Pid controller design for process with time delay

TRUONG NGUYEN LUAN VU VIETNAM NATIONAL UNIVERSITY – HO CHI MINH CITY PRESS PID CONTROLLER DESIGN FOR PROCESS WITH TIME DELAY HO CHI MINH CITY UNIVERSITY OF TECHNOLOGY AND EDUCATION TRUONG NGUYEN LUAN[.]

TRUONG NGUYEN LUAN VU

PID CONTROLLER DESIGN

FOR PROCESS WITH

TIME DELAY

HO CHI MINH CITY UNIVERSITY OF

TECHNOLOGY AND EDUCATION

TRUONG NGUYEN LUAN VU

PID CONTROLLER DESIGN

FOR PROCESS WITH

TIME DELAY

Vietnam National University Ho Chi Minh City Press - 2018

ABOUT THE AUTHOR

Truong Nguyen Luan Vu is currently an Associate Professor of Mechanical Engineering at Ho Chi Minh City University of Technology and Education, Vietnam He received his B.S degree from Ho Chi Minh City University of Technology, Ho Chi Minh City National University in

2000, and his Master and Ph.D degrees from Yeungnam University, Republic of Korea in 2005 and 2009, respectively He has also taught at Yeungnam University for two years in terms of an International Professor His research interests include multivariable control, fractional control, PID control, process control, automatic control, and control hardware

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CONTENTS

OVERVIEW 9

CHAPTER 1 DESIGN OF ADVANCED PID CONTROLLERS FOR TIME-DELAY PROCESSES 13

1.1 INTRODUCTION 13

1.2 GENERALIZED IMC APPROACH FOR PID CONTROLLER

DESIGN 14

1.3 DESIGN OF PID CONTROLLER CASCADED WITH A LEAD-LAG FILTER 16

1.4 PROPOSED TUNING RULES FOR TYPICAL TIME- DELAY MODELS 18

1.4.1 First-Order plus Dead Time (FOPDT) Process Model 18

1.4.2 Integrator Plus Time Delay Model 18

1.4.3 First-Order Delayed Unstable Process (FODUP) Model 19

1.4.4 First-Order Delayed Integrating Process (FODIP) Model 21

1.5 Second-Order Delayed Unstable Process (SODUP) Model 21

1.5.1 SODUP Model with One Unstable Pole 21

1.5.2 SODUP Model with Two Unstable Poles 22

1.6 PERFORMANCE AND ROBUSTNESS MEASUREMENTS 22

1.6.1 Integral Absolute Error (IAE) Criteria 22

1.6.2 Overshoot 22

1.6.3 Maximum Sensitivity (Ms) Criterion 22

1.6.4 Total Variation (TV) 23

1.7 SIMULATION STUDY 23

1.8 DISCUSSION 43

1.8.1 Effect Of  On the Tradeoff between Performance and

Robustness 43

1.8.2 Effectiveness of the Proposed Method for the Dead-Time Dominant Process 45

1.9 CONCLUSIONS 46

REFERENCES 47

CHAPTER 2 IMC-PID CONTROLLER TUNING FOR PROCESS WITH TIME DELAY 49

2.1 INTRODUCTION 49

2.2 GENERALIZED IMC-PID DESIGN APPROACH 49

2.3 IMC-PID TUNING RULES FOR TYPICAL PROCESS 52

2.3.1 First-order Plus Dead Time (FOPDT) Process Model 52

2.3.2 Integrator Plus Time Delay (IPTD) Model 54

2.3.3 First-order Delay Unstable Process (FODUP) Model 55

2.3.4 First-order Delayed Integrating Process (FODIP) Model 58

2.3.5 Second-order Delayed Unstable Process (SODUP) Model 58

2.3.5.1 SODUP Model with One Unstable Pole 58

2.3.5.2 SODUP Model with Two Unstable Poles 60

2.4 ROBUST ANALYSIS 62

2.5 SIMULATION STUDY 64

2.6 CONCLUSIONS 80

REFERENCES 80

CHAPTER 3 FRACTIONAL-ORDER PROPORTIONAL-INTEGRAL CONTROLLERS DESIGN FOR TIME-DELAY PROCESSES 83

3.1 INTRODUCTION 83

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3.2 PRELIMINARIES 84

3.2.1 Fractional calculus 84

3.2.2 Integer Order Approximation 85

3.2.3 Fractional linear model 85

3.2.4 FOPI controller 86

3.3 ANALYTICAL DESIGN OF GENERALIZED FOPI

CONTROLLER TUNING RULES 87

3.4 PERFORMANCE AND ROBUSTNESS MEASUREMENTS 92

3.4.1 Integral Absolute Error (IAE) Criteria 92

3.4.2 Overshoot 92

3.4.3 Total variation (TV) 92

3.4.4 Resonant peak (Mp) criterion 92

3.5 SIMULATION STUDY 93

3.6 DISCUSSION 103

3.6.1 Effect of Mp Values on the Tuning Parameters and the Closed-Loop Performance 105

3.6.2 Fractional order (λ) guideline for the proposed FOPI parameter tuning 105

3.7 CONCLUSIONS 108

REFERENCES 108

CHAPTER 4 SMITH PREDICTOR BASED FRACTIONAL-ORDER PI CONTROL FOR TIME-DELAY PROCESSES 111

4.1 INTRODUCTION 111

4.2 THEORY DEVELOPMENT 112

4.2.1 Fractional Calculus 112

4.2.2 Design of FOPI Controller in Frequency Domain 114

4.2.3 SP-FOPI Controller Design Procedure 115

4.3 SELECTION OF TUNING PARAMETERS 119

4.4 SIMULATION STUDY 120

4.5 CONCLUSIONS 134

REFERENCES 134

CHAPTER 5 FRACTIONAL-ORDER PI CONTROLLER TUNING RULES FOR CASCADE CONTROL SYSTEM 137

5.1 INTRODUCTION 137

5.2 PRELIMINARIES 138

5.2.1 Fractional Linear Model 139

5.2.2 Design of FOPI Controller in Frequency Domain 139

5.3 ANALYTICAL TUNING RULES OF FOPI CONTROLLERS FOR CASCADE CONTROL SYSTEM 140

5.3.1 FOPI Controller Design Procedure for General Process Models 140

5.3.2 Design of Secondary Controller 140

5.3.3 Design of Primary Controller 142

5.4 SIMULATION STUDY 144

5.5 CONCLUSIONS 146

REFERENCES 146

APPENDIX USE OF MATLAB IN PID CONTROL 148

REFERENCES 151

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OVERVIEW

The IMC structure, a control structure incorporating the internal model of plant control, has been widely utilized in the design of PID-type controllers, usually denoted IMC-PID controllers, because of its simplicity, flexibility, and apprehensibility The most important advantage of IMC-PID tuning rules is that the tradeoff between closed-loop performance and robustness can be directly obtained using a single parameter related to the closed-loop time constant IMC-PID tuning rules can provide good set-point tracking, but have been lacking regarding disturbance rejection, which can become severe for processes with a small time-delay/time constant ratio Disturbance rejection is more important than set-point tracking in many process control applications, and thus is an important research topic

A 2DOF control scheme can be used to improve disturbance performance for various time-delay processes The controller’s performance can be significantly enhanced using a PID controller cascaded with a conventional filter, something easily implementable in modern control hardware Consequently, several controller tuning rules have been reported despite PID controllers cascading with conventional filters being often more complicated than a conventional PID controller for processes with time delay However, this difficulty can be overcome

by using appropriate low-order Padé approximations of the time delay term in the process model Therefore, the PID-type controller can be indirectly obtained by considering the Padé approximations Accordingly, first-order Padé approximations have been used by a number of authors This expansion does introduce some modeling errors, though within acceptable limits To reduce this problem, a higher order Padé approximation has been used Alternatively, a Taylor expansion can be directly applied to transform an ideal feedback controller into a standard

PID-type controller The performance of the resulting IMC-PID controller is largely dependent on how closely the PID controller approximates an ideal controller equivalent to the IMC controller It also depends on the structure of the IMC filter Many methods for approximating an ideal controller to a PID controller have been discussed, but most are case dependent Few unified approaches to PID controller design that can be employed for all typical time-delay processes have been fully achieved PID filter controllers closely approximating ideal feedback controllers are also obtained by using directly high order Padé approximations, since those of previous works are only indirectly used Padé approximations in terms of the time delay part The study is focused on the design of PID controllers cascaded with a lead-lag filters

to fulfill various control purposes; tuning rules should be simple, of analytical form, model-based, and easy to implement in practice with excellent performance for both regulatory and servo problems

Recently, fractional-order dynamic systems are useful in representing various stable physical phenomena with anomalous decay because they can provide increased flexibility with less computational cost, allowing precise simulation and implementation Fractional calculus (i.e fractional integro-differential operators) is a generalization of integration and differentiation to non-integer orders It is obtained from ordinary calculus by extending ordinary differential equations (ODE) to fractional-order differential equations (FODE) Similarly, a fractional-order proportional-integral-derivative (FOPID) controller is a generalization of a standard (integer) PID controller; its output is a linear combination of the input and the fractional integral or derivative of the input [2] It affords more flexibility in PID controller design due to its five controller parameters (instead of the standard three): proportional gain, integral gain, derivative gain, integral order, and derivative order However, the tuning rules of fractional-order PID (FOPID) controllers are much more complex than those of standard (integer) PID controllers with only three parameters The two extra parameters (λ and µ) give this

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type of controller improved flexibility over integer PID controllers, giving it much industrial applicability Tuning methods of PIλDμcontrollers can be generally classified as either analytic or heuristic Most analytic methods are tuned by considering the nonlinear objective function, which is depended on user-imposed specifications

In this book, several case studies are reported to demonstrate the simplicity and effectiveness of the proposed method compared with several other prominent design methods The simulation results confirm that the proposed method can afford robust PID filter controllers for both disturbance rejection and set-point tracking

Chapter 1 DESIGN OF ADVANCED PID CONTROLLERS

FOR TIME-DELAY PROCESSES

1.1 INTRODUCTION

The design of proportional-integral-derivative (PID) controllers cascaded with first-order lead-lag filters is introduced for various time-delay processes The controller’s tuning rules are directly derived using the Padé approximation on the basis of internal model control (IMC) for enhanced stability against disturbances A two-degrees-of-freedom (2DOF) control scheme is employed to cope with both regulatory and servo problems Simulation is conducted for a broad range of stable, integrating, and unstable processes with time delays Each simulated controller is tuned to have the same degree of robustness in terms of maximum sensitivity (Ms) The results demonstrate that the design method provides superior disturbance rejection and set-point tracking when compared with recently published PID-type controllers Controllers’ robustness is investigated through the simultaneous introduction of perturbation uncertainties to all process parameters to obtain worst-case process-model mismatch The process-model mismatch simulation results demonstrate that the design method consistently affords superior robustness

to the controlled output, set-point input, disturbance input, and manipulated variables, respectively If there is no model error:

where pm

 

(minimum phase), and pA

 

(it is the non-minimum phase that may include dead time and/or right half plane zeros chosen to be all-pass) The requirement that pA

 

is necessary for the controlled variable to track its set-point with no set

off-The IMC controller q s can then be designed as:

 

where  is an adjustable parameter that can be used to trade

performance and robustness off against each other The integer n is

selected to be large enough for the IMC controller proper The parameter

i

 is determined so as to cancel poles near zero in Gd

 

The ideal feedback controller, Gc

 

responses in Eq (1.7) and Eq (1.8) can be constituted as:

c

P

qG

s s

As indicated by Eq (1.10), the numerator expression





of a PID filter-type controller, it is necessary to find a PID-filter controller that approximates the ideal feedback controller most closely

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1.3 DESIGN OF PID CONTROLLER CASCADED WITH A LEAD-LAG FILTER

The ideal feedback controller, Gc

 

PID controller as follows:

Because Gc

 

Expanding Gc

 

3

f q

A comparison of Eq (1.13) and Eq (1.16) yields tuning rules of the proportional, integral, and derivative terms of the proposed PID controller:

time-1.4.1 First-Order plus Dead Time (FOPDT) Process Model

One of the most widely used models is the FOPDT process model:

P

KG

s

e s

1

s s

This IMC filter form has been considered by several researchers [5,

9, 11] Accordingly, the ideal feedback controller follows:

The lead-lag filter parameters b and a can be found from Eq (1.20)

and Eq (1.21), respectively Tuning rules for the proposed PID controller can also be obtained by considering Eq (1.17), Eq (1.18), and Eq (1.19)

The value of the extra degree of freedom, β, can be determined by compensating the open-loop pole at s  1 τ According to Eq (1.5), it is:

This equation has also been used by several researchers

1.4.2 Integrator Plus Time Delay Model

This model is also applicable to delayed integrating processes (DIPs), which can be reasonably modeled by considering the integrator

as a stable pole near zero for the aforementioned IMC procedure to be

applicable to an FOPDT, since the term β disappears at s = 0 As

discussed by Lee et al [11], the controller resulting from a model with a

stable pole near zero can give more robust closed-loop responses than

those based on models with an integrator or an unstable pole near zero

Therefore, a DIP can be approximated to an FOPDT as follows:

where ψ is a sufficiently large arbitrary constant The IMC filter

structure for the DIP model is identical to that for the FOPDT model:

Thus, the ideal feedback controller for the DIP model can be

approximated as that for the FOPDT model The PID controller tuning

rules used for the FOPDT model are applicable to the DIP model after a

simple modification: the process gain and time constant are replaced by

Kψand ψ, respectively  can be obtained as:

2 θ

1.4.3 First-Order Delayed Unstable Process (FODUP) Model

The unstable FOPDT process model is frequently used in the

the FOPDT process model, i.e.,

     

f s  βs1 s1 Therefore, the IMC controller becomes:

      

The most widely used approximate model for chemical processes is

the SOPDT model:

4

θ τ 2

Eq (40) and Eq (41) have been widely used to design 2DOF

controllers for SOPDT process models

1.4.4 First-Order Delayed Integrating Process (FODIP) Model

The FODIP process model can be represented as:

Thus, its ideal feedback controller can be approximated as that of

the SOPDT process model The PID controller tuning rules obtained for

the SOPDT process model can also be used for the FODIP process model

after a simple modification: replacing the process gain (K) and time

constants (τ1 and τ2) in Eq (1.38) with Kψ, ψ, and τ, respectively

The values of β1 and β2 are easily obtained from the modification of Eq

(1.40) and Eq (1.41), where τ1and τ2are replaced by ψand τ

1.5 SECOND-ORDER DELAYED UNSTABLE PROCESS

(SODUP) MODEL

1.5.1 SODUP Model with One Unstable Pole

The transfer function of the process model is:

The resulting PID controller tuning rules can be designed by the

above procedure for the SOPDT in terms of changing the sign of 1

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1.5.2 SODUP Model with Two Unstable Poles

On the basis of the above design procedure, the process can be

representatively modeled as:

f s  β s β s1 s1 The IMC controller

is then formulated by:

q s   s1  s1 β s β s1 K s1 (1.47) From this:

To evaluate closed-loop performance, the IAE criterion is

considered here for both disturbance rejection and set-point tracking:

Responses overshoot if they exceed the ultimate value following a

step change in disturbance or set-point

1.6.3 Maximum Sensitivity (Ms) Criterion

The robustness of a control system can be evaluated from the peak value of the sensitivity function Ms, which has many useful physical

interpretations [13, 14] Ms is defined as the inverse of the shortest distance from the Nyquist curve of the loop transfer function to the

For a fair comparison, the model-based controllers should be tuned

by adjusting λ so that the Ms values are identical, meaning that all comparative controllers are designed to have the same level of robustness

in terms of maximum sensitivity

1.6.4 Total Variation (TV)

TV is a measure of the smoothness of a signal and can be used to evaluate the required control effort It is computed from the total variation of the manipulated variable by considering the sum of all moves

up and down:

1 1

The effectiveness of the proposed PID tuning rules is demonstrated

in several illustrative examples

Example 1.1: FOPDT Process

The following FOPDT process was introduced by Chien et al [15]

It is an important viscosity loop in a polymerization process with a large open loop time constant and dead time The process transfer function is:

Shamsuzzoha and Lee [9] previously confirmed the superiority of

their method over that of Chien et al [15] for this polymerization process Therefore, in this simulation study, the proposed PID controller

is compared with the controller of Shamsuzzoha and Lee [9], as well as

the PID controllers of Horn et al [5] and Rivera et al [2] For a fair

comparison, all controllers are tuned to have the same level of robustness

by measuring the Ms value The closed-loop time constant, λi , is adjusted to obtain Ms = 2.62 in each case

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The resulting controller parameters, together with performance and robustness indices calculated for the foreknown methods, are tabulated in Table 1.1 A load step change of -1.0 is introduced into the load disturbance and the corresponding simulation results are shown in Fig 1.2 The figure and table show that the proposed controller affords superior closed-loop performance with faster and better-balanced responses than the other controllers in terms of disturbance rejection The controlled variable responses resulting from unit step changes

in the set-point are also shown in Fig 1.3 Under the freedom (1DOF) control structure, any controller achieving good disturbance rejection essentially gives a significant overshoot in set-point response To overcome this, the 2DOF control structure is commonly used Consequently, the set-point filter used for the set-point response has a clear benefit Therefore, a 2DOF controller with set-point filter is used in each case, except for the method of Rivera et al [2] To obtain an enhanced set-point response, this method has been previously suggested for use with a 1DOF controller with a conventional lag filter; therefore, it

one-degree-of-is used here without modification For the proposed method and that of Shamsuzzoha and Lee [9],  in the set-point filter is selected to be 0.3 Fig 1.3 shows that the proposed method, together with that of Shamsuzzoha and Lee [9], performs better than the other methods However, the PID controller of Shamsuzzoha and Lee [9] affords the largest value of TV due to the dominant lead term in the controller filter

The robust performance of the proposed method is demonstrated in another simulation study, where perturbation uncertainties of ±10% are introduced to the process gain, time constant, and time delay in the worst direction and assuming the actual processes as

 

in Table 1.2 for the set-point and disturbance rejection problems, respectively; they confirm that the controller settings of the proposed method provide more robust performance than those of the other methods for both disturbances and set-point changes

Fig 1.2 Simulation results of PID controllers for unit step disturbance

(Example 1.1)

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Fig 1.3 Simulation results of PID controllers for unit step set-point

change (Example 1.1)

Example 1.2: DIP Process

The following process model [16] is considered It can be reasonably approximated to the FOPDT process model as follows:

In this simulation study, the constant ψ is arbitrarily selected as

100 The performance of the proposed method is compared with those of the aforementioned design methods i is adjusted to obtain Ms = 2.40 in each case Figs 1.4 and 1.5 show the closed-loop time responses for disturbance rejection and set-point, respectively The proposed controller shows a fast, well-balanced response with minimum integral IAE values, whereas that of Shamsuzzoha and Lee [9] shows a slow response with a longer settling time The controllers of Horn et al [5] and Rivera et al [2] give large overshoots The resulting controller parameters, together with their performances, and robustness indices, are summarized in Table 1.3 The results show that the proposed method affords good performance for both disturbance rejection and set-point tracking

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In the robustness study, the controllers are evaluated by considering the worst cases under simultaneous ±10% perturbation uncertainties in all three process parameters The simulation results for plant-model mismatch are listed in Table 1.4 The proposed method consistently affords strong robust performance both for disturbances and set-point changes

Fig 1.4 Simulation results of PID controllers for unit step disturbance

(Example 1.2)

Fig 1.5 Simulation results of PID controllers for unit step set-point

change (Example 1.2)

Example 1.3: FODUP Process

The following FODUP model is considered:

Figs 1.6 and 1.7 show the disturbance and set-point responses afforded by each of the methods, respectively The proposed method is shown to perform well compared with the other methods The controller characteristics summarized in Tables 1.5 and 1.6 confirm the improvement in the performance of the proposed method

A perturbation uncertainty of ±10% is simultaneously introduced to all three process parameters to evaluate the controllers’ robustness The simulation results in Table 1.6 indicate that the proposed PID controller provides improved robust performance both in terms of disturbance rejection and set-point tracking

Example 1.4: FODIP Process

The FODIP process studied by Zhang et al [21] and Shamsuzzoha and Lee [8] is considered:

p

G s 100e s/ 100s1 4s1 , where the arbitrary constant ψ=100 Figs 1.8 and 1.9 show the output responses of each tuning method for disturbance rejection and set-point tracking, respectively

The proposed method shows the fastest settling time and a small overshoot The method of Shamsuzzoha and Lee [8] settles next quickest, while Zhang et al.’s [21] method gives significant overshoot and oscillation that requires a long time to settle The controller setting parameters for each method are listed in Table 1.7; it also shows the advantages of the proposed method over the other methods Table 1.8 shows performance index values, when ±10% perturbation uncertainty is simultaneously introduced to all three process parameters for worst-case model mismatch The performance and robustness indices clearly demonstrate the significantly more robust performance of the proposed controller

The most important factor for robust performance is the value of b

In Shamsuzzoha and Lee’s [8] method, robust performance was achieved

using a value of 0.1b instead of b When this is applied to the proposed

method, the level of robustness increases, as Ms = 3.57 To guarantee a fair comparison, the controller of Shamsuzzoha and Lee [8] is adjusted to have the same degree of robustness, by using 2.017; in this case, the resulting controller of the proposed method affords a much enhanced robust performance

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Example 1.5: SODUP (One Unstable Pole) Process

The following SOPUP model with one unstable pole was approximated by Huang and Chen [20]:

In previous research, Lee et al [11] confirmed the superiority of

their method over several other design methods, such as Huang and Chen [20] and Poulin and Pomerleau [22] Shamsuzzoha and Lee [8] also demonstrated the advantage of their method over that of Rao and Chidambaram [10] Therefore, the proposed method is compared with both methods to show its effectiveness To provide a fair comparison, each controller is tuned to the same degree of robustness by adjusting

In this example, both the proposed method and that of Shamsuzzoha and

Lee [8] employ a value of 0.1b to improve their robust performance

Unit step changes are introduced to both the load disturbance and set-point A set-point filter is used in each case to enhance the set-point response without affecting the disturbance response

Fig 8 Simulation results of PID controllers for unit step disturbance

(Example 4)

Fig 9 Simulation results of PID controllers for unit step set-point change

(Example 4)

Fig 110 Simulation results of PID controllers for unit step disturbance

(Example 1.5)

38

Fig 1.11 Simulation results of PID controllers for unit step set-point

change (Example 1.5) The simulation results in Figs 1.10 and 1.11, and Table 1.9 show that the proposed controller gives better output responses with smaller IAE values than those of the other methods, particularly with respect to disturbance rejection

To evaluate robustness, perturbation uncertainties of ±10% are simultaneously introduced to all three parameters in the worst direction The simulation results of model mismatch for each method are tabulated

in Table 1.10 The performance and robustness indices clearly demonstrate the advantages of the proposed controller for both disturbance rejection and set-point tracking

Example 1.6: SODUP (Two Unstable Poles) Process

The SODUP process considered below has been studied by a number of authors [6, 8, 10, 11]

of robustness Accordingly, the closed-loop time constant, i , is respectively adjusted to 0.35 and 0.64 for the proposed method and that of Rao and Chidambaram [10] to give the same robustness level of Ms = 3.1

40

Simulation results when simultaneously assuming perturbation uncertainties of ±10% in all three parameters of the process are summarized in Table 1.12 The proposed controller performs robustly for both disturbance rejection and set-point tracking with minimum IAE values

(a)

(b)

Fig 1.12 Simulation results of PID controllers for unit step disturbance (Example 1.6): controlled variable (a); manipulated variable (b)

(a)

(b) Fig 1.13 Simulation results of PID controllers for unit step set-point

change (Example 1.6): controlled variable (a); manipulated variable (b)

Example 1.7: High Order Process with Positive Zero

The following high order process with positive zero was studied by Skogestad [23]: