This paper proposes a new method for tuning a linear quadratic  proportional integral derivative controller for second order systems to simultaneously meet the time and frequency domain design specifications. The suitable loopshape of the controlled system and the desired step response are considered as specifications in the time and frequency domains, respectively. The weighting factors, Q and R of the LQ controller are determined by the algebraic Riccati equation with respect to the limiting behavior and target function matching. Numerical examples show the effectiveness of the proposed LQPID tuning method
1. Introduction
Linear optimal control theory has been extensively studied since Bellman’s dynamic programming
[1]
and Pontryagin et al.’s minimum principle
[2]
were published early in the 1960s. In linear optimal control, the linear quadratic regulator (LQR) problem is to find the optimal feedback control law to minimize the quadratic performance criterion with respect to the states and inputs via weighting matrices Q and R
[3

5]
. Thus the central issue remains of how to relate the weighting matrices in the quadratic performance criterion to classical specifications in the time and frequency domains for the control system design
[6

9]
.
Various analytic methods have been developed for frequency domain specifications such as disturbance attenuation, noise rejection, and good command following for the LQR
[10
,
11]
and linear quadratic Gaussian / linear transfer recovery (LQG/LTR)
[12
,
13]
, based on the loopshaping method. These existing methods have difficulty directly matching the quadratic performance indices with the filter dynamics to the frequency loop shape
[3
,
4]
.
On the other hand, eigenvalue positioning methods were proposed for the time domain approach to select the appropriate weighting matrices, Q and R, to locate the closed loop poles inside the desired region or match them with the specific eigenvalues through the ARE
[4]
. Existing pole positioning approaches are limited in their analysis of time domain performances such as overshoot, rising and settling time
[4]
. LQproportional integral (PID) control methods have been also developed separately in time and frequency domains
[14
,
15]
.
We propose a new optimal control method for LQPID design to simultaneously satisfy the design specifications in the time and frequencydomain without any filter or polezero cancelation
[16]
. For frequencydomain performances, we improved the existing limiting behaviour method of the loop transfer function to more exactly interpret the loop shape with respect to all design factors, based on Kalman’s equality
[17
,
18]
. This loop shaping makes it possible to consider the magnitudes of the LQ loop system independently in low and high frequency ranges for good command following, disturbance rejection, and attenuation of noise effect
[19]
. Time domain specifications such as percent overshoot, rise time, and settling time are related to the design factor by target function matching regardless of the polezero cancellation
[16]
. Overshoot and the Routh Hurwitz stability criterion are formulated by the coefficient analysis with respect to the normalized thirdorder closedloop system
[20]
. The other timerelated specifications are addressed by the timeregulation property of the target function
[21]
. In the proposed method, the LQPID control parameters are determined by optimizing to minimize the cost function, which is subject to constraints to meet the combined time  and frequencydomain specifications for classical performances. We choose a combination of integral absolute error (IAE) and 2norm of the control gains for the cost function of the optimal problem
[22]
.
The primary contributions of the proposed method include as follows:

(i) The proposed method provides the direct matching manner to relate the weighting factor, Q, to the loop shape of the loop transfer function in frequency domain.

(ii) The proposed target function matching method improves the existing time domain approach of LQPID control by considering the specific time domain performances of transient response such as overshoot, rising time, settling time.

(iii) Both of time and frequency domain specifications are combined by the proposed optimization problem in order to be simultaneously satisfied.
The paper is organized as follows. In Section 2, we introduce the LQPID formulation to design the optimal PID controller by transforming the PID control into the LQ approach. The design methodologies for the proposed controller are discussed as the optimization problem with constraints subject to the time and frequencydomain specifications in Section 3. Numerical examples are given in Section 4 to show the effectiveness of the proposed design method, compared to other design methods. Finally, Section 6 contains some concluding remarks and remaining challenges.
2. LQPID Control
Briefly, we introduce the LQPID control method proposed by Suh and Yang
[14]
, in which the optimal linear feedback control law is matched to the conventional PID control for the second order system by augmenting the integral of the output variable as a new state.
Consider the following second order model:
where
y
(
t
) ,
u
(
t
) ,
ζ
and
ω_{n}
are the output variable, the control variable, the damping ratio and the natural frequency. The initial conditions of
y
(
t
) and
dy
(
t
) /
dt
are specified.
The new augmented state variables are:
The statespace representation of the augmented system becomes:
where
If we apply the different input gain according to and as
where
A_{c}
is the closed loop system matrix.
The time delay system can be represented as the system without the time delay as where
[23]
.
The quadratic performance criterion is considered for the LQR formulation:
with the assumption that the weighting factor,
Q
, is positive semidefinite and symmetric
Q
=
Q^{T}
≥ 0 , and
ρ
is a positive value.
The linear feedback control law to regulate the state,
x
(
t
) , is obtained as follows:
where the optimal gain matrix,
K
=
K^{T}
, is obtained by solving the ARE:
Let the components of
K
and
G
be:
which we will hereafter refer to as the optimal Kalman gains of the LQPID.
The optimal control law of Eq. (9) is represented as the following PID control formula:
The state feedback system with the control input,
u
(
t
) , of Eq. (8) is obtained by substituting Eq. (12) into Eq. (3):
So, the closed loop system matrix,
A_{c}
, is:
and its characteristic equation becomes:
The closed loop transfer function of the second order system with the conventional PID controller can be expressed as follows:
where
and the transfer function of Eq. (1) is
The characteristic equation of Eq. (19) is
Fig. 1
shows the block diagram of LQR of the state feedback system with the augmented state variable, forming PID structure where the optimal tuning of the conventional PID controller is achieved by LQR.
Structure of LQPID control
By corresponding Eq. (18) to Eq. (16), the parameters of the PID controller are matched with the optimal Kalman gains of the LQPID structure from Eq. (13).
It has the following relationship
such that the PID controller is tuned by selecting the weighting matrices,
Q
and
ρ
, because the parameters of the PID controller are related to the solution,
K
, of the ARE through Eqs. (19) and (12), and
ω_{n}
is given by the system plant.
Let the weighting matrix,
Q
, be as Eq. (20):
where
N
= [
n
_{0}
n
_{1}
n
_{2}
] is assumed as a partitioned matrix of the weighting matrix,
Q
.
3. Design Methodology
In this section we describe the proposed design procedure of the LQPID controller to meet both the frequency and time domain specifications.
Given a specific set of design requirements in the time domain such as overshoot, and rise and settling times, the feasible area of design parameters will be determined by pole matching to the normalized simple third order system
[15
,
18]
. The parameters are then connected to weighting factors,
Q
and
ρ
, by the ARE. For the frequency specifications, we consider the limiting behaviour for the loop shaping method in which Kalman’s inequality is applied
[16
,
21]
. The time and frequencydomain approaches are combined by formulating the optimal problem with constraints in terms of
Q
and
ρ
to satisfy both time and frequencydomain specifications. We consider the cost function of the proposed optimal problem as the weighted summation with the 2norm of control gains and the integral of the absolute error in order to avoid the saturation of input energy and reduce the overall effect of the error.
 3.1 Combined constrains for time and frequency domain
 3.1.1 Frequency domain performances
In the frequency domain, there are many performance indices such as gain margin, phase margin, infinite norm of the sensitivity function, and the complementary sensitivity function. The LQPID control guarantees the robust stability of the optimal system, which is verified by Kalman’s inequality
[15]
. The inequality verifies that the sensitivity performance is improved at all frequencies,
S( jω) ≤ 1 , for all ω [3, 4, 17, 19].
The sensitivity function is defined in the usual way,
where the loop transfer function
g_{LQ}
(
s
) of LQR is obtained as follows:
In order to associate the frequency loop shaping procedure with the weighting factors,
Q
and
ρ
, we use Kalman’s identity for the single input single output case:
where
g_{OL}
(
jω
) =
N
(
jωI
−
A
)
^{−1}
B
with a partitioned matrix,
N
, of the weighting matrix,
Q
,. Eq. (23) can be represented as:
Deriving the magnitude of the complex function in Eq. (24), we have:
Eq. (25) can be approximated as Eq. (26) only for low and high frequencies since 
g_{LQ}
(
jω
) >> 1 and 
g_{LQ}
(
jω
) << 1 , respectively
[15]
.
The magnitudes of the loop transfer function,
g_{LQ}
(
s
) , are approximated via the limiting behaviours of
g_{LQ}
(
jω
) at low and high frequencies
[19]
, respectively, as:
and
Let each asymptotic line of 
g_{LQ}
(
jω
) at the low and high frequency be
l_{L}
(
ω
) and
l_{H}
(
ω
) , respectively.
We consider a design procedure that involves explicitly shaping the magnitude of the loop transfer function,
g_{LQ}
(
s
) , for frequency domain performances.
In classical loop shaping, better performances such as good command following and disturbance rejection are obtained when the magnitude of the loop transfer function,
g_{LQ}
(
s
) , is larger for low frequencies at 
g_{LQ}
(
jω
) >> 1
[19]
. Fortunately, robust stability, noise attenuation, and control energy reduction are valid primarily in high frequencies at 
g_{LQ}
(
jω
) << 1 . The shape of 
g_{LQ}
(
jω
) then should not invade either the low and high frequency barriers,
Q
and
β
(
ω
) , respectively, as described in
Fig. 2
.
Typical shape of g_{LQ}( jω) with barriers for the requirements
For this adequate loop shaping, we can develop a loop shaping technique in which the design parameter,
N
, plays a key role in maintaining 
g_{LQ}
(
jω
) beyond the required boundaries. Specifically, this means that we match the weighting matrix,
Q
, of the LQ problem directly with the performances of the frequency domain to satisfy the boundary conditions via the approximation of limiting behavior.
Let Ω
_{r}
be a boundary frequency of the command following barrier,
α
(
ω
) , and scalar r m be a magnitude of
α
(
ω
) , as shown in
Fig. 2
. Set the barrier,
α
(
ω
) , as:
Since
l_{L}
(
ω
) should exist on the upper side of the barrier,
α
(
ω
) , the asymptotic line of 
g_{LQ}
(
jω
) at the low frequency must satisfy:
Solving Eq. (30) for the range of the parameters,
n
_{0}
and
n
_{1}
, gives:
Let Ω
_{r}
be a boundary frequency of the senor noise, and
be the inverse of the maximum value of the modeling error in
Fig. 2
.
The barrier,
β
(
ω
) , can then be expressed as:
The inequality constraint of
l_{H}
(
ω
) for high frequencies is derived from Eq. (28) in the same manner:
Hence this constraint can be rearranged with the respect to the element
n
_{2}
of the partial matrix,
N
of
Q
as
Finally, we can yield the constraints of Eqs. (31) and (34) with respect to the weighting factors,
Q
and
ρ
, of the LQ problem to satisfy frequency domain performances, adjusting the loop shaping of 
g_{LQ}
(
jω
) .
 3.1.2 Time domain performances
In the time domain, the desired transient performances are typically characterized by overshoot, and rise, and settling times. Designs for controllers that improve these transient performances of a second order closed loop system in the time domain are well explained in current literatures; however higher order systems are generally analyzed via the dominant pole concept as the reduced second order system is hard to formulate
[24]
.
In this paper, we consider the special formulation of the third order closed loop system as the target function which is referred to
[14]
as:
The shape of the unit step response of Eq. (35) is determined by parameters
p
,
r
, and
ω_{t}
. Furthermore, it is found that the overshoot is maintained constantly, regardless of the variations in natural frequency,
ω_{t}
, when parameters
p
and
r
are fixed. This is verified from the formulation of the output,
y
(
t
)
[25]
.
where
Since the time,
t
, and the natural frequency,
ω_{t}
, are united, we can modify the time,
t
, to a new time,
τ
, using
ω_{t}
as the time ratio, as follows:
where
τ
= *
t
ω_{t}
The output
y
(
τ
) is represented as the response of the normalized thirdorder closedloop system (NTCS), described as Eq. (35) with
ω_{t}
= 1 . Hence the time ratio of the same step response
y
(
τ
) can be regulated independently on variations of the natural frequency,
ω_{t}
as shown in
Fig. 3
.
Step response of the third order system according to variations in ω_{t}
We consider two steps to formulate the constraints of time domain specifications. First, we determine the constraints with respect to
p
,
r
, and
ω_{t}
to satisfy the performance requirement in the time domain. For the overshoot requirement, the constraint function is illustrated as follows:
where
f_{OS}
(
r, p
) represents the maximum step response function consistent with the variables,
p
and
r
.
Fig. 4
shows that the contours of maximum percentage overshoot with respect to the step reference can be plotted in the coefficient plane of
p
and
r
. If the RouthHurwitz stability criterion is applied to the normalized system transfer function of Eq. (35), Eq. (40) is required for simple absolute stability with
h
= 1 :
Coefficient plane for overshoot in NTCS
A gain margin of 0.33, which is allowable in practice, corresponds to the line of Eq. (40) for
h
= 1.5
[18]
. The region between the two contours at
h
= 1 and
h
= 1.5 is the unallowable design area, which is distinct by oblique in the coefficient plane of
Fig. 4
.
Hence, except in terms of area, the overshoot coefficient is settled according to the boundary of the required overshoot percentage. The natural frequency,
ω_{t}
, can be addressed as the design parameter, which plays a role in regulating the rise and settling time under the overshoot determined by
p
and
r
.
where
ω_{ST}
and
ω_{RT}
depend on the values of the natural frequency,
ω_{t}
, to satisfy the required settling and rising time, respectively. In other words, the system parameters
p
and
r
are determined to meet the given overshoot requirement, and
ω_{t}
is considered as a factor used to separately satisfy the given settling time and rising time requirements.
Secondly, the system parameters
p
,
r
, and
ω_{t}
should be formulated as a function with respect to the optimal Kalman gains of the LQPID feedback system via pole matching to equalize the characteristic equation of the target function Eq. (16) of NTCS.
Given the above Eq. (42), the following three equations are derived.
Using Eq. (43), weighting matrices,
Q
, and
ρ
, can be expressed in terms of the variables of the target transfer function via the ARE to satisfy the time domain performances, as follows:
By substituting Eq. (43) to Eq. (44), the components [
n
_{0}
n
_{1}
n
_{2}
] of weighting matrix,
Q
are expressed as Eq. (45) in terms with the variables,
p
,
r
, and
ω_{t}
for time domain performances, given input weighting,
ρ
, and system parameters,
ω_{n}
,
ζ
and
c
of the plant
where
f
_{1}
and
f
_{2}
are the functions to meet time domain specifications in terms of the system variables. Eq. (39) and (41) then can be represented by the function of the optimal Kalman gains through Eqs. (42), (43), and (44) as follows:
Therefore we can determine the feasible constraints Eqs. (46) and (47) in terms of positive weighting matrices,
Q
and
ρ
, of the LQ problem to meet transient step response requirements such as overshoot, rise, and settling time in time domain.
 3.2 Formulation of an optimization problem
In the above sections, we derived that the constraint conditions Eqs. (31), (34), (46), and (47) to satisfy the combined time and frequencydomain requirements. The conditions are represented as inequality functions with respect to weighting matrices,
Q
and
ρ
, of LQ problem, obtained via the loop shaping method and the pole matching method by the ARE. In this subsection, we formulate the optimal problem to determine optimal values of the design parameter
N
to minimize the object function subject to the constraints combined by time and frequencydomain performances. We choose the weighted combination of two objectives, the 2norm of PID gain and IAE. This objective of the optimal problem can vary depending on the control purpose of the designer in practice.
The first objective term is represented as Eq. (48) in order to avoid a high gain problem when we take
ρ
<< 1for the cheap control as shown by Eq.
(27)
[26]
.
The second objective therm is the IAE index as Eq. (50) for the time response to consider the error arising from the difference between a nominal plant and a closed loop target function Eq. (35):
These two objectives are combined by weighting vector,
γ
, as follows:
where the weighting vector can be chosen according to the importance of each objective.
In order to combine the time and frequencydomain performances, the optimization problem is formulated with respect to the object functions in Eq. (50) and the constraints in Eqs. (31), (34), (46), and (47), as follows:
minimize (n_{0}, n_{1}, n_{2})
subject to
f_{os}(ρ,n_{0},n_{1},n_{2})≤Γ, ∀τ
f_{WT}(ρ,n_{0},n_{1},n_{2})≥max(ω_{ST},ω_{RT}), fixed p, r
n_{0},n_{1},n_{2}>0 for positive definite
where
ω_{n}
andζ are given by the process, and Ω
_{r}
,
m_{r}
, Ω
_{n}
,
, Γ ,
ω_{ST}
,
ω_{RT}
, and
ρ
are given as the design specifications.
The design procedure is summarized as
Summary of Design Procedure
Step 1
: Set parameter values of a given plant : (ζ ,
ω_{n}
) .
Step 2
: Specify the frequency domain constraints with respect to the required design specifications (m
_{r}
, Ω
_{r}
, Ω
_{n}
,
via the loop shaping method.
Step 3
: Specify the time domain constraints with respect to the required design specifications (Γ,
ω_{ST}
,
ω_{RT}
) via target function matching.
Step 4:
Perform the optimization problem of Eq. (51) with the specific value of
ρ
to determine the weighting factor,
Q
=
N^{T} N
, subject to the constraints obtained by Step 2 and 3.
Step 5: Determine the optimal PID control gain,
G
, by solving the ARE with the system parameters and the weighting matrix
Q
and
R
=
ρ I
determined in Step 4.
Step 6: Finish the process to determine the adequate controller if all of required design specifications are satisfied, if not reset the constraints, beginning with Step 2.
4. Simulation Examples
Example 1
: Consider the second order system with a small delay, referred in
[27]
as
For the optimization, the constraints and weighting vector
γ
are given as follows
f_{OS} (ρ, n_{0}, n_{1}, n_{2}) ≤ 9%
f_{WT} (ρ, n_{0}, n_{1}, n_{2}) ≥ω_{ST} (8 sec)
γ = [1 0.5], ρ = 0.0001
The frequency and time domain constraints are chosen to avoid the loop shape from the frequency barriers
Q
and
β
(
ω
) , and to satisfy that the overshoot and settling time of the target system are less than 9% and 8 sec, respectively.
According to the design procedures, design parameter
N
is determined as
N
= [0.0099 0.0151 0.005] , and PID controller parameters are obtained as
K_{p}
= 1.5027,
T_{i}
= 0.9967 and 0.5005
T_{d}
= via the ARE.
In
Table 1
, it is shown that the proposed method only yields the adequate loop shape required to avoid invading the frequency barrier, a good step response to meet the time response requirements of less than 9% overshoot and 8 sec settling time, as well as simultaneously minimizing the cost function.
Figs. 5
and
6
present the comparisons of the simulation results for frequency and time responses, respectively.
System performance comparisons of time and frequency responses for example 1.
System performance comparisons of time and frequency responses for example 1.
Comparison of the frequency shape of the loop transfer function with various other methods
Comparison of step response with various other methods for example 1
The results of the simulation are compared with other PID control methods by Yang
[27]
, Ho
[28]
, Grassi
[29]
, and Olof
[30]
. As shown in the
Table 1
and
Fig. 5
, the responses of the proposed and Olof methods satisfy the frequencydomain specifications. Among them, only the proposed method meets the required timedomain specifications described as settling time (8 sec) and overshoot (9%) in
Fig. 6
.
Example 2
: A fired heater problem is taken from
[31]
as an example of practical process; this is one of the most common examples with long time delay for the practical process. The open loop dynamic of the system is described by the transfer function,
G_{l}
(
s
) , at low fuel gas feed rates:
For the optimization, the constraints and weighting vector
γ
are given as follows.
f_{OS} (ρ, n_{0}, n_{1}, n_{2}) ≤ 10%
f_{WT} (ρ, n_{0}, n_{1}, n_{2}) ≥ω_{ST} (100 sec)
γ = [1 0.5], ρ = 0.0001
The design parameter
N
and PID controller are obtained as
N = [0.196 3.08 5.33]×10^{3} K_{p} = , 0.221 T_{i} = 0.02, and T_{d} = 0.4567 respectively.
Table 2
shows the results of the proposed controller and those by Grassi
[29]
, Olof
[30]
, Ho
[28]
, and Huang
[32]
. With the exception of the Ho method, they satisfy the time domain specifications with respect to the percent overshoot (10%) and settling time (100 sec). On the other hand, the loop shapes at the low and high frequency are obtained only by the proposed method, and Ho’s. Only by the proposed method presents the allowable good step response for the time domain performance simultaneously satisfied in addition to the frequency response to guarantee a magnitude higher than 15 db at the low frequency and lower than 40 db at high frequency.
Figs. 7
and
8
present a comparison of loop shapes in the frequency domain and step responses in the time domain, respectively.
System performance comparisons of time and frequency responses for example 2.
System performance comparisons of time and frequency responses for example 2.
Comparison of the frequency shape of the loop transfer function with other methods for example 2
Comparison of the step response with other methods for example 2
5. Conclusion
This paper proposes a new tuning method to combine time and frequencydomain requirements for an LQPID controller. The weighting matrices, Q and R, are formulated to improve limiting behaviour of the loop transfer function by Kalman’s equality and target function matching is used for frequency and time domain performances, respectively. Both of them are simultaneously considered as the constraints of the optimal problem with the cost function in which the 2norm of optimal control law and IAE index are applied. The PID control coefficients are obtained directly through the ARE, including the weighting factors Q and R. Simulation results have been presented to show the effectiveness of the proposed method in comparison to others. In conclusion, the proposed method guarantees not only the robust stability and design simplicity inherent in LQPID optimal control, but also considers the time and frequencydomain performances for the second order system.
Acknowledgements
This work was supported by the Korea Institute of Energy Technology Evaluation and Planning (KETEP) grant funded by the Korea government Ministry of Knowledge Economy (No.20132010101870)
BIO
ChangHyun Kim He received his B.A. in Electronics Engineering from Kangnam University, Korea, in 2003. And he received his M.S and Ph.D in Electrical Engineering from Hanyang University, Korea, in 2006, and 2015, respectively. He was a visiting professsor at Kandahar University, Afghanistan from 2006 to 2007. He is currently a researcher in the Research Institute of Industrial Science of Hanyang University, Korea. His current research interests include the robust control, MPC, optimal digital control, and its application to network congestion control, magnetic levitation systems.
Ju Lee He received his M.S. degree from Hanyang University, Seoul, South Korea, in 1988, and his Ph.D. from Kyusyu University, Japan in 1997, both in Electrical Engineering, He joined Hanyang University in September, 1997 and is currently a Professor of the Division of Electrical and Biomedical Engineering. His main research interests include electric machinery and its drives, electromagnetic field analysis, new transformation systems such as hybrid electric vehicles (HEV), and highspeed electric trains and standardization. He is a member of the IEEE Industry Applications Society, Magnetics Society, and Power Electronics Society.
HyungWoo Lee He received the B.S. and M.S. degrees from Hanyang University in 1998 and 2000, respectively, and the Ph.D. degree from Texas A&M University, College Station, TX, in 2003, all in electrical engineering. In 2004, he was a Postdoctoral Research Assistant in the Department of Theoretical and Applied Mechanics, Cornell University. In 2005, he was a contract Professor at the BK division of Hanyang University. In 2006, he has been a Senior Researcher at the Korea Railroad Research Institute. He joined Korea National University of Transportation since 2013 and is currently an Assistant professor of the Dept. of Railway Vehicle System Engineering. His research interests include design, analysis and control of motor/generator, power conversion systems, application of motor drives such as Maglev trains, robots, and modern renewable energy systems.
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