forces output generated by the actuators under the action of the DRPD, DRFOPD, and
DRIFOPD controllers.
4.3. Fault Analysis
In this subsection, we analyze the control performance and reliability of the DRIFOPD in the
presence of one fault. Three scenarios are tested: fault case 1—the control subsystem 2 fails; fault case
2—both the control subsystems 1 and 3 fail; fault case 3—both the control subsystems 2 and 3 fail.
4.3.1. Fault Case 1
For the fault case 1, both the DIFOPD and DRIFOPD controllers have acceptable performance
after the fault. Figures 11 and 12 depict the displacements of the structure. We verify that the maximum
and average displacements g2 obtained with the DRIFOPD are much smaller than those obtained
with the DIFOPD. This result indicates that the performance of the DRIFOPD is better than the one
exhibited by the DIFOPD.

0 1 2 3 4 5 6 7 8
−0.1
−0.05
0
0.05
0.1
0.15
0.2
0.25
Time(s)
DisplacementofMaximumVibrationFloor(m)
Uncontrol
DIFOPD
DRIFOPD
Figure 11. The displacement of the maximum vibration floor under fault case 1.
0 0.05 0.1 0.15 0.2 0.25
0
1
2
3
4
5
6
7
8
9
Floor
Uncontrol
DIFOPD
DRIFOPD
Figure 12. Maximum displacement of each floor under fault case 1.
4.3.2. Fault Case 2
For the fault case 2, the displacement of the 9-floor building structure is shown in
Figures 13 and 14. The maximum displacement for the DIFOPD is 0.0691 m and the average
displacement is g2 = 0.0438 m. The maximum displacement for the DRIFOPD is 0.0579 m and
the average displacement is g2 = 0.009 m. Compared with the DIFOPD, the maximum displacement
for the DRIFOPD is reduced by 16.2%, and the average displacement g2 is reduced by 79.4%. The
results show that (i) both the DIFOPD and the DRIFOPD have some control action under fault case 2,
and (ii) the DRIFOPD is better than the DIFOPD.
0 1 2 3 4 5 6 7 8
−0.1
−0.05
0
0.05
0.1
0.15
0.2
0.25
Time(s)
Uncontrol
DIFOPD
DRIFOPD

Mathematics 2020, 8, 326 10 of 13
0 0.05 0.1 0.15 0.2 0.25
0
1
2
3
4
5
6
7
8
9
Floor Peak floor displacement(m)
Uncontrol
DIFOPD
DRIFOPD
4.3.3. Fault Case 3
For the fault case 3, the displacement of the 9-ﬂoor building structure is shown in
Figures 15 and 16. Comparing the displacement obtained with the DIFOPD with the one for the
uncontrolled mode, we conclude that the DIFOPD has almost a complete failure. This result occurs
because, in the decentralized strategy, the control actions of subsystems are different. The control
subsystem 1 of the DIFOPD produces outputs with very small amplitudes that mainly play a regulating
role. Therefore, when the control subsystems 2 and 3 fail, the vibration of the building structure can
not be suppressed merely by the control subsystem 1. However, when the DIFOPD fails, the DRIFOPD
still has a good control effect. In fact, when the self-tuning strategy in the DRIFOPD detects the fault, it
makes the output of the control subsystem 1 more signiﬁcant by adjusting the parameters, so as to
maintain the control performance of the global system. The results show that the performance of the
DRIFOPD is better than that of the DIFOPD in fault case 3. Moreover, the results also prove that the
reliability of the DRIFOPD is better than that of the DIFOPD.
0 1 2 3 4 5 6 7 8
−0.1
−0.05
0
0.05
0.1
0.15
0.2
0.25
Time(s)
Uncontrol
DIFOPD
DRIFOPD
0 0.05 0.1 0.15 0.2 0.25
0
1
2
3
4
5
6
7
8
9
Floor
Uncontrol
DIFOPD
DRIFOPD

Mathematics 2020, 8, 326 11 of 13
5. Conclusions
The paper addressed the design of a composite control system to improve the performance
and reliability of building structure vibration control. An improved FOPD controller was proposed.
Simulations under centralized control revealed that the performance of the CIFOPD is much better than
the one exhibited by the CFOPD. A decentralized strategy was then designed by adding a regulator
and fault self-regulation to a traditional decentralized controller. The combination of the IFOPD with
the improved decentralized strategy led to the DRIFOPD controller. Simulation results verified that
the DRIFOPD has a superior reliability and excellent control performance.
Author Contributions: K.X., simulation, writing and editing the manuscript; L.C., validation, supervision, and
project administration; M.W., methodology formal analysis; A.M.L., writing—review and editing; J.A.T.M.,
writing—review and editing. H.Z., simulation. All authors have read and agreed to the published version of the
manuscript.
Funding: The work was supported by the National Natural Science Foundation of China (No. 11971032).
Acknowledgments: The authors are grateful to the four anonymous reviewers for their valuable comments and
suggestions which have led to significant improvement of this paper.
Conflicts of Interest: The authors declare no conflict of interest.
Appendix A
Table A1. Structural parameters of 9-floor buildings.
Floor 1 2 3 4 5 6 7 8 9
Height (m) 4 3.3 3.3 3.3 3.3 3.3 3.3 3.3 3.3
Quality (kg) 29890 21700 21700 21700 21700 21700 21700 21700 21700
Rigidity (1 × 107 N/m) 1.764 2.08 2.08 2.08 2.08 2.08 2.08 2.08 2.08
The matrices M, C, and K in the model (3) are calculated from the parameters in Table A1.
M = diag{29890, 21700, 21700, 21700, 21700, 21700, 21700, 21700, 21700},
K = 1 × 107
















3.844 −2.08 0 0 0 0 0 0 0
−2.08 4.16 −2.08 0 0 0 0 0 0
0 −2.08 4.16 −2.08 0 0 0 0 0
0 0 −2.08 4.16 −2.08 0 0 0 0
0 0 0 −2.08 4.16 −2.08 0 0 0
0 0 0 0 −2.08 4.16 −2.08 0 0
0 0 0 0 0 −2.08 4.16 −2.08 0
0 0 0 0 0 0 −2.08 4.16 −2.08
0 0 0 0 0 0 0 −2.08 2.08
















,
C = 1 × 105
















3.21 −1.69 0 0 0 0 0 0 0
−1.69 3.44 −1.69 0 0 0 0 0 0
0 −1.69 3.44 −1.69 0 0 0 0 0
0 0 −1.69 3.44 −1.69 0 0 0 0
0 0 0 −1.69 3.44 −1.69 0 0 0
0 0 0 0 −1.69 3.44 −1.69 0 0
0 0 0 0 0 −1.69 3.44 −1.69 0
0 0 0 0 0 0 −1.69 3.44 −1.69
0 0 0 0 0 0 0 −1.69 1.75
















.

Mathematics 2020, 8, 326 12 of 13
Table A2. PSO optimization algorithm conﬁguration.
Name of Parameters Value of Parameters
Particle number 50
Number of Iterations/Number of repeated experiments 300/50
Scaling factors C1 = C2 = 2, w = 0.6, Vmax = 1, Vmin = −1
Parameter optimization range kp ∈ [0, 20], ki ∈ [0, 5], u, R ∈ [0, 2]
Table A3. Control system parameters.
Name of Control System Control System Parameter Value
CPD [Kp, Kd] = [0.01, 3.997]
CFOPD [Kp, Kd, µ] = [20, 5, 1.63]
CIFOPD [Kp, Kd, µ] = [15.614, 2.996, ∼]
DRPD [Kp1, Kd1, Kp2, Kd2, Kp3, Kd3, R1, R2, R3]
= [20, 4.91, 19.88, 5, 20, 5, 1.985, 2, 2]
DRFOPD [Kp1, Kd1, µ1, Kp2, Kd2, µ2, Kp3, Kd3, µ3, R1, R2, R3]
= [10.19, 5, 1.198, 0.0976, 5, 1.627, 20, 5, 1.228, 0.7781, 1.1273, 2]
DRIFOPD [Kp1, Kd1, µ1, Kp2, Kd2, µ2, Kp3, Kd3, µ3, R1, R2, R3]
= [19.65, 3.881, ∼, 2.821, 4.046, ∼, 10.06, 2.501, ∼, 0.8483, 1.719, 1.842]
Fault case 1 : DIFOPD [Kp1, Kd1, µ1, Kp2, Kd2, µ2, Kp3, Kd3, µ3]
= [20, 4.8086, ∼, ×, ×, ×, 15.2195, 3.3904, ∼]
Fault case 1 : DRIFOPD [Kp1, Kd1, µ1, Kp2, Kd2, µ2, Kp3, Kd3, µ3, R1, R2, R3]
= [20, 2.9654, ∼, ×, ×, ×, 19.587, 3.0118, ∼, 0.5672, ×, 1.5528]
= [×, ×, ×, 16.7368, 5, ∼, ×, ×, ×]
= [×, ×, ×, 6.8145, 3.56, ∼, ×, ×, ×, ×, 1.7177, ×]
= [20, 4.8086, ∼, ×, ×, ×, ×, ×, ×]
= [0.01, 4.9266, ∼, ×, ×, ×, ×, ×, ×, 1.0702, ×, ×]
Note: the symbol ∼ means that the value changes with time, and the symbol × refers to the fault of the controller.
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c 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access
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