512 Weidong Zhang et al. / ISA Transactions 41 (2002) 511–520 Fig. 1. Unity feedback control loop. Fig. 2. IMC structure.of time-domain response, such as overshoot andrise time, or frequency-domain response, such asresonance peak and stability margin. It can be sim- C͑ s ͒ply formulated as follows: Given a nominal plant Q͑ s ͒ϭ 1ϩG ͑ s ͒ C ͑ s ͒and desired time-domain response or frequency-domain response, we design a PID controller to and we havemeet these indexes. In this paper, an H ϱ PID con-troller is ﬁrst presented based on optimal control Q͑ s ͒ C͑ s ͒ϭ . ͑2͒theory. The properties of the controller are then 1ϪG ͑ s ͒ Q ͑ s ͒investigated and compared with an H 2 PID con-troller and a Maclaurin PID controller. It is shown In the nominal case, the unity feedback loop canthat these three controllers can provide quantita- be equivalent to the well-known IMC structure de-tive time-domain response and frequency-domain picted in Fig. 2. The sensitivity transfer functionresponse. The work is of signiﬁcance in that it can of the closed-loop system can be written asestablish a relationship between the classical de- 1sign requirement and that of optimal design meth- S͑ s ͒ϭ ϭ1ϪG ͑ s ͒ Q ͑ s ͒ ͑3͒ 1ϩG ͑ s ͒ C ͑ s ͒ods, and provide insight into control system de-sign. and the complementary sensitivity transfer func- The paper is organized as follows. In Section 2 tion asthe suboptimal H ϱ PID controller is derived ana-lytically. The H 2 PID controller and Maclaurin G͑ s ͒C͑ s ͒ T͑ s ͒ϭ ϭG ͑ s ͒ Q ͑ s ͒ . ͑4͒PID controller are brieﬂy introduced. The perfor- 1ϩG ͑ s ͒ C ͑ s ͒mance degree is deﬁned and the quantitative time- T ( s ) is just the closed-loop transfer function.domain response and frequency-domain response It is well known that any model obtained byare discussed in Section 3. Finally, the paper is using experimental procedures involves uncer-concluded in Section 4 with a brief discussion of tainty in its parameters. For the low-order modelsthe results. used in many practical applications, the uncer- tainty is expressed in a parametric fashion. The2. Controller design parameters of the simpliﬁed model used to ap- proximate a usually high-order dynamics are Consider the unity feedback control system known to lie in an interval. The mean value of ˜ each parameter is chosen to represent its nominalshown in Fig. 1, where G ( s ) denotes the plant and value, often used to obtain nominal plant and con-C ( s ) denotes the controller. In the context of pro- troller parameters ͓8,9͔. For example, the plant is ˜cess control, the process G ( s ) is usually describedby the following model ͑or the nominal plant͒ ˜ ˜ K e ϪsG(s): G͑ s ͒ϭ ˜ ͑5͒ ˜ sϩ1 Ke Ϫ s G͑ s ͒ϭ , ͑1͒ with sϩ1 K ͓ K Ϫ ,K ϩ ͔ , ˜ ˜ ͓ Ϫ, ϩ͔ , ˜ ͓ Ϫ, ϩ͔ . where K is the gain, is the time constant, and isthe time delay. We deﬁne the transfer function The midrange plant is chosen as the model:
Weidong Zhang et al. / ISA Transactions 41 (2002) 511–520 513 Ke Ϫ s In other words, we must guarantee that S ( s ) G͑ s ͒ϭ , ͑6͒ ϭ ( 1ϪG ( s ) Q ( s )) has a zero at sϭ0 to cancel the sϩ1 pole of W ( s ) . With the constraint, the unique op-where timal Q im ( s ) is obtained as follows: K Ϫ ϩK ϩ Ϫϩ ϩ Ϫϩ ϩ ͑ sϩ1 ͒͑ 1ϩ s/2͒ Kϭ , ϭ , ϭ . Q im ͑ s ͒ ϭ . 2 2 2 K A central objective in automatic control is that a Obviously Q im ( s ) is improper. Now we use thephysical quantity is made to behave in a pre- following low-pass ﬁlter to roll Q im ( s ) off at highscribed way by using the error between the system frequency:output and the setpoint input. This gives rise to the 1optimal control. Assume that the optimal perfor- J͑ s ͒ϭ , Ͼ0.mance index is H ϱ optimal, i.e., minʈW(s)S(s)ʈϱ , ͑ sϩ1 ͒ 2where W ( s ) is a weighting function. W ( s ) should Thenbe selected such that the 2-norm boundary of thesystem input is normalized by unity. That is, ͑ sϩ1 ͒͑ 1ϩ s/2͒W ( s ) ϭ1/s for a unit step setpoint. The perfor- Q ͑ s ͒ ϭQ im ͑ s ͒ J ͑ s ͒ ϭ . K ͑ sϩ1 ͒ 2mance index implies that the controller is designed ͑11͒to minimize the worst error resulting from system ´inputs. With Pade approximation, we have As tends to be zero, the controller tends to be optimal. The corresponding controller of the unity 1Ϫ s/2 feedback loop is G ͑ s ͒ ϭK , ͑7͒ ͑ sϩ1 ͒͑ 1ϩ s/2͒ 1 ͑ sϩ1 ͒͑ 1ϩ s/2͒ C͑ s ͒ϭ . ͑12͒which will be regarded as the nominal plant uti- Ks 2 sϩ2ϩ /2lized to derive the H ϱ PID controller. The errorintroduced by the approximation is included in un- The suboptimal H ϱ PID controller is derived ana-certainty, which will be discussed later. lytically. Comparing the above controller with the Instead of a numerical method, an analytical de- following practical PID controller,sign procedure is developed for the given plant. Itis seen that W ( s ) S ( s ) is analytical in the openright half plane. According to the well-known ͩ C ͑ s ͒ ϭK C 1ϩ 1 T Is ϩT D s ͪ 1 T F sϩ1 . ͑13͒maximum modulus theorem, a fundamental fact The parameters of the new PID controller areconcerning complex functions, ͉ W ( s ) S ( s ) ͉ doesnot attain its maximum value at an interior point 2 T Fϭ , T Iϭ ϩ , T Dϭ ,of the open right half plane. On the other hand, the 2ϩ /2 2 2T IG ( s ) has a zero at sϭ2/ in the open right halfplane. Thus, for all Q ( s ) ’s, TI K Cϭ . K ͑ 2ϩ /2͒ ʈ W ͑ s ͒͑ 1ϪG ͑ s ͒ Q ͑ s ͒͒ ʈ ϱ у ͉ W ͑ 2/ ͒ ͉ . ͑8͒ If the practical PID controller is in the form of ͩ ͪConsequently we have 1 T D sϩ1minʈ W ͑ s ͒ S ͑ s ͒ ʈ ϱ ϭminʈ W ͑ s ͒͑ 1ϪG ͑ s ͒ Q ͑ s ͒͒ ʈ ϱ C ͑ s ͒ ϭK C 1ϩ , ͑14͒ T I s T F sϩ1 ϭ /2. ͑9͒ the parameters of the PID controller areHowever, W ( s ) has a pole on the imaginary axis. 2 To obtain a ﬁnite inﬁnity norm, a constraint will T Fϭ , T Iϭ , T Dϭ , 2ϩ /2 2be imposed on the design procedure: lim ͑ 1ϪG ͑ s ͒ Q ͑ s ͒͒ ϭ0. ͑10͒ TI K Cϭ . s→0 K ͑ 2ϩ /2͒
514 Weidong Zhang et al. / ISA Transactions 41 (2002) 511–520If the practical PID controller is in the form of T Fϭ T Iϭ ϩ , T Dϭ ͩ ͪ , , 1 T Ds 2 ͑ ϩ ͒ 2 2T I C ͑ s ͒ ϭK C 1ϩ ϩ , ͑15͒ T I s T F sϩ1 TI K Cϭ .the parameters of the PID controller are K ͑ ϩ ͒ 2 T Fϭ , T I ϭ ϩ ϪT F , Let C ( s ) ϭ ( sϩ1 ) / ( sϩ1Ϫe Ϫ s ) ϭ f ( s ) /s. 2ϩ /2 2 The controller can be expanded in a Maclaurin series as TI ͫ ͬ T Dϭ ϪT F , K Cϭ . 2T I K ͑ 2ϩ /2͒ 1 f Љ͑ 0 ͒ 2 C͑ s ͒ϭ f ͑ 0 ͒ ϩ f Ј ͑ 0 ͒ sϩ s ϩ¯ .Since Eq. ͑13͒ is used in many papers, for com- s 2!parison, only this form is considered in the later ͑18͒part of this paper. Taking the ﬁrst three terms, we obtain the Maclau- If a conventional PID controller is installed, the rin PID controller, of which the parameters are ͓9͔parameter T F has been determined. Usually T Fϭ0.1T D ͓4͔. In this case, one can also use the 2above rules by simply omitting the new T F . Then, T F ϭ0, T Iϭ ϩ,similar results will be obtained. 2 ͑ ϩ ͒ ͫ ͬ Both the H 2 PID controller and Maclaurin PIDcontroller are based on the result of IMC. In IMC, 2 TI T Dϭ 1Ϫ , K Cϭ .the plant is factored as 2 ͑ ϩ ͒ 3T I K ͑ ϩ ͒ G ͑ s ͒ ϭG ϩ ͑ s ͒ G Ϫ ͑ s ͒ , Sometimes, one may use a second-order model.where G ϩ ( s ) contains all nonminimum phase fac- The above methods can be directly extended totors, G ϩ ( 0 ) ϭ1, and G Ϫ ( s ) is the minimum phase this case. Since the design procedure is almost theportion of the model. The IMC controller is then same, only the results are given here. Suppose thatgiven by the following formula ͓8͔: the model is 1 Ke Ϫ s Q͑ s ͒ϭ , G͑ s ͒ϭ . ͑19͒ ͑ sϩ1 ͒ n G Ϫ ͑ s ͒ ͑ 1 sϩ1 ͒͑ 2 sϩ1 ͒where n is chosen so that the controller is bi- Utilizing the Taylor series, the PID controller pa-proper, that is, both Q ( s ) and 1/Q ( s ) are proper. rameters designed by the proposed method areFor the given plant we have 2 1 2 sϩ1 T Fϭ , T Iϭ 1ϩ 2 , T Dϭ , C͑ s ͒ϭ , ͑16͒ 2ϩ TI sϩ1Ϫe Ϫ sSince a time delay is included in the controller, TI K Cϭ . ͑20͒it cannot be directly implemented. Many methods K ͑ 2ϩ ͒have been presented to approximate the control-ler by a rational transfer function, such as a nu- The H 2 PID controller parameters are ´merical method, Pade approximation, Taylor se-ries, Maclaurin series, and so on ͓11͔. The H 2 sub- 1 2optimal PID controller given by ͓7͔ utilizes a Pade´ T Fϭ , T Iϭ , ϩ2 1ϩ 2approximation. The resultant controller is 1 ͑ sϩ1 ͒͑ 1ϩ s/2͒ TI C͑ s ͒ϭ ͑17͒ T Dϭ 1ϩ 2 , K Cϭ . ͑21͒ Ks s/2ϩϩ K ͑ ϩ2 ͒and the PID controller parameters are The Maclaurin PID controller parameters are
Weidong Zhang et al. / ISA Transactions 41 (2002) 511–520 515 2 2 Ϫ 2 and T F ϭ0, T Iϭ 1ϩ 2Ϫ , 2 ͑ 2ϩ ͒ 1Ϫ0.5s T͑ s ͒ϭ . 2 2 Ϫ 2 1 2 Ϫ 3 / ͑ 12ϩ6 ͒ ͑ sϩ1 ͒͑ 1ϩ0.5s ͒ T D ϭϪ ϩ , 2 ͑ 2ϩ ͒ TI Only one pole of the nominal plant is canceled. Note that there is an adjustable parameter in TI the three PID controllers. It has been shown by K Cϭ . ͑22͒ K ͑ 2ϩ ͒ some researchers that relates directly to the nominal performance and robustness of the sys-3. Discussion tem. This paper will show that the quantitative time-domain response and frequency-domain re- All three PID controllers are suboptimal. The sponse can be gained by adjusting .design procedure shows that they relate closely Consider the H ϱ PID controller again. We re-and each has its own features. The difference be- gard the error introduced by the approximation astween the Maclaurin PID controller and the H 2 uncertainty. The actual plant is in the form of aand H ϱ PID controllers is obvious. The difference ﬁrst-order plus time delay. Thenbetween the H 2 PID controller and the H ϱ PID ͑ 1ϩ s/2͒ e Ϫ scontroller lies in the manner they cancel the poles T͑ s ͒ϭof the plant. The proposed H ϱ controller tends to 2 s 2 ϩ ͑ 2ϩ /2͒ sϩ ͑ 1ϩ s/2͒ e Ϫ scancel all poles of the process while the H 2 con- ͑23͒troller tends to cancel the poles of the minimum andphase part of the process. 2 s 2 ϩ ͑ 2ϩ /2͒ s S͑ s ͒ϭ .3.1. Example 1 2 s 2 ϩ ͑ 2ϩ /2͒ sϩ ͑ 1ϩ s/2͒ e Ϫ s ͑24͒ The difference between the H 2 PID controllerand the H ϱ PID controller is illustrated in this ex- It is seen that T ( s ) does not depend on K and .ample. Consider the plant described by This implies that the setpoint response for the closed-loop system relates only to and . The e Ϫs similar result also exists for the H 2 PID controller. G͑ s ͒ϭ . Strictly speaking, the setpoint response of the sys- sϩ1 tem with the Maclaurin PID controller relates to ´The nominal plant with Pade approximation is not only and but . Since the difference is very 1Ϫ0.5s G͑ s ͒ϭ . ͑ sϩ1 ͒͑ 1ϩ0.5s ͒The H ϱ method yields that ͑ sϩ1 ͒͑ 1ϩ0.5s ͒ Q͑ s ͒ϭ ͑ sϩ1 ͒ 2and the nominal complementary sensitivity trans-fer function is 1Ϫ0.5s T͑ s ͒ϭ . ͑ sϩ1 ͒ 2We see that both of the two poles of the nominalplant are canceled. For the H 2 controller we have sϩ1 Fig. 3. Responses of system A. ͑Solid line: H ϱ ; dashed Q͑ s ͒ϭ sϩ1 line: Maclaurin; dotted line: H 2 .͒
516 Weidong Zhang et al. / ISA Transactions 41 (2002) 511–520Fig. 4. Responses of system B. ͑Solid line: H ϱ ; dashed Fig. 6. Quantitative rise time. ͑Solid line: H ϱ ; dashed line:line: Maclaurin; dotted line: H 2 .͒ Maclaurin; dotted line: H 2 .͒small, it can be considered that its setpoint re-sponse relates only to and . overshoot. The resultant parameters are ϭ0.41 for the H 2 controller, ϭ0.46 for the H ϱ PID3.2. Example 2 controller, and ϭ0.42 for the Maclaurin PID controller. The closed-loop responses are shown in The three suboptimal PID controllers are com- Fig. 3 and Fig. 4. For the two plants, the H 2 con-pared in this example. Consider the following two troller and the H ϱ PID controller have almost theplants: same response. For the plant with a small ratio of and , A, the H 2 controller and the H ϱ PID e Ϫ0.5s e Ϫ5s controller have faster rise times and settling times A: G ͑ s ͒ ϭ , B: G ͑ s ͒ ϭ . sϩ1 sϩ1 than that of the Maclaurin PID controller, and for the plant with a large ratio of and , B, the H 2A unit step setpoint is added at tϭ0s and a unit controller and the H ϱ PID controller have a slowerstep load disturbance at tϭ6s or 60s. The param- settling time and disturbance rejection.eter in all the three controllers is adjustable. To It is found that the response shapes of the H 2obtain a fair comparison, the parameter is adjusted controller and the H ϱ PID controller for the twosuch that the closed-loop responses have the same plants are almost the same. This validates theFig. 5. Quantitative overshoot. ͑Solid line: H ϱ ; dashed Fig. 7. Quantitative ISE. ͑Solid line: H ϱ ; dashed line:line: Maclaurin; dotted line: H 2 .͒ Maclaurin; dotted line: H 2 .͒
Weidong Zhang et al. / ISA Transactions 41 (2002) 511–520 517above analysis. We deﬁne as the ‘‘performance that the ratio of ISE and is also determined bydegree.’’ It is seen that the overshoot and the ratio the performance degree ͑Fig. 7͒. This implies thatof rise time and are determined only by the ratio the relationship between the classical performanceof the performance degree and ͑Figs. 5 and 6͒. indices and the optimal performance indices is es-The break in Fig. 6 is caused by the difference of tablished.the deﬁnition of the rise time for systems with In frequency-domain analysis, one importantovershoot and without overshoot. The empirical concept is the resonance peak, that is, the maxi-formulas for estimating the two indices can also be mum modulus of the closed-loop transfer function.given. For example, the formulas of the H ϱ PID In ͓14͔, it is referred to as the maximum logcontroller are as follows: modulus and utilized to design PID controller. A commonly used speciﬁcation for it is 2dB. The Overshoot ͭ quantitative relationship between the resonance Ϫ0.86͑ / ͒ 3 ϩ14.21͑ / ͒ 2 peak and the performance degree is shown in Fig. 8. ϭ Ϫ8.72/ ϩ1.86, 0.1р/ р0.59 Another important concept in frequency domain 0, 0.59р/ р1.2, is stability margin, on which numerous methods are based ͑see, for example, ͓15͔͒. The magnitude ͑25͒ stability margin and phase stability margin provide Rise time intuitive tools for control system design and are ͭ very familiar to engineers. The quantitative rela- 30.34͑ / ͒ 3 Ϫ24.63͑ / ͒ 2 tionship between the stability margin and the per- formance degree is shown in Figs. 9 and 10. ϭ ϩ8.48/ Ϫ0.45, 0.1р/ р0.59 Recently, ͓16͔ proposed a new frequency- 3.97/ Ϫ1.02, 0.59р/ р1.2. domain index for PID controller design, ͑26͒ 1/max͉ Re͓ G ͑ j ͒ C ͑ j ͔͒ ͉ ,In ͓12,13͔, nonovershoot and monotone nonde- which involves both the magnitude stability mar-creasing response is studied. For the three control- gin and phase stability margin to a certain extent.lers it is very easy to get such a response. It is found that there also exists the quantitative Both the H 2 control and the H ϱ control relate to relationship between the new index and the perfor-the integral square error ͑ISE͒. The H 2 control mance degree ͑Fig. 11͒.minimizes the ISE for a particular input and the Unfortunately, the simple quantitative relation-H ϱ control minimizes the worst ISE resulting ship comes into existence only for setpoint re-from any two-norm bounded inputs. It is found sponse. The transfer function from the load distur-Fig. 8. Quantitative resonance peak. ͑Solid line: H ϱ ; Fig. 9. Quantitative magnitude margin. ͑Solid line: H ϱ ;dashed line: Maclaurin; dotted line: H 2 .͒ dashed line: Maclaurin; dotted line: H 2 .͒
518 Weidong Zhang et al. / ISA Transactions 41 (2002) 511–520 Fig. 12. Quantitative perturbance peak.Fig. 10. Quantitative phase margin. ͑Solid line: H ϱ ;dashed line: Maclaurin; dotted line: H 2 .͒ where ⌬ ( ) is the uncertainty proﬁle. Let S ( s ) and T ( s ) be the sensitivity transfer function and the complementary sensitivity transfer function ofbance to the system output is G ( s ) S ( s ) . Thus, the the approximated system, respectively. The robustdisturbance response relates not only to and , stability is met if and only ifbut K and . We deﬁne the perturbance peak to bethe maximum output caused by the disturbance ʈ ⌬ ͑ ͒ T ͑ j ͒ ʈ ϱ Ͻ1, ᭙. ͑28͓͒10͔. For the H ϱ controller the relationship isshown in Fig. 12. This implies that arbitrary robust stability can be For the second-order model, the closed-loop re- gained by increasing the performance degree .sponse relates both 1 and 2 . If one of them is Assume that the performance is ʈ W ( s ) S ( s ) ʈ ϱdetermined, a similar quantitative relationship also Ͻ5 . Then, the robust performance is met if andexists, but not so clearly. only if There always exists uncertainty in practice. Sup- ʈ ͉ W ͑ j ͒ S ͑ j ͒ /5 ͉ ϩ ͉ ⌬ ͑ ͒ T ͑ j ͒ ͉ ʈ ϱ Ͻ1, ᭙.pose that the norm-bounded uncertainty is de- ͑29͒scribed by ͯ ͯ The quantitative uncertainty proﬁle that guaran- G ͑ s ͒ ϪG ͑ s ͒ ˜ tees robust performance for an H ϱ PID controller ϭ ͉ ⌬ m ͑ j ͒ ͉ р⌬ ͑ ͒ , ͑27͒ is shown in Fig. 13. G͑ s ͒ In classical control theory, the controller is usu- ally designed for the nominal performance speci- ﬁcation and used for the control of the uncertain plant. If a good model can be obtained, the esti-Fig. 11. Quantitative new index. ͑Solid line: Hϱ ; dashedline: Maclaurin; dotted line: H2 .͒ Fig. 13. Quantitative uncertainty proﬁle.
Weidong Zhang et al. / ISA Transactions 41 (2002) 511–520 519mates given by the above study are in good agree- the H ϱ PID controller, the relationship is exact,ment with the actual time-domain responses. If the and for the Maclaurin PID controller, the relation-uncertainty scope is large, one may hope to know ship roughly exists. Thus, all three controllers canwhether the required performance is obtained or be designed for desired time-domain response ornot. Eqs. ͑28͒ and ͑29͒ give a perfect estimate. frequency-domain response. The given quantita-However, they are more mathematical than practi- tive relationship between the classical perfor-cal and very inconvenient. As a matter of fact, we mance indices and the optimal performance indi-usually wish to estimate the ‘‘worst case’’ re- ces makes it possible to build a bridge betweensponse ͑i.e., the gain and time delay are at their the classical design method and modern designupper limits, while the time constant is at its lower methods.limit͒ or the range of the closed-loop response. For practical purposes, the H 2 PID controller ͓17͔ recently developed a method for designing may be the most convenient one, because it has aIMC systems with parametric uncertainty. First, a linear relationship with many performance indices.speciﬁed value M p is chosen based on the maxi- Since there always exists uncertainty in practice,mum desired overshoot, or the ‘‘worst case’’ over- the quantitative design for the system with para-shoot. Second, the magnitude of the complemen- metric uncertainty is also discussed, and a newtary sensitivity transfer function is designed to be design method is proposed to estimate the ‘‘worstequal to or less than M p. For example, for the case’’ response or the range of the closed-loop re-‘‘worst case’’ overshoot 10%, M pϭ1.05. By solv- sponse of the proposed controller.ing ͉ T ( j ) ͉ рM p, a unique solution on can be It is also shown that the suboptimal PID gives aobtained. satisfactory approximation to the exact time delay Here, an alternative method is proposed to esti- compensated scheme. This implies that the PIDmate the ‘‘worst case’’ response or the range of the controller can provide relatively good response forclosed-loop response of the proposed controller. systems with time delay, even when the time delayAssume that we hope to achieve the ‘‘worst case’’ is very large.overshoot of 5%. The new procedure is as follows: ͑i͒ Design the controller for the nominal Acknowledgments plant. For 5% overshoot, ϭ0.5. ͑ii͒ Substitute the nominal plant by the This project was supported by the National ϩ ‘‘worst case’’ plant K ϩ e Ϫ s / ( Ϫ sϩ1 ) . Natural Science Foundation of China ͑Grant ͑iii͒ Increase the performance degree No. 69804007͒ and the National Key Technologies monotonically until the overshoot is R&D Program in the Tenth Five-Year Plan ͑Grant equal to 5%. No. 2001BA201A04͒. For system with time delay, the typical step in References͑iii͒ is 0.01, and for systm without time delay, thetypical step in ͑iii͒ is 0.01. This procedure can ͓1͔ Ziegler, J. G. and Nichols, N. B., Optimum settings for automatic controllers. Trans. ASME 64, 759 ͑1942͒.easily be performed in Matlab with SIMULINK. ͓2͔ Cohen, G. H. and Coon, G. A., Theoretical consider- ations of retarded control. Trans. ASME 75, 827 ͑1953͒.4. Conclusions ͓3͔ Lopez, A. M., Smith, C. L., and Murrill, P. W., Con- troller tuning relationships based on integral perfor- In this paper, an efﬁcient H ϱ optimal method for mance criteria. Instrum. Technol. 14, 57 ͑1967͒.designing a PID controller was developed. Ex- ͓4͔ Astrom, K. J. and Hagglund, T., PID Controllers: theory, design, and tuning, 2nd ed. ISA, NC, 1995.plicit formulas are derived analytically. The result ͓5͔ Sung, S. W., Lee, J., and Lee, I., A new tuning rule andis similar to the H 2 PID controller, but different in modiﬁed PID controller. Ind. Eng. Chem. Res. 34,the manner of canceling the poles of the plant. 4127 ͑1995͒. The most important contribution of this paper is ͓6͔ Astrom, K. J., Panagopoulos, H., and Hagglund, T.,that the concept ‘‘performance degree’’ is deﬁned Design of PI controllers based on nonconvex optimi- zation. Automatica 34 ͑5͒, 585 ͑1998͒.and the quantitative relationship between the ͓7͔ Rivera, D. E., Morari, M., and Skogestad, S., Internalclosed-loop response and the performance degree model control: 4-PID controller design. Ind. Eng.is given. In systems with the H 2 PID controller or Chem. Res. 25, 252 ͑1986͒.
520 Weidong Zhang et al. / ISA Transactions 41 (2002) 511–520 ͓8͔ Morari, M. and Zaﬁriou E., Robust Process Control. Prentice-Hall, Englewood Cliffs, NJ, 1989. Yugeng Xi was born in the People’s Republic of China in ͓9͔ Lee, Y., Park, S., Lee, M., and Brosilow, C., PID con- 1946. He graduated from troller tuning for desired closed loop response for Haerbin Engineering College SISO systems. AIChE J. 44 ͑1͒, 106 ͑1998͒. in 1968. From 1979 to 1984 he was a visiting scholar at the In-͓10͔ Zhang, W. D., Xu, X. M., and Sun, Y. X., Quantitative stitute of Control Technology, performance design for integrating processes with Technical University Munich, time delay. Automatica 35 ͑4͒, 719 ͑1999͒. Germany, where he received͓11͔ Zhang, W. D., Robust control of systems with time the Dr.-Ing. degree in 1984. Since then, he has been work- delay, Ph.D thesis, Zhejiang University, 1996. ing at Shanghai Jiaotong Uni-͓12͔ Lin, S. K. and Fang, C. J., Nonovershoot and mono- versity. He received awards for tone nondecreasing step response of a third order Achievement in Science and SISO linear system. IEEE Trans. Autom. Control 42 Technology six times by the State Commission of Education and the Shanghai government. Prof. Xi was the vice chair of the IFAC TC ͑9͒, 1299 ͑1997͒. Large Scale Systems and is now the vice president of the Chinese͓13͔ Dan-Isa, A. and Atherton, D. P., Time-domain method Association of Automation. His current research interests include pre- for the design of optimal linear controllers. IEE Proc.: dictive control, robust control, robot control, and computer vision. Control Theory Appl. 144, 287 ͑1997͒.͓14͔ Luyben, W. L., Simple method for tuning SISO con- trollers in multivariable systems. Ind. Eng. Chem. Pro- Genke Yang was born in the People’s Republic of China in cess Des. Dev. 25, 654 ͑1986͒. 1963. He received his B.Sc.͓15͔ Ho, W. K., Lim, K. W., Hang, C. C., and Ni, L. Y., and M.Sc. degrees in math- Getting more phase margin and performance out of ematics, and Ph.D. degree in systems engineering. He is cur- PID controllers. Automatica 35, 1579 ͑1999͒. rently an associate professor in͓16͔ Wang, Y. G. and Shao, H. H., Optimal tuning for PI the department of automation, controllers. Automatica 36 ͑1͒, 147 ͑2000͒. Shanghai Jiaotong University.͓17͔ Stryczek, K., Laiseca, M., Brosilow, C., and Leitman, He is the author of 40 papers. His current research interests M., Tuning and design of single-input, single-output include modeling, analysis, control systems for parametric uncertainty. AIChE J. and control theory of discrete 46 ͑8͒, 1616 ͑2000͒. event dynamic systems and hy- brid dynamic systems. Xiaoming Xu was born in the People’s Republic of China in Weidong Zhang was born in 1957. He received the BS de- the People’s Republic of China gree from Huazhong Univer- in 1967. He received the BS, sity of Science and Technology MS, and Ph.D degree from in 1982, and the MS and Ph.D Zhejiang University in 1990, degrees from Shanghai Jiao- 1993, and 1996, respectively. tong University in 1984 and He worked in National Key 1987, respectively. From 1988 Laboratory of Industrial Con- to 1990 he worked in Germany trol Technology as a postdoc- as an Alexander von Humboldt toral research fellow before research fellow. He joined joining Shanghai Jiaotong Uni- Shanghai Jiaotong University versity in 1998 as an associate in 1990 as a professor in the professor. Since 1999 he has department of automation. Since 1997 he has been the deputy princi- been a professor at the Depart- pal of Shanghai Jiaotong University. He is the head of numerous statement of Automation, Shanghai Jiaotong University. He is the author of research projects and the author of numerous papers. His current re-68 papers. His current research interests include process control, ro- search interests include control theory, artiﬁcial intelligence, computerbust control, ﬁeld-bus, and digital signal processing. networking, and digital signal processing.