Adaptive Harmonic Steady State Control for Disturbance Rejection

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1 Adaptive Harmonic Steady State Control for Disturbance Rejection J. Chandrasekar L. Liu D. Patt P. P. Friedmann and D. S. Bernstein I. INTRODUCTION The use of feedback control for disturbance rejection is of fundamental importance in a broad range of applications and the development of effective algorithms is an ongoing area of research. For well-modeled plants with broadband disturbance classical LQG theory can be applied with weighting filters introduced to shape controller effort in accordance with the disturbance spectrum and performance objectives 1. On the other hand if the disturbance is tonal or multi-tonal with known spectrum then a model of the exogenous signal can be embedded in the controller to produce high gain feedback at frequencies that comprise the spectrum of the disturbance 2. An alternative approach which is applicable in the case of tonal or multi-tonal disturbances with known spectrum allows the system to reach harmonic steady state and uses measurements of the steady state response amplitude and phase to determine the required control signal. This technique was developed independently within two research communities. For helicopter vibration reduction this technique is known as higher harmonic control 3 4. The same technique was developed independently for active rotor balancing in this case known as convergent control 5. We refer to this algorithm as HSS harmonic steady state control. Connections between higher harmonic control and internal model control are discussed in 6. When the plant and the disturbance are not well modeled then the problem can be significantly more challenging. Within the active noise control literature a host of adaptive algorithms have been developed inspired by digital signal processing techniques. These techniques are based on LMS updating of FIR filters 7. Another approach involves continuously adjusting the frequency magnitude and phase of the control input to cancel the disturbance 8. Alternative techniques which require limited modeling of the plant dynamics and disturbance spectrum have also been developed; see for example 9. This research was supported by the Air Force Office of Scientific Research under grant F and the Army Research Office under grant The authors are with Department of Aerospace Engineering The University of Michigan Ann Arbor MI FAX dsbaero@umich.edu Fig. 1. w u G G c Harmonic Steady State Control Architecture Implementation of HSS control requires knowledge of the frequency response of the transfer function between the control input and the measurements at the disturbance frequency. In practice this information is obtained through modeling or off-line identification. When this information is uncertain or when the plant is subject to change instability can occur. Consequently the robustness of HSS control is analyzed in 5 for both additive and multiplicative model uncertainty. Adaptive extensions of HSS control that remove the need to independently model the control-tomeasurement frequency response have been considered. Specifically in the least squares procedure was proposed for estimating this transfer function. Analysis of convergence of the estimates and performance of HSS with simultaneous estimation were not discussed in In the present paper we develop a unified framework for analyzing the properties and performance of adaptive harmonic steady state control thus extending and including most of the previous literature on harmonic steady state control. II. HARMONIC PERFORMANCE ANALYSIS Assume for convenience that the disturbance w R d acting on the plant is a single harmonic with constant amplitude and phase. The HSS control algorithm waits until the output z R p reaches harmonic steady state and then measures the amplitude and phase of the output. With this information the control input u R m is determined to minimize the effect of disturbance. As shown in Fig. 1 the HSS control algorithm is a feedback controller and thus can potentially destabilize the plant although not in z

2 the usual LTI sense. As indicated in Fig. 1 we assume that the disturbance signal is unmeasured and thus is not available for feedback. The inputs uw and the output z are related by z w G zw G zu 2.1 u where G zw s and G zu s are multi-input multi-output continuous-time transfer functions and for all i 1...p j 1...d and l 1...m G ziw j s and G ziu l s are the SISO entries of G zw s and G zu s respectively. In HSS control the update of the control input u is not performed continuously but rather at specified times t k. The control input is harmonic with the same spectrum as the disturbance and the amplitude and phase of the control input are updated at t k. The time interval t k+1 t k between two successive updates need not be constant but must be sufficiently large to allow the output z to reach harmonic steady state. At steady state the amplitude and phase of the output z are completely determined by the amplitude and phase of the disturbance and control input. Assuming that the disturbance w is harmonic with frequency ω 1 and the output z has reached harmonic steady state within the time interval t k t k+1 wt zt and ut have components wt ŵ 1Ree jω 1 t+φ 1 ŵ d Ree jω 1 t+φ d T zt ẑ 1k Ree jω 1 t+θ 1k ẑ pk Ree jω 1 t+θ pk T ut û 1k Ree jω 1 t+ψ 1k û mk Ree jω 1 t+ψ mk T 2.2 where ŵ i R ẑ ik R û ik R are the amplitudes and φ i R θ ik R ψ ik R are the phase angles of the ith component of wt zt and ut respectively. Note that the amplitude ŵ i and phase φ i of the ith component of wt are independent of the time interval and furthermore φ i is determined by the choice of t 0. Next define w si R w ci R z sik R z cik R u sik R and u cik R by w si ŵ i sinφ i w ci ŵ i cosφ i z sik ẑ ik sinθ ik z cik ẑ ik cosθ ik u sik û ik sinψ ik u cik û ik cosψ ik. 2.3 Note that for all i 1...d w si and w ci are constants determined by the choice of t 0 and that u sik and u cik are determined by the control law. Define w R 2d z k R 2p and u k R 2m by w T Ț w s1 w c1 w sd w cd z k z s1k z c1k z spk z cpk u k u s1k u c1k u smk u cmk T. 2.4 It follows from that the system dynamics in terms of w z k and u k are given by z k Tu k + Ww 2.5 where for all i 1...p j 1...m and l 1...d the entries T ij R 2 2 and W il R 2 2 of T R 2p 2m and W R 2p 2d respectively are defined by ReGzi u T ij j jω 1 ImG zi u j jω 1 ImG zi u j jω 1 ReG zi u j jω 1 ReGzi w W il l jω 1 ImG zi w l jω 1 ImG zi w l jω 1 ReG zi w l jω Replacing k by k+1 in 2.5 and subtracting the resulting equation from 2.5 yields the disturbance-free update model z k+1 z k + Tu k+1 u k. 2.7 When the disturbance wt is a sum of sinusoids of multiple frequencies the above analysis carries through with minor modifications. III. THE HSS ALGORITHM Consider the cost function Jz k u k z T k Qz k + 2z T k Su k + u T k Ru k 3.1 where Q R 2p 2p S R 2p 2m and R R 2m 2m are weighting matrices such that R is positive definite and Q S is positive semidefinite. Substituting zk from S T R 2.5 into 3.1 yields Jz k u k u T k T T QT + T T S + S T T + R u k + 2u T k TT Q + S T Ww + w T W T QWw. Since 3.2 involves only u k and w we define 3.2 J wu k Jz k u k 3.3 and write J wu k as Jw u k Ww T u T Q QT + S Ww k T T Q + S T D u k 3.4 where the positive-semidefinite matrix D is defined by D T T QT + T T S + S T T + R. 3.5 To determine u k that minimizes J wu k we set J wu k u k 2Du k + 2T T Q + S T Ww Assuming D is positive definite the optimal control law is given by u opt u kopt D 1 T T Q + S T Ww 3.7 and the minimum cost is Jw u opt w T W T Q QT + SD 1 T T Q + S T Ww. 3.8 Since u opt depends on w whose measurement is not available we derive an equivalent control law that can be used for all k 1. Setting k 0 in 2.5 yields Ww z 0 Tu and hence substituting 3.9 into 2.5 yields z k z 0 + Tu k u

3 From 3.9 the optimal control law u opt in 3.7 can be expressed as u opt D 1 T T Q + S T z 0 Tu IV. CONVERGENCE ANALYSIS OF HSS ALGORITHM Note that u opt given by 3.11 is independent of k and hence remains constant for all k 1. Substituting 3.11 in 3.10 and 3.1 the optimal value of z k for all k 1 is given by z opt z kopt and thus I TD 1 T T Q + S T Jz opt u opt z 0 Tu 0 T Q QT + SD 1 z 0 Tu T T Q + S T z 0 Tu Using 3.10 the optimal control law can be expressed recursively as u k+1opt D 1 T T Q + S T z kopt Tu kopt. 4.3 The state-space representation of the system dynamics with the optimal control law is zk+1opt zkopt A 4.4 u k+1opt u kopt where A R 2p+m 2p+m is defined by I2p TM I A 2p TMT M MT 4.5 and M R 2m 2p is defined by M D 1 T T Q + S T. Note that A 2 A and hence A is an idempotent matrix and its eigenvalues are either 0 or 1. In fact A can be factored as I2p T I2p 0 I2p T A 0 I 2m M 0 2m 0 I 2m 4.6 which implies that speca speci 2p spec0 2m. 4.7 Since A is idempotent with the initial conditions z 0 and u implies that for all k zopt A k z0 A u opt u 0 z0 u 0 I TMz0 Tu 0 Mz 0 Tu Consequently the optimum values of u opt in 3.11 and z opt in 4.1 are attained after the first update. Note that if S 0 then z opt in 4.1 can be expressed as Hence z opt Q 1 Q 1 + TR 1 T T 1 z 0 Tu σ maxr z opt σ minqσ l TT T z0 Tu where l min2p2m. Note that if σmaxr is large minimum energy σ minq control it follows from 4.10 that z opt may be large indicating poor performance. Alternatively if σmaxr σ minq is small cheap control then 4.10 implies that the z opt is small and hence the performance is good. V. ROBUSTNESS OF HSS CONTROL Implementation of HSS requires knowledge of T. An erroneous model of T can result in degraded performance and possible instability. If an estimate ˆT of T is given the control law defined in 4.3 becomes where û k+1 ˆMz k ˆTû k 5.1 ˆM ˆD 1 ˆT T Q + S T R 2m 2p 5.2 assuming that ˆD ˆT T Q ˆT + S T ˆT + ˆT T S + R 5.3 is positive definite. The state-space representation of the system-dynamics with 5.1 is zk+1  zk 5.4 û k+1 where  is defined by I2p T  ˆM T ˆM ˆT I 2p ˆM ˆM ˆT and T ˆT T. û k It is useful to factor  as I2p T I2p 0  0 I 2m ˆM ˆM T which shows that 5.5 I2p T 0 I 2m 5.6 specâ speci 2p spec ˆM T. 5.7 Hence the HSS algorithm is stable if and only if Assuming S 0 it follows that sprad ˆM T sprad sprad ˆM T < ˆT T Q ˆT + R 1 ˆTT Q T σmaxt + σmax TσmaxQσmax T. σ minr Therefore it can be shown that if σ max T < σmaxt 2 then sprad ˆM T < 1. σ minr σ maxt σminr σ maxq 5.10 If is large minimum energy control σ maxq then according to 5.10 HSS control possesses a high degree of robustness and a sufficient condition for the stability of HSS algorithm is approximately given by σ max T < σ minr σ maxq However if σminr σ maxq is small cheap control then 5.10 implies that robustness is compromised.

4  k I2p T 0 I 2m From 5.6 it follows that k 1 i1 I 0 ˆM T i ˆM k ˆM T I2p T 0 I 2m.5.12 Now assume that HSS control is stable that is 5.8 is satisfied. In this case lim ˆM T k 0 and let k 1 Γ lim ˆM T i I ˆM T 1. Hence the i1 limiting values of z k and û k are given by lim zk lim  k z0 ûk û 0 I TΓ ˆMz0 Tû 0 Γ ˆMz 0 Tû Now consider the case where the estimate ˆT of T involves a multiplicative error that is ˆT TI + T mul where T mul R 2m 2m. Then ˆT can be expressed equivalently by ˆT T + T where T T T mul. A sufficient condition for stability of HSS control is sprad ˆMT T mul < 1. Following a procedure similar to the one discussed in 5.9 and 5.10 for additive uncertainty it can be shown that the HSS algorithm is stable if σ max T mul < σ minr σmax 2 TσmaxQ VI. ADAPTIVE HSS CONTROL ALGORITHM Here we discuss online identification of the matrix T which will then be used as the basis for an adaptive extension of HSS control discussed in the previous sections. Define z k R 2p and u k R 2m by z k z k z k 1 u k u k u k and Z k R 2p k and U k R 2m k by Z k z 1 z k Uk u 1 u k. 6.2 The system dynamics equation 2.7 can be represented by Hence it follows from 6.1 that z k T u k. 6.3 Z k T U k. 6.4 Assuming U k Uk T is nonsingular we define P k U k Uk T and it follows from 6.4 that the least squares estimate ˆT LSk of T is given by ˆT LSk Z k U T k P k. The recursive least squares method is used to iteratively update ˆT LSk based on the past and current values of z k and u k. Since u k u T k is positive semidefinite for all k and k U k Uk T u i u T i 6.6 i1 it follows that if U k0 U T k 0 is nonsingular then U k Uk T is nonsingular for all k > k 0. Hence the recursive procedure for determining T LSk for all k > k 0 is given by where K k u T k+1p k u k+1 1 u T k+1p k 6.7 ˆT LSk+1 ˆT LSk + ε k+1 K k Note that P k+1 P k I u k+1 K k ε k+1 z k+1 ˆT LSk u k rank U k U T k rank U k mink 2m Since U k Uk T is 2m 2m it follows from 6.11 that U k Uk T is singular for all k < 2m. Hence the recursive procedure cannot be used for k < 2m. A suboptimal way to determine an estimate ˆT k of T is to replace P k in by ˆP k that is K k u T ˆP k+1 k u k+1 1 u T ˆP k+1 k 6.12 ˆT k+1 ˆT k + ε k+1 K k ˆP k+1 ˆP k I u k+1 K k where ˆP 0 is positive definite but otherwise arbitrary and ε k is defined by 6.10 with ˆT LSk replaced by ˆT k. It follows from that ˆP k is positive definite for all k 0 and is given by 1 ˆP k ˆP 0 + U k Uk T Furthermore ˆTk is given by ˆT k ˆT 1 0 ˆP 0 + Z k Uk T 1 ˆP 0 + U k Uk T Since ˆP 0 is positive definite the inverse in 6.16 always exists and hence the recursive procedure can be used for all k 0. The updated estimate ˆT k is used at each control update step to calculate the control law u k+1 which is given by where ˆM k is defined by u k+1 ˆM k z k ˆT k u k 6.17 ˆM k ˆT T k Q ˆT k + S T ˆTk + ˆT T k S + R 1 ˆT T k Q + ST VII. CONVERGENCE ANALYSIS OF THE ESTIMATE ˆT k Define T k R 2p 2m by T k ˆT k T 7.1 where ˆT k is updated using Next define the function V T ˆP by and V k by V T ˆP T ˆP 1 T T 7.2 V k V T k+1 ˆP k+1 V T k ˆP k 7.3

5 where ˆP k is updated using and ˆP 0 is the positive definite matrix used to initialize Subtracting T from both sides of 6.13 yields T k+1 T k + ε k+1 K k Using 6.3 and 7.1 ε k+1 can be expressed as ε k+1 T k u k Substituting and 7.5 into 7.3 yields ε k+1 ε T k+1 V k 1 + u T ˆP 7.6 k+1 k u k+1 and hence V T k ˆP k is non-increasing. Since V T k ˆP k 0 it follows that lim V T k ˆP k exists and is nonnegative. Hence lim ε k+1 ε T k u T ˆP lim k+1 k u V k k+1 Next we show that u k is bounded. Substituting 6.4 into 6.16 yields ˆT k ˆT 1 0 ˆP 0 + T U k U T 1 k ˆP 1 ˆP 0 + U k U T k Post-multiplying 7.8 by 0 + U k Uk T yields 1 1 T k T 0 ˆP 0 ˆP 0 + U k Uk T If U k0 Uk T 0 is nonsingular then for all k k implies that σ max T k σ max T 0 σ min ˆP 0 σ min U k0 U T k Hence if ˆP0 is chosen to be sufficiently large then σ max T k can be made sufficiently small and even made to satisfy condition 5.10 and in that case it follows from 5.9 that for all k k 0 σ max ˆM k T k < The state space representation of the system dynamics with the control law 6.17 is zk+1 zk 7.12 u k+1 Âk where Âk is defined by 5.5 with ˆT and ˆM replaced by ˆT k and ˆM k respectively. Hence for all k k 0 zk zk u k ik 0  i u k u k0 Note that  i can be factored as ik 0 I2p T I 0  i I2p 0 I 2m  k 21 ik ˆM T ik 0 i T i 0 I 2m where  21 ˆM k0 I + k 1 ik 0 i j0 T ˆM j j+1. From 7.11 it follows that σ max ˆM i T i ik 0 ik 0 σ max ˆM i T i < and hence it follows from that z k and u k are bounded. Hence for all k let γ > 0 satisfy u k < γ and thus u k u k+1 u k < 2γ From 6.5 and 6.6 it follows that ˆPk+1 ˆP k which implies that ˆP k ˆP Hence it follows from 7.17 and 7.16 that 1 + u T k ˆP k u k 1 + 4γ 2 λ max ˆP and thus 7.8 implies that lim ε k Taking the limit as k of 6.13 yields lim ˆTk+1 ˆT k lim ε k+1 u T k+1 ˆP k 1 + u T k+1 ˆP k u k From it follows that ˆTk+1 ˆT k lim Thus { ˆT k } is a Cauchy sequence and hence ˆT k converges. However there is no guarantee that ˆT k will converge to T. In fact it can be shown that there are certain choices of ˆP 0 and ˆT 0 such that ˆT k will not converge to T. Conditions on ˆT 0 and ˆP 0 that guarantee convergence of ˆT k to T are not available. However the steady state performance of the adaptive HSS control depends on the steady-state value of the estimate ˆT k. Note that using 6.16 the RLS estimate ˆT k can be expressed as ˆT k T + T 0 ˆP 1 0 ˆP k Consequently if ˆP k 0 as k then ˆT k T as k. It follows from the theory of RLS 12 that if u k is persistently exciting then ˆP k 0 as k. However since u k is given by the adaptive control law 6.17 u k may not be persistently exciting. Next define δ k R 2m by δ k 0 1 i 1 δ ki 0 1 2m i T 7.23 and δ ki R by δ ki δ sign u ki where u ki is the ith component of u k δ > 0 and i is the remainder when k is divided by 2m. Next define ũ k by ũ k u k + δ k. It can be shown that if δ is sufficiently large then ũ k is persistently exciting. The modified control law is given by u k+1 u k + ũ k. 7.24

6 Microphone output in volts At about 4 s the microphone is moved to a new location At about 22 s adaptive HSS control begins time in seconds Fig. 2. Top view of the acoustic drum with two end-mounted speakers and one internal microphone. The microphone is initially at Location A and is moved to Location B while the HSS control algorithm is operational. Fig. 3. Disturbance rejection using fixed-model and adaptive HSS control. The microphone is moved at t 4sand the output diverges. Adaptive HSS control begins at t 22 s and convergence is achieved. VIII. EXPERIMENTAL EXAMPLE: NOISE CANCELLATION IN AN ACOUSTIC DRUM The acoustic drum see Fig. 2 has two endmounted speakers and up to six microphones suspended inside the drum through holes drilled along the top. Though the equations of motion of the acoustic drum is not similar to that of the duct 1 the input-output response is linear and hence HSS control can be used to reject a disturbance with a known harmonic spectrum. A constant-amplitude single-tone disturbance signal wt with frequency 10 Hz ω 20π rad/s is produced by the disturbance speaker. The actuation speaker produces the control ut for cancelling the disturbance. The control objective is to reduce the output zt measured by microphone 1 at Location A. To estimate T a sinusoidal input with frequency 10 Hz amplitude û 1 and phase angle ψ 1 is applied to the system in N separate trials. The amplitude and phase of the output ẑ 1 and θ 1 are used to determine an initial estimate T LS of T using the batch least squares procedure. A dspace 1003 system is used to determine the vector z k from measurements of zt and the update u k is computed by a Simulink implementation of the HSS control algorithm. Fig. 3 shows the performance of HSS control with T T LS. Next we consider the case in which T is uncertain. At t 4 s the microphone is moved from its original location Location A to a new location Location B resulting in a change in the system dynamics. Since conventional HSS control is unaware of this change the modified system is unstable and the output diverges. At t 22 s adaptive HSS control begins and stability is recovered providing disturbance rejection at the new location. IX. CONCLUSION In this paper we developed adaptive harmonic steady state control for disturbance rejection. HSS control extends higher harmonic control and convergent control developed for helicopter vibration reduction and rotor imbalance control. HSS control is applicable to stable systems with tonal or multi-tonal disturbances. The adaptive HSS algorithm is easy to implement and robust in the sense that no modeling information is required aside from the number of disturbance harmonics. REFERENCES 1 J. Hong and D. S. Bernstein Bode Integral Constraints Colocation and Spillover in Active Noise and Vibration Control IEEE Trans. Contr. Sys. Tech. vol. 6 pp W. Messner and M. Bodson Design of Adaptive Feedforward Algorithms using Internal Model Equivalence Int. J. Adap. Contr. Sig. Proc. vol. 9 pp P. P. Friedmann and T. A. Millott Vibration Reduction in Rotorcraft Using Active Control: A Comparison of Various Approaches J. Guid. Contr. Dyn. vol. 18 pp K. P. Nygren and D. P. Schrage Fixed-Gain Versus Adaptive Higher Harmonic Control Simulation J. Amer. Helicopter Soc. vol. 34 pp C. R. Knospe R. W. Hope S. M. Tamer and S. J. Fedigan Robustness of Adaptive Unbalance Control of Rotors with Magnetic Bearings J. Vibr. Contr. vol. 2 pp S. R. Hall and N. M. Werely Performance of Higher Harmonic Control Algorithms for Helicopter Vibration Reduction J. Guid. Contr. Dyn. vol. 16 pp S. M. Kuo and D. R. Morgan Active Noise Control Systems. Wiley- Interscience M. Bodson and S. C. Scott Adaptive Algorithms for the Rejection of Sinusoidal Disturbances with Unknown Frequency Automatica vol. 33 pp R. Venugopal and D. S. Bernstein Adaptive Disturbance Rejection Using ARMARKOV/Toeplitz Models IEEE Trans. Contr. Sys. Tech. vol. 8 pp C. R. Knospe R. W. Hope S. J. Fedigan and R. D. Williams Experiments in the Control of Unbalance Response using Magnetic Bearings Mechatronics vol. 5 pp W. Johnson Self-Tuning Regulators for Multicyclic Control of Helicopter Vibration NASA Tech. Paper no K. J. Astrom and B. Wittenmark Adaptive Control. Addison Wesley 1995.