Proceedings of the 20th World Congress Proceedings of 20th The International Federation of Congress Automatic Control Proceedings of the the 20th World World Congress Proceedings of the 20th World Congress Control The of Toulouse, France,Federation July 9-14, 2017 The International International Federation of Automatic Automatic Control Available online at www.sciencedirect.com The International Federation of Automatic Control Toulouse, Toulouse, France, France, July July 9-14, 9-14, 2017 2017 Toulouse, France, July 9-14, 2017 ScienceDirect IFAC PapersOnLine 50-1 (2017) 1316–1321 Delayed Model Approximation and Control Design Delayed Model Approximation and Control Design Delayed Model Approximation Systems and Control Design for Under-Damped for Under-Damped Systems for Under-Damped Systems ∗ ∗∗ David F. Novella-Rodrı́guez ∗ Basilio del Muro-Cuéllar ∗∗ ∗ Basilio ∗∗ ∗∗ ∗∗ David F. Novella-Rodrı́guez David Novella-Rodrı́guez del Muro-Cuéllar German Hernández-Hernández Juandel F. Muro-Cuéllar Márquez-Rubio ∗∗ David F. F. Novella-Rodrı́guez ∗ Basilio Basilio Muro-Cuéllar ∗∗ ∗∗ ∗∗ Juandel ∗∗ German Hernández-Hernández F. Márquez-Rubio German Hernández-Hernández ∗∗ Juan F. Márquez-Rubio ∗∗ German Hernández-Hernández Juan F. Márquez-Rubio ∗ Instituto Tecnológico de Estudios Superiores de Monterrey, ITESM Campus ∗ ∗ Instituto Tecnológico de Estudios Superiores de Monterrey, ITESM Campus Estudios Superiores de Ciudad dede México. (email: dnovellar@itesm.mx) ∗ Instituto Tecnológico Instituto Tecnológico Estudios Superiores de Monterrey, Monterrey, ITESM ITESM Campus Campus ∗∗ Ciudad de dede México. (email: dnovellar@itesm.mx) Ciudad México. (email: dnovellar@itesm.mx) Instituto Politécnico Nacional, IPN, ESIME Culhuacan, Santa Ana 1000, Ciudad de México. (email: dnovellar@itesm.mx) ∗∗ ∗∗ Instituto Politécnico Nacional, IPN, ESIME Culhuacan, Santa Ana 1000, Nacional, IPN, Santa C.P. 44300, Politécnico México, (email: bdelmuro@ipn.mx, german.h.team@gmail.com, ∗∗ Instituto Instituto Politécnico Nacional, IPN, ESIME ESIME Culhuacan, Culhuacan, Santa Ana Ana 1000, 1000, C.P. 44300, México, (email: bdelmuro@ipn.mx, german.h.team@gmail.com, C.P. 44300, México, (email: bdelmuro@ipn.mx, german.h.team@gmail.com, jfcomr23@yahoo.com.mx C.P. 44300, México, (email: bdelmuro@ipn.mx, german.h.team@gmail.com, jfcomr23@yahoo.com.mx jfcomr23@yahoo.com.mx jfcomr23@yahoo.com.mx Abstract: The presented paper introduces a simple methodology to obtain a reduced-order approximaAbstract: The presented simple methodology to obtain aa reduced-order approximaAbstract: The paper introduces simple to approximation to high-order systemspaper with introduces oscillatingaaadynamics. The reduction is based on the frequency domain Abstract: The presented presented paper introduces simple methodology methodology to obtain obtain a reduced-order reduced-order approximation to high-order systems with oscillating dynamics. The reduction is based on the frequency domain tion to high-order systems with oscillating dynamics. The reduction is based on the frequency domain characteristics of the under-damped high-order process. Classic control theory tools are used in order tion to high-order systems with oscillating dynamics. The reduction is based on the frequency domain characteristics of the under-damped high-order process. Classic control theory tools are used in order characteristics of the under-damped high-order process. Classic control theory tools are used in order to obtain an accurate model reduction. Time-delay term plays an important role at the low-order model characteristics of the model under-damped high-order process. Classic control theory tools are used inmodel order to obtain an accurate reduction. Time-delay term plays an important role at the low-order to obtain an accurate model reduction. Time-delay term plays an important role at the low-order model since it allows to hold the phase characteristics of the original system. Finally, a simple PID tuning to obtain an accurate model reduction. Time-delay term plays an important role at the low-order model since it allowshas to hold the phaseincharacteristics the original system. Finally, aa simple PID tuning since it to hold the of the original Finally, PID methodology order to obtainof gains from the reduced Some since it allows allowshas to been hold proposed the phase phaseincharacteristics characteristics ofthe thecontroller original system. system. Finally, a simple simplemodel. PID tuning tuning methodology been proposed order to obtain the controller gains from the reduced model. Some methodology has been proposed in order to obtain the controller gains from the reduced model. Some numerical simulations are presented obtaining a similar performance when the PID controller applied to methodology has beenare proposed in order to obtain the controller gains from the reduced model. Some numerical simulations presented obtaining a similar performance when the PID controller applied to numerical simulations are presented obtaining a similar performance when the PID controller applied the high-order plant. numerical simulations are presented obtaining a similar performance when the PID controller applied to to the high-order plant. the high-order plant. the high-order plant. © 2017, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved. Keywords: Time-invariant systems, Linear systems, Systems with time-delays, Process control, Keywords: Time-invariant Keywords: Time-invariant systems, Linear Linear systems, systems, Systems Systems with with time-delays, time-delays, Process Process control, control, N-dimensional systems systems, Keywords: Time-invariant N-dimensional systems systems, Linear systems, Systems with time-delays, Process control, N-dimensional systems N-dimensional systems 1. INTRODUCTION contrary of the half-rule presented in Skogestad (2001, 2003), 1. INTRODUCTION INTRODUCTION contrary of the the half-rule half-rule presented presented in Skogestad Skogestad (2001, 2003), 1. contrary of in 2003), the proposed given here is based on(2001, a frequency 1. INTRODUCTION contrary of themethodology half-rule presented in Skogestad (2001, 2003), the proposed methodology given here is based on a frequency the proposed methodology given here is based on a frequency analysis, taking intogiven account the characteristics above the proposed methodology here is based on a frequency Systems with under-damped characteristics appear in such domain domain analysis, analysis, taking into account account thecoefficients characteristics above domain taking into the characteristics above mentioned; natural frequency, damping and resoSystems with under-damped characteristics appear in such Systems with under-damped characteristics appear in such domain analysis, taking into account the characteristics above different areas. For instance mechanical systems, Gawronski mentioned; natural frequency, damping coefficients and resoSystems with under-damped characteristics appear in such mentioned; natural frequency, damping coefficients and resonance frequency. A time-delay term is added in order to condifferentnoise areas. For instancevibration, mechanical systems, Gawronski different areas. instance mechanical Gawronski naturalA frequency, damping coefficients and reso(2010), andFor structural Maosystems, and Pietrzko (2013) mentioned; nance frequency. time-delay term is added in order to condifferent areas. For instance mechanical systems, Gawronski nance frequency. A time-delay term is added in order to consider the phase lag due to the high order dynamics. The main (2010), noise and structural vibration, Mao and Pietrzko (2013) (2010), structural Mao and Pietrzko (2013) frequency. A due time-delay termorder is added in order to main conand An noise et al.and (2013), largevibration, civil structures, Spencer-Jr. et al. nance sider the phase lag to the high dynamics. The (2010), noise and structural vibration, Mao and Pietrzko (2013) sider the phase lag due to the high order dynamics. The main of the lag approximation is to order designdynamics. suitable controllers and An An Quinonero et al. al. (2013), (2013), large civilThenozhi structures, Spencer-Jr. et al. al. objective and et large civil structures, Spencer-Jr. et sider the phase due to the high The main (1998); et al. (2012); and Yu (2013). Flexobjective ofaccount the approximation approximation is to to design suitable controllers and An Quinonero et al. (2013), civilThenozhi structures, et al. objective the is controllers intoof the reduced order model suitable in order to simplify (1998); et al. al.large (2012); andSpencer-Jr. Yuthat (2013). Flex(1998); Quinonero et Thenozhi and (2013). objective the approximation is to design design suitable controllers ible structures in motion have specific features are Flexnot a taking taking intoofaccount account thePID reduced order model inconsidered order to to simplify (1998); Quinonero et al. (2012); (2012); Thenozhi and Yu Yuthat (2013). Flextaking into the reduced order model in order the controller tuning. controllers will be to give ible structures in motion have specific features are not a ible structures in motion specific that are not into account thePID reduced order model inconsidered order to simplify simplify secret to an engineer. Onehave of them is thefeatures resonance-strong am-aa taking the controller tuning. controllers will be to give give ible structures in motion have specific features that are not the controller tuning. PID controllers will be considered a perspective of the approximation method effectiveness. secret to an engineer. One of them is the resonance-strong amsecret to an One is the resonance-strong amthe controller of tuning. PID controllers will beeffectiveness. considered to to give plification ofengineer. the motion atof a them specific frequency, called natural a perspective the approximation method secret to an engineer. One of them is the resonance-strong ama perspective of the approximation method effectiveness. plification of of the motion motion at aaofspecific specific frequency, called called natural plification the at frequency, natural frequency. Another feature large mechanical structures is a perspective of the approximation method effectiveness. plification of the motion at aofspecific frequency, called natural frequency. Another feature large mechanical structures is 2. BACKGROUND frequency. Another feature of large mechanical structures is that they can be mathematically represented as systems with frequency. Another feature of large mechanical structures is 2. BACKGROUND BACKGROUND 2. that they they conjugated can be be mathematically mathematically represented as systems systemsmodal with that can represented as with complex poles. Their real parts (representing 2. BACKGROUND that they conjugated can be mathematically represented as systemsmodal with complex poles. Their real parts (representing complex conjugated poles. Their real parts (representing modal damping) are typically small, and their distance from the ori- In the literature, there are different works dealing with the probcomplex conjugated poles. Their real parts (representing modal In the theofliterature, literature, thereaare are different works dealing withathe the probdamping) are typically typically small, and their distance from the the ori- lem In there different with probcharacterize large order works systemdealing by means simpler damping) are small, and their distance from origin is the natural frequency of a structure, Gawronski (2010). In theofliterature, thereaare different works dealing withathe probdamping) are typically small, and their distance from the orilem characterize large order system by means simpler gin is is the the natural frequency of structure, Gawronski (2010). lem of characterize aa large order system by means simpler preserving the important features of theaa original gin natural frequency aaa structure, Then, a large mechanical or of civil structure Gawronski consists on (2010). several description, lem of characterize large order system by means simpler gin is the natural frequency of structure, Gawronski (2010). description, preserving themargins. important features of the the original Then, aa large large mechanical or civil civil structure structure consists ondifferent several systems, description, the important features of suchpreserving as stability There exist methods based Then, or consists several under-damped systems connected them or in aon description, preserving themargins. important features of the original original Then, a large mechanical mechanical or civil among structure consists ondifferent several systems, systems, such as stability There exist methods based under-damped systems connected among them or in a such as stability margins. There exist methods on truncated balanced realizations Moore (1981), Singularbased Valunder-damped systems connected among them or in a different perspective, a high-order system with n pairs of complex consystems, such as stability margins. There exist methods based under-damped systems connected among themoforcomplex in a different on truncated balanced realizations Moore (1981), Singular Valperspective, a high-order system with n pairs conon truncated balanced realizations Moore (1981), Singular Values decomposition, Krylov Antoulas and Sorensen (2001), freperspective, a high-order system with n pairs of complex conjugated and stable poles, representing a challenge for the design on truncated balanced realizations Moore (1981), Singular Valperspective, a high-order system with n pairs of complex conues decomposition, Krylov Antoulas and Sorensen (2001), frejugated andcontrol stable poles, poles, representing aa challenge challenge for for the the design design quency ues decomposition, Krylov Antoulas and Sorensen (2001), fredomain methods Sootla (2013), etc. All the mentioned jugated and stable representing of suitable laws. ues decomposition, Krylov Antoulas andetc. Sorensen (2001), frejugated andcontrol stable poles, representing a challenge for the design quency quency domain methods Sootla (2013), All the mentioned of suitable laws. domain methods Sootla (2013), etc. All the mentioned methods obtain amethods finite dimensional approximation. A different of suitable control laws. quency domain Sootla (2013), etc. All the mentioned of suitable control laws. In Skogestad (2001, 2003), first and second-order delayed ap- approach methods obtain obtain finite dimensional dimensional approximation. A different different methods finite approximation. A is the aaamethod developed by Skogestad (2003, 2001), methods obtain finite dimensional approximation. A different In Skogestad Skogestad (2001, (2001, 2003), first and second-order second-order delayed apIn and proximations of high2003), order first systems are proposed delayed in orderapto which approach is the the method method developed by Skogestad (2003, 2001), approach is developed by Skogestad (2003, 2001), approximate a high order system by a first or second In Skogestad (2001, 2003), first and second-order delayed apis the method developed by Skogestad (2003, 2001), proximations of high high order order systems are proposed in order order to approach proximations of systems are proposed in to derive PID controllers by means of a simple analytically prowhich approximate a high order system by a first or second which approximate aa high order system aa first or second system plus time-delay, in order to by simplify the design proximations of high order systems are proposed in order to order which approximate high order system by first or second derive PID controllers by means of a simple analytically proderive PID controllers by of analytically cedure, yielding to acceptable results in the closed loop proper- of order system plus time-delay, time-delay, in methodology order to to simplify the design design order plus in order the PIDsystem controllers, the empirical of Skogestad is derive controllers by means means of aa simple simple analytically order system plus time-delay, in methodology order to simplify simplify the design cedure,PID yielding to acceptable results insystem. the closed closed loop propercedure, yielding to acceptable results in the loop performance for the high-order closed loop However, this of PID controllers, the empirical of Skogestad is of PID controllers, the empirical methodology of Skogestad is based on the time response of the open-loop high order system cedure, yielding to acceptable results in the closed loop perof PID controllers, the empirical methodology of Skogestad is formance for for thebeen high-order closedforloop loop system. However, However, this based on the time response of the open-loop high order system formance the high-order closed system. this procedure have developed high-order systems conbased on the time response of the open-loop high order system and is briefly presented below. formance for the high-order closed loop system. However, this based on the time response of the open-loop high order system procedure have been developed for high-order systems conprocedure have been developed for systems sidering only simple poles. Therefore, the aforementioned and is is briefly presented presented below. procedure have beenreal developed for high-order high-order systems concon- and and is briefly briefly presented below. below. sidering only simple real poles. Therefore, the aforementioned aforementioned sidering only simple real poles. Therefore, the work does not consider the case of oscillating processes, i.e. sidering only simple real the poles. Therefore, the aforementioned 2.1 Half Rule Method work does not consider case of oscillating processes, i.e. work does not consider the case of oscillating processes, i.e. large mechanical systemsthe andcase civilof structures hasprocesses, not been con2.1 Half Half Rule Method Method work does not consider oscillating i.e. 2.1 large mechanical mechanical systems and and civil civil structures structures has not not been been concon2.1 Half Rule Rule Method large systems has sidered. large mechanical systems and civil structures has not been conThe half rule method approximates a high order system to sidered. sidered. The half rule rule method methodplus approximates highmethod order system system to sidered. half approximates high order to The aim of this work is to present a second order approxima- aThe first-order time delay. aaaThe states the half rulesystem methodplus approximates highmethod order system to The with aim of of this work work term is to to for present a second second order approximaapproximaaThe first-order system time delay. delay. The states the the The aim this is present a order a first-order system plus time The method states tion a time-delay high-order oscillating structures following: the largest neglected time constant is distributed to The aim of this work term is to for present a second order approximaafollowing: first-orderthesystem plus time delay. The method states the tion with a time-delay high-order oscillating structures largest neglected time constant is distributed to tion with aa time-delay term oscillating following: the largest time constant is to (typical on mechanical and civil engineering). On the the effective andneglected the smallest constant retained by tion withmodels time-delay term for for high-order high-order oscillating structures structures following: thedelay largest timetime constant is distributed distributed to (typical models on mechanical mechanical and civil civil engineering). engineering). On the the the the effective effective delay andneglected the smallest smallest time constant retained by by (typical models on and On delay and the time constant retained (typical models on mechanical and civil engineering). On the the effective delay and the smallest time constant retained by Copyright 1348Hosting by Elsevier Ltd. All rights reserved. 2405-8963 © © 2017 2017, IFAC IFAC (International Federation of Automatic Control) Copyright © 2017 1348 Copyright © under 2017 IFAC IFAC 1348Control. Peer review responsibility of International Federation of Automatic Copyright © 2017 IFAC 1348 10.1016/j.ifacol.2017.08.127 Proceedings of the 20th IFAC World Congress Toulouse, France, July 9-14, 2017 David F. Novella-Rodríguez et al. / IFAC PapersOnLine 50-1 (2017) 1316–1321 the approximated time constant, Skogestad (2003, 2001). Let the original system be: G(s) = j −Tjinv + 1 i (τi0 s + 1) e θ0 s , where the lags τi0 are ordered according to their magnitude, and inv Tj0 > 0 denote the inverse response time constants, (negative numerator of the original system). Then, according to the half rule method a first-order model can be obtained as: 1317 term. At this frequency, the resonance peak takes place, namely the maximum magnitude of the frequency domain analysis. The resonance peak is directly associated to the damping coefficient of an under-damped system. Finally, the approximation of a high order system by a reduced order systems have a significant difference in the phase characteristics. Every couple of complex conjugated poles adds a lag of 180◦ on the high order frequencies region. In order to compensate the mentioned phase difference, a time-delay is introduced to the approximation. Then, for the high-order system given by (1), it is proposed the second order process with time delay given by: −θs ˆ = e , G(s) τ1 s + 1 GLO (s) = where: (s2 K̄e−τ s , + 2ζωn s + ωn2 ) (2) Bode Diagram 50 i≥3 −50 −100 j Phase (deg) −180 θ r −360 −540 −2 10 −1 0 10 10 Frequency (rad/s) 1 10 2 10 Fig. 1. Oscillating system frequency analysis 3. APPROXIMATION FOR OSCILLATING SYSTEMS The aim of this work is to compute a descriptive approximation for the high order system: m K j=1 (s2 + 2ζj ωnj s + ωn2 j ) GHO (s) = n , 2 2 i=1 (s + 2ζi ωni s + ωni ) ωr −150 0 Is it worth to stress that the half rule approximation is based on an empirical method. Moreover, the approach has been developed for typical process control applications. Unstable processes have not been considered, with the exception of integrating processes. Oscillating processes (with complex poles or zeros) have also not been considered. Based on these three terms it is possible to find the parameters of the system approximation (2), namely the damping coefficient ζ, the natural frequency ωn and the time delay τ . Below, a procedure to compute the reduced model is provided. 4. REDUCTION PROCEDURE (1) where m < n, ζi is the damping coefficient and ωni is the natural frequency of the corresponding under damped subsystem. For this purpose, it is possible to consider the frequency response of the high order system. The chosen tool to perform the reduction model is the Bode plot of the high-order system. Taking into account that the analyzed systems have oscillating modes, there are three important characteristics of the frequency domain analysis to point out: • Resonance frequency ωr , • Resonance peak Mr , • Phase margin θr . Mr 0 Magnitude (dB) and τ1 = τ10 + τ20 inv θ = θ0 + τi0 + Tj0 . The movement of mechanical or civil structure depends on several factors like the amplitude and other features such as external perturbation motion, the dynamic properties of the structure, the characteristics of the materials of the structure and its foundation (basestructure interaction). A mechanical and/or civil structure will have multiple natural frequencies, which are equal to its number of Degree of Freedom (DOF), Thenozhi and Yu (2013). If the frequency of the ground motion is close to the natural frequency of the building, the resonance occurs. As a result, the floors may move rigorously in different directions causing inter-story drift, the relative translational displacement between two consecutive floors. For this reason, the reduced model should consider the resonance frequency as an important Taking into account the parameters of the high-order system, the procedure to compute the reduction parameters will be presented in this section. 4.1 Damping coefficient ζ From the Bode diagram of the high-order open-loop system it is possible to compute the resonance peak, namely, the maximum value of the magnitude plot in [dB]. Since the purpose of the paper is to deal with oscillatory systems, i.e. systems with complex conjugated poles, the magnitude Bode diagram always presents a resonance peak for some frequency ωr . Considering the frequency domain characteristics of an oscillatory system, it is possible to found the relation between the resonance peak of the high order system and the damping coefficient of a second order process with the following equation (or expressed graphically in Fig. 2): Mr = 4.2 Natural frequency ωn 2ζ 1 1 − ζ2 (3) As a second step it is possible to relate the resonant frequency of the high order system with the natural frequency of the reducedorder system by means of the following equation: 1349 Proceedings of the 20th IFAC World Congress 1318 Toulouse, France, July 9-14, 2017 David F. Novella-Rodríguez et al. / IFAC PapersOnLine 50-1 (2017) 1316–1321 4.5 Example 30 Let us consider a high order system with oscillating modes given by the following transfer function: Resonance peak Mr 25 20 GHO (s) = 15 (7) This system can be separate as four under-damped subsystems with the following parameters: 10 • • • • 5 0 0.1 0.2 0.3 0.4 0.5 Damping coefficient ζ 0.6 0.7 0.8 Fig. 2. Relation: Damping coefficient and resonance peak. ωr , ωn = 1 − 2ζ 2 (4) using the damping coefficient computed before for the second order model. As the damping ratio ζ approaches to zero, the natural frequency of the reduction approaches to the resonant frequency of the original system. 4.3 Proportional gain K̄ Once the parameters of the reduced model have been calculated, the value corresponding to the proportional gain should be determined taking into account the DC gains of the high order system and the reduced model, as follows: K̄ = K limω→0 GHO (jω) . limω→0 GLO (jω) (5) 4.4 Time delay τ Finally, a time delay is introduced to the reduced model in order to compensate the phase difference due to the model reduction. As a parameter of compensation, the gain margin of the high order system is considered, then, the crossover frequency ωgm is taking into account for this purpose (see Fig. 3 ), the difference between the phase of the reduced model φLO (ωgm ) and the phase original model φHO(ωgm ) at this frequency is related with the delay as follows: φHO − φLO τ= 57.3ωgm = 0.5 = 0.6 = 0.65 = 0.4 ζ1 ζ2 ζ3 ζ4 ωn1 = 1.1180, ωn2 = 2.2361, ωn3 = 3.1623, ωn4 = 1.4142, Then, we proceed to compute the model reduction, namely a second order under-damped system plus time delay. First, from the frequency domain of the high-order system we can obtain the resonance peak Mr = 3.66dB, which is equivalent to have a damping coefficient ζ = 0.3513 and this peak occurs at the resonance frequency ωr = 1.37rad/s. Taking into account the previous values, it is possible to obtain the natural frequency of the reduced model by means the equation (4), then, ωn = 1.0946. Finally a proportional constant of the system is given by K = 1.198. Then we have a delay free reduced model given by: G(s) = 1.198 . s2 + 0.7691s + 1.198 (8) Comparing the phase plot of the Bode diagram of the original system and the reduced model, the phase margin of the original systems takes place at a frequency ω = 1.38rad/s. At this frequency, the high order model has a phase of −284.5, and for the reduced model the phase at this frequency is −123.62. To compensate this pase difference it is necessary to introduce a time delay, using the equation (6) we compute a delay τ = 2.0428. In order to compare the frequency and time characteristics of the original high-order systems and the model reduction, Fig. 4 shows the Bode diagram of both systems and Fig. 5 shows the time response to a step input and a sinusoidal input respectively. Bode Diagram Gm = -3.64 dB (at 0.953 rad/s) , Pm = -97.7 deg (at 1.34 rad/s) 10 0 Magnitude (dB) 0 125 s 8 + a 7 s 7 + a 6 s 6 + a 5 s 5 + a 4 s4 + a 3 s 3 + a 2 s 2 + a 1 s + a 0 (6) -10 -20 -30 High-Order System Second order plus time-delay system -40 0 -90 Phase (deg) whit the phase given in degrees. -180 -270 -360 10 -1 High-order system Reduced model 25 20 Magnitude (dB) 10 0 Frequency (rad/s) 30 Fig. 4. Frequency response: delayed system 15 10 5 0 -5 5. PID CONTROLLER TUNING -10 0 Phase (deg) -90 φ LO -180 Phase to be compensated -270 φ HO -360 3 4 Fig. 3. Phase compensation 5 Frequency (rad/s) 6 7 8 9 10 11 12 13 Once a model reduction is obtained, a procedure to obtain the parameters of a PID controller is proposed. Following the methodology proposed by Skogestad Skogestad (2003); Skogestad and Grimholt (2012), an Internal Model Control approach is used to derive the controller gains based on the reduced model. Let us consider the closed loop system given in Fig. 6. 1350 Proceedings of the 20th IFAC World Congress Toulouse, France, July 9-14, 2017 David F. Novella-Rodríguez et al. / IFAC PapersOnLine 50-1 (2017) 1316–1321 2 ωnd , K̄ ω2 KP = nd 2ζωn , K̄ 2 ωnd KI = ω2 , K̄ n KD = Step Response Amplitude 1.5 1 0.5 High-Order System Second Order System plus Time-Delay 0 0 2 4 6 1319 8 10 12 14 16 Time (seconds) Linear Simulation Results 2 (15) Amplitude 1 and the first-order filter has a pole located at s = −(2ζd ωnd + τ ). 0 -1 -2 0 2 4 6 8 10 12 14 16 18 20 Time (seconds) 5.1 Example Fig. 5. Time response The PID controller is given for the following equation: CP ID (s) = KP + KI + KD s. s (9) The closed loop transfer function is: Y (s) GLO (s)C(s) = . R(s) 1 + GLO (s)C(s) (10) Let us consider the low-order retarded approximation computed in Section 4.5, given by the following transfer function: 1.198 e−2.0428s , G(s) = 2 s + 0.7691s + 1.198 the controller parameters are computed following the procedure given in the previous section. An important issue on this work, is to compare the response of the closed loop approximated reduced system and the original high order system, both controlled by means of the PID tuning methodology presented previously. Fig. 7 provides such comparison. The continuous line shows the closed loop performance of the PID controller used for the original system. The dashed line indicates the closed loop response of the low order approximation. 2.5 Original system Reduced system 2 Fig. 6. Closed Loop System In order to obtain the parameters of the controller, it follows to specify a desired closed loop response, for this purpose, we consider a second order desired response: Y (s) R(s) d 1.5 1 2 ωnd −τ s = 2 , 2 e s + 2ζd ωnd s + ωnd (11) 0.5 note that the delay term is considered in the desired closed loop response due to it is not a negligible factor. Then, considering the closed loop system (10), and solving for the controller C(s) we have: C(s) = 1 GLO (s) R(s) Y (s) 1 (12) , d −1 0 0 10 20 30 40 50 Fig. 7. Controller performance comparison 5.2 Application Example Substituting (2) and (11) into (12), we obtain the following infinite dimensional expression: 2 Let consider the mechanical systems shown in Fig. 8, with a transfer function relating the input force F (s) and the position X2 (s) corresponding to the mass m2 given by: To obtain the controller parameters it is possible to consider a first-order Taylor series approximation of the delay term in equation (13), giving the following expression: b1 s + k 1 X2 (s) = , F (s) m1 m2 s4 + αs3 + βs2 + (b1 k1 + b2 k1 )s + k1 k2 (16) C(s) = 2 s2 + 2ζωn + ωn K̄ C(s) = ωnd 2 − ω 2 e−τ s s2 + 2ζd ωnd s + ωnd nd 2 s2 + 2ζωn + ωn sK̄ 2 ωnd s + 2ζd ωnd + τ , . (13) (14) it can be noted that C(s) consists in a PID in series with a firstorder filter, the PID controller gains are computed as follows: with α = m1 (b1 + b2 ) + m2 b1 and β = m1 k1 + m2 (k1 + k2 ) + b2 b1 . The system parameters are: m1 = m2 = 1kg, k1 = k2 = 1N/m, b1 = 0.65N s/m and b2 = 1N s/m, the example is analyzed in Nechak et al. (2015) with a different reduction method. Applying the method described previously, 1351 Proceedings of the 20th IFAC World Congress 1320 Toulouse, France, July 9-14, 2017 David F. Novella-Rodríguez et al. / IFAC PapersOnLine 50-1 (2017) 1316–1321 a second order approximation is computed, obtaining the following result: 5.3 Disturbance Rejection Disturbance rejection is one of the main issues of concern in mechanical and civil engineering. As it can be noted in Fig. 7, the proposed PID control structure is not able to deal with this problem. In order to obtain a satisfactory performance with respect to the disturbance rejection, the feed-forward control structure, shown in Fig. 11, could be used, Visioli (2006). 0.3853 GLO (s) = 2 e−0.319s s + 0.3129s + 0.3853 Fig. 8. Mass spring damper fourth order system Fig. 11. Feedforward action for load disturbance rejection task. Step Response Amplitude 1.5 Taking into account the model reduction the proposed feedforward controller is: 1 0.5 0 High-Order System Approximation 0 5 10 15 20 25 30 35 Linear Simulation Results and Amplitude 4 T (s) = 2 0 -2 0 5 10 15 20 25 2 ωnh −τ s , 2 e + 2ζh ωnh s + ωnh (17) 2 s2 + 2ζωn s + ωn2 K¯h ωnh 2 , 2 K̄ωn2 s + 2ζh ωnh s + ωnh (18) H(s) = K¯h 40 Time (seconds) 30 Time (seconds) Fig. 9. Response of the open-loop system to a step and a sinusoidal input The comparison of the open-loop response of the original system and the reduced model are presented in Fig. 9. As it can be seen the response for a step response (Fig. 9 up) is similar for both systems. For a sinusoidal input oscillating to the resonance frequency sin(ωr t), (Fig. 9, down). The PID controller is designed in order to regulate the output X2 (s), considering the tuning methods given in Section 5. The tuning parameters are ωnd = 4ωn , with ωn being the reduction natural frequency, and ζd = 1.2. Under these conditions, the closed loop performance shown in the Fig. 10. s2 The feedforward controller gains are: ζh = 2ζ, ωnh = 2ωn and K¯h = 0.1 The obtained response are shown in Fig. 12. Dashed line indicates the closed-loop performance for the control system shown in Fig. (6), it can be seen a large overshot due to an unitary input disturbance introduced in the instant t = 50s. On the other hand, the response of the system with the feed-forward controller shown in Fig. 11 is denoted by the solid line, where the effect of the disturbance is attenuated. Both simulations were performed considering the high order system (16). 2 1.8 1.6 1.4 1.2 1 1.2 0.8 0.6 1 0.4 0.8 PID controller Feedforward PID controller 0.2 0 0.6 0 10 20 30 40 50 60 70 80 90 100 0.4 Fig. 12. Closed-loop response: Feedforward effect on the disturbance rejection task. 0.2 0 −0.2 Original system Reduced system 0 20 40 Fig. 10. Closed loop Performance 60 80 100 5.4 Evaluation of the reduction model with different tuning control methods In order to validate the reduced order model, it is evaluated using different tuning methods for PID controllers found in 1352 Proceedings of the 20th IFAC World Congress Toulouse, France, July 9-14, 2017 David F. Novella-Rodríguez et al. / IFAC PapersOnLine 50-1 (2017) 1316–1321 the available literature O’Dwyer (2009). The PID controllers were computed taking into account the second order reduced model plus time delay obtained in the example given in Section 5.2 (except in the case of the Ziegler-Nichols method), the tuning rules considered have been taken from different authors, Schaedel (1997); Rivera and Jun (2000); Huang et al. (2005); Jahanmiri and Fallahi (1997). The computed controller is applied to the original high order system. In order to numerically evaluate the output control performance, it is possible to compute the Integral absolute error (IAE) of the error signal, e(t) = r(t) − y(t): IAE = 0 ∞ |e(t)|dt, Fig. 13 shows the obtained results in terms of the performance index (IAE) using different tuning methods available in the literature for second order systems, and applying the obtainedd controller to the original high order system. 10 1. Proposed Method 2. Shaedel 3. Rivera and Jun 4. Ziegler−Nichols 5. Huang et al 6. Jahanmiri and Fallali 9 Integral Absolute Error IAE 8 7 6 5 4 3 2 1 0 1 2 3 4 Tuning Method 5 6 Fig. 13. Performance Index for different Tuning Methods 6. CONCLUSIONS The main contribution of this paper is to propose a reduced order model for high order systems with oscillating dynamics. This class of systems appears commonly in the modeling of large mechanical process or civil structures. The reduction methodology is simple but it takes into account frequency domain characteristics of the high order original system and uses them to obtain an accurate model reduction. In order to match the phase characteristics of the high order systems and the low order simplification a time delay is introduced to the model reduction. The reduction of the model parameters allows us to simplify the tuning of PID controllers for the system. Following the IMC tuning rules it is possible to synthesize PID controller parameters in a simple and efficient way and use this controllers in the original high order process. Numerical simulations show that the controllers computed for the model reductions work with an acceptable performance for the high order plant. 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