Computation of All Stabilizing PID Gain for Second-Order Delay System
The problem of stabilizing a second-order delay system using classical proportional-integral-derivative (PID) controller is considered. An extension of the Hermite-Biehler theorem, which is applicable to quasipolynomials, is used to seek the set of complete stabilizing PID parameters. The range of admissible proportional gains is determined in closed form. For each proportional gain, the stabilizing set in the space of the integral and derivative gains is shown to be either a trapezoid or a triangle.
Dead times are often encountered in various engineering systems and industry processes such as electrical and communication network, chemical process, turbojet engine, nuclear reactor, and hydraulic system. In fact, delays are caused by many phenomena like the time required to transport mass, energy or information, the time processing for sensors, the time needed for controllers to execute a complicated algorithm control, and the accumulation of time lags in a number of simple plants connected in series . Delay makes system analysis and control design much more complex which is explained as follows. First, the effect of the control action takes some time to be felt in the controlled variable. Second, the control action that is applied based on the actual error tries to correct a situation that originated previously. Finally, in the frequency domain, the time delay introduces an extra decrease in the systems phase, which may cause instability [1, 2].
Today, proportional-integral (PI) and proportional-integral-derivative (PID) controller types are the most widely used control strategy. It is estimated that over 90% of process control applications employ PID control thanks to its essential functionality and structural simplicity . Since the minimal requirement for PID controller is to guarantee the system stability. It is desirable to know the complete set of stabilizing PID parameters before tuning and design. Surveys have reported that poor tuning, configuration errors, traditional and empirical techniques such as Ziegler Nichols or Cohen-Coon tuning methods are found in the industrial application of PID . A great deal of academic and industrial effort has focused on improving PID control in the areas of tuning rules to decrease the rising gap between engineering practice and control theory. Recent studies have made use of the generalization of Hermite-Biehler theorem to compute the set of all stabilizing PID controllers for a given plant . Since almost all plants encountered in process control contain time delays, so computing the complete set of PID controllers that stabilize time delay system is of considerable importance. A robust PI/PID design via numerical optimization for delay processes was proposed in . Padma Sree et al.  propose a PI/PID controllers design for first-order delay system by extracting the coefficients of the numerator and denominator of the closed-loop transfer function. In the work presented in , the authors have developed a graphical approach which is based on D-partition method , the borders of absolute and relative stability regions are described in the parameter space. A unified approach and Delta operator are used to obtain unified stability boundaries for PI, PD, and PID controller for an arbitrary order delay system; where the boundaries can be found when only the frequency response and not the parameters of the plant are known . Eriksson and Johansson  consider a design of PID controller for system with varying time delay using multi-objectives’ optimization. Neural network has been widely applied for nonlinear dynamical system identification via PID controller . Zhang et al.  present an intelligent nonlinear PID controller based on the recurrent neural networks (RNNs) to control multivariable plants by using two predictive control schemes. In [14, 15], the authors propose a new PID neural networks (PIDNN) which is a dynamic multilayer network based on P, I, and D neurons. This PIDNN is used to control multivariable plants  and has also the ability to control time delay system . In , Roy and Iqbal have explored PID tuning of first-order delay system using a first-order Padé approximation and the Hermite-Biehler stabilization framework. The set’s characterization of all stabilizing PI/PID parameters using a version of the Hermit-Biehler theorem for first-order delay system is presented in [5, 17–20]. However, these results are not applicable to the second-order delay system. Although, the PID stabilization for a given time delay plant has been developed  using the generalized Nyquist criterion, the stabilizing set of the proportional gain cannot be found. In [22–24], the characterization of the set of all stabilizing P/PI/PID parameters is given by using the Hermit-Biehler Theorem for polynomials for a class of time delay system which verify the interlacing property at high frequencies. This method, however, is complex and there are difficulties to achieve the stabilizing range of proportional parameter. As shown in [23, Example 2], this latter approach does not determine the precise range of proportional gain which is larger than the exact stabilizing range given by . Furthermore, a known stabilizing value of is first determined using Nyquist criterion, then the entire stability region is established. In our work, the stabilizing problem of PID controller for second order delay system is analyzed using the extended Hermit-Biehler theorem for quasi-polynomials. The exact range of stabilizing proportional gain is first determined. The set of stabilizing integral and derivative constant values are then derived. Our algorithm is more simple and faster in time computing than the other presented in .
2. Preliminary Results for Analyzing Time Delay System
where: and are polynomials with real coefficients and represent time delays. These characteristic equations are recognized as quasi-polynomials. Under the following assumptions:
One can consider the quasi-polynomials described by
The zeros of are identical to those of since does not have any finite zeros in the complex plane. However, the quasi-polynomial has a principal term since the coefficient of the term containing the highest powers of and is nonzero. If does not have a principal term, then it has an infinity roots with positive real parts .
The stability of the system with the characteristic equation (2.1) is equivalent to the condition that all the zeros of must be in the open left half of the complex plan. We said that is Hurwitz or is stable. The following theorem gives a necessary and sufficient condition for the stability of .
Theorem 2.1 (see ). Let be given by (2.2), and write
where and represent, respectively, the real and imaginary parts of
Under conditions () and (), is stable if and only if(1) and have only simple, real roots and these interlace,(2)for some in ,where and denote the first derivative with respect to of and , respectively.
A crucial stage in the application of the precedent theorem is to make sure that and have only real roots. Such a property can be checked while using the following theorem.
Theorem 2.2 (see ). Let and designate the highest powers of and which appear in . Let be an appropriate constant such that the coefficient of terms of highest degree in and do not vanish at . Then a necessary and sufficient condition that and have only real roots is that in each of the intervals
or have exactly real roots for a sufficiently large .
3. PID Control for Second Order Delay System
A second order system with delay can be mathematically expressed by a transfer function having the following form:
where is the static gain of the plant, is the time delay, and and are the plant parameter’s which are always positive. The characteristic equation of the closed-loop system is given by
we deduce the quasi-polynomial :
by replacing by , we get
Clearly, the parameters and only affect the real part of whereas the parameter affects the imaginary part.
Let’s put , we get
The application of the second condition of Theorem 2.1, led us to
from (3.6) we have
for (a value that annul ), and we get:
which implies since and .
We pass to the verification of the interlacing condition of and roots. For such purpose, we are going to determine the roots from the imaginary part, which is translated by the following relation:
We notice the is a trivial root of the imaginary part. The others are difficult to solve analytically. However, this can be made graphically. The following two cases are presented.
Case 1 (). In this case, we graph the curves of and of which are presented in Figure 1. that is, verifies
Case 2 (). Figure 2 represents the case where the curves of and of are tangent in , where is the largest number of .
The plot in Figure 3 corresponds to the case where and the plot of does not intersect twice in the interval .
For this new quasi-polynomial, we see that and where and designate the highest powers of and which appear in . We choose that satisfies the condition giving by theorem 3 as and . According to Figure 1, we notice that for , possess four roots in the interval including the root at origin. As is odd function of so it possesses seven roots in . Hence, we can affirm that has exactly in . In addition, it can be shown that has two real roots in each of the intervals and for . It fallows that has exactly real roots in for . At the end, according to Theorem 2.2, has only real roots for every in . For , corresponding to Figures 2 and 3, the roots of are not real. We pass to determine the superior value of . According to the definition of , if then the curves of and are tangent in the point . Which is translated by
once is determined, the parameter is expressed by the relation:
Theorem 3.1. The have real roots if and only if: where is the solution of the equation in the interval .
After determination of the roots of the imaginary part , we pass to the evaluation of these roots by the real part
From condition 1 of Theorem 2.1, the roots of and have to interlace for to be stable. We evaluate at the roots of the imaginary part .
For , we have
for , where , we get:
Interlacing the roots of and is equivalent to (since ), .
We can use the interlacing property and do it as which has only real roots to reach that possess real roots too.
From the previous equations we get the following inequalities:
Thus intersecting all these regions in the (,) space, we get the set of (,) values for which the roots of and interlace for a fixed value of . We notice that all these regions are half planes with their boundaries being lines with positive slopes .
Example 3.2. Consider the plant given by relation (3.1) with the following parameters and :
The imaginary part has only simple real roots if and only if .
We set the controller parameter to 1.3, which is inside the previous range. For this , takes the form: We next compute some of the positive real roots of this equation and arrange them in increasing order of magnitude: Using (3.18) we calculate the parameters and for : Interlacing the roots of the real and the imaginary part occurs for , if and only if the following inequalities are satisfied: The boundaries of these regions are illustrated in Figure 4.
In this case, the stability region is defined by only two boundaries: because we have the following inequalities:
Example 3.3. Consider the plant (3.1) with the following parameters
The imaginary part has only simple real roots if and only if .
We now set the controller parameter to 0.5, which is inside the previous range. For this , takes the form We next compute some of the positive real roots of this equation and arrange them in increasing order of magnitude Using (3.18), we calculate the parameters and for : Interlacing the roots of the real and imaginary part occurs for , if and only if the following inequalities are satisfied: The boundaries of these regions are illustrated in Figure 5.
In this case, the stability region is defined by only two boundaries: because we have the following inequalities:
We now state an important technical lemma that allows us to develop an algorithm for solving the PID stabilization problem. This lemma shows the behavior of the parameter , for different values of the parameter inside the range proposed by Theorem 3.1.
Lemma 3.4. If where is the solution of the equation in the interval .Then:(1)(2)(3)
Proof. From (3.18) we have then
Remark 3.5. As we can see from Figure 1, the odd roots of that is, for verify and are getting closer to as increase, so we have: Moreover, since cosine function and sine function are monotonically increasing between and , we have
Remark 3.6. As we can see from Figure 1, the even roots of that is, for verify and are getting closer to as increase, so we have: Moreover, since cosine function and sine function are monotonically decreasing between and , we have:
The change of can be found with graphical approach. Given a stabilizing value inside the admissible range by using Theorem 3.7, the curve of is plotted to obtain their roots denoted by , the curves of and are plotted too in order to understand the behavior of .
As far as the odd roots of are concerned, the corresponding is decreasing by large magnitude, and for the even ones, the corresponding is increasing by large magnitude. However, compared to the change of , the difference between the values of and is much smaller than both for odd and even . Thus, the have the similar change rules as ,(i) for odd values of ,(ii) for even values of .
We are ready to state the main results of our work.
Theorem 3.7. Under the above assumptions on , , , and , the range of values for which a solution exists to the PID stabilization problem of an open-loop stable plant with transfer function is given by where is the solution of the equation in the interval .
For values outside this range, there are no stabilizing PID controllers. The complete stabilizing region is given by(1)the cross-section of the stabilizing region in the space is the Trapezoid if (2)the cross-section of the stabilizing region in the space is the triangle if ,
The parameters necessary for determining the boundaries, can be determined using (3.21) where are the positive-real solutions of arranged in ascending order of magnitude.
In view of Theorem 3.7, we propose an algorithm to determine the set of all stabilizing parameters for a second order delay system.
An algorithm for determining PID parameters is given as follows
(1)Choose in the interval suggested by Theorem 3.7 and initialize ,(2)Find the roots of ,(3)Compute the parameter associated with the for founded, (4)Determine the stability region in the plane using Figure 7 (Theorem 3.7).(5)Go to step 1.
In this work, we have proposed an extension of Hermit-Biehler theorem to compute the stability region for second-order delay system controlled by PID controller. The procedure is based first on determining the range of proportional gain value for which a solution to PID stabilization exists. Then, it is shown that for a fixed inside this range, the stabilizing integral and derivative gain values lie inside a region with known shape and boundaries.
Q. C. Zhong, Robust Control of Time Delay System, Springer, London, UK, 2006.
S.-I. Niculescu, Delay Effects on Stability, vol. 269 of Lecture Notes in Control and Information Sciences, Springer, London, UK, 2001.View at: MathSciNet
C. Knospe, “PID control,” IEEE Control Systems Magazine, vol. 26, no. 1, pp. 30–31, 2006.View at: Google Scholar
H. Takatsu, T. Itoh, and M. Araki, “Future needs for the control theory in industries—report and topics of the control technology survey in Japanese industry,” Journal of Process Control, vol. 8, no. 5-6, pp. 369–374, 1998.View at: Google Scholar
G. J. Silva, A. Datta, and S. P. Bhattacharyya, PID Controllers for Time-Delay Systems, Control Engineering, Birkhäuser, Boston, Mass, USA, 2005.View at: MathSciNet
J. McKay, “The D-partition method applied to systems with dead time and distributed lag,” Measurement and Control, vol. 3, no. 10, pp. 293–294, 1970.View at: Google Scholar
T. Lee, J. M. Watkins, T. Emami, and S. Sujoldžić, “A unified approach for stabilization of arbitrary order continuous-time and discrete-time transfer functions with time delay using a PID controller,” in Proceedings of the IEEE Conference on Decision and Control (CDC '07), pp. 2100–2105, 2007.View at: Publisher Site | Google Scholar
S.-J. Li and Y.-X. Liu, “An improved approach to nonlinear dynamical system identification using PID neural networks,” International Journal of Nonlinear Sciences and Numerical Simulation, vol. 7, no. 2, pp. 177–182, 2006.View at: Google Scholar
H. Shu, X. Guo, and H. Shu, “PID neural networks in multivariable systems,” in Proceedings of the IEEE International Symposium on Intelligent Control, pp. 440–444, 2002.View at: Google Scholar
A. Roy and K. Iqbal, “PID controller tuning for the first-order-plus-dead-time process model via Hermite-Biehler theorem,” ISA Transactions, vol. 44, no. 3, pp. 363–378, 2005.View at: Google Scholar
G. J. Silva, A. Datta, and S. P. Bhattacharyya, “Stabilization of time delay systems,” in Proceedings of the American Control Conference, vol. 2, pp. 963–970, 2000.View at: Google Scholar
G. J. Silva, A. Datta, and S. P. Bhattacharyya, “Stabilization of first-order systems with time delay using the PID controller,” Proceedings of the American Control Conference, vol. 6, pp. 4650–4655, 2001.View at: Google Scholar
H. Xu, A. Datta, and S. P. Bhattacharyya, “PID stabilization of LTI plants with time-delay,” in Proceedings of the 42nd IEEE Conference on Decision and Control (DC '03), vol. 4, pp. 4038–4043, 2003.View at: Google Scholar
V. A. Oliveira, L. V. Cossi, A. M. F. Silva, and M. C. M. Teixeira, “PID stabilization of a class of time delay systems,” in Proceedings of the 44th IEEE Conference on Decision and Control, and the European Control Conference (CDC-ECC '05), pp. 1367–1372, 2005.View at: Publisher Site | Google Scholar
S. P. Bhattacharyya, H. Chapellat, and L. H. Keel, Robust Control: The Parametric Approach, Prentice-Hall, Upper Saddle River, NJ, USA, 1995.