Time delay system identification based on optimization approaches Ahlem SASSI, Saïda BEDOUI, Kamel ABDERRAHIM Numerical Control of Industrial Processes, National School of Engineers of Gabes, University of Gabes, St Omar Ibn-Khattab, 69 Gabes, Tunisia. E-mail: ahlemsessi@yahoo.com, saida.bedoui@enig.rnu.tn, amelabderrahim@yahoo.fr Abstract In this paper, the problem of estimating the time delay and dynamic parameters of monovariable time delay discrete is addressed. This problem involves both the estimation of the time delay and the dynamic parameters from input-output data. In fact, we have considered our previous method which consists in minimizing a quadratic criterion using either the gradient method or the Levenberg-Marquardt method. The used criterion is deduced from a formulation allowing to define the time delay and the dynamic parameters in the same estimated vector and to build the corresponding observation vector. In order to improve the performance of this approach, we advocate the use of the quasi-newton approach based on the Broyden- Fletcher-Goldfarb-Shanno (BFGS) algorithm. Simulation results are presented to illustrate the performance of the new solution. I. INTRODUCTION This paper deals with the problem of identification of time delay systems. This problem consists in building mathematical models of processes using observed input-output data []. It has received a great attention in the last years, since the delay time is a physical phenomenon which arises in most control loops of industrial processes [],[3]. In fact, the delay may be an inherent property of the system such as in the transport processes or in the accumulation of time lags in a large number of low order systems connected in series. It may also be introduced by the time response of control loop devices such as sensors, actuators, controllers, networs, etc. The time delay identification problem involves both the estimation of the dynamic parameters and the delay time. This is one of the most difficult problems that represent an area of research where considerable wor has been done in the last years. The proposed methods for the identification of time delay systems can be classified in different ways depending on : the type of used time (continuous or discrete), the form of solution (linear or nonlinear), the type of data processing (batch or iterative or recursive), etc []-[4],[7]-[],[3],[5],[6],[8],[],[3]. Only the iterative identification approach for monovariable discrete time delay systems is considered in this paper. This approach has found applications in many fields, such as signal processing and control [],[9],[]. The early iterative method [] developed in the literature consists in using the parametrisation approach. It is based on two main steps. The first consists in using the least square algorithm to estimate the parameters of a system with a nown delay which is greater than the real delay. The second allows to deduce the delay from the first zero coefficients of the numerator. In practice, it is difficult or rather impossible to have zero coefficients from experimental 978--4799-8-4/3/$3. c 3 IEEE data. Thus, a threshold must be selected. This selection leads to poor results when the input-output measurements are noisy. Another method is proposed in [6]. It is based on the use of the least square approach to identify the parameters assuming that the delay is nown. Then, it estimates the delay either by maximizing the correlation function, or by minimizing the quadratic error. This method assumes that the domain range of the delay is nown. We cite also our approach which consists in defining the time delay and the dynamic parameters in the same estimated vector and in building the corresponding observation vector [],[3]. This formulation is then used to propose a method to identify these systems by minimizing a quadratic criterion using either the gradient method or the Levenberg- Marquardt method. The gradient algorithm usually converges very slowly [5],[3],[33]. In fact, this algorithm cannot be recommended. The Levenberg-Marquardt switches between the Gauss- Newton approach and the gradient method [3],[33]. It allows us to find a solution even if the initial conditions are far to the final minimum. However, it finds only a local minimum, not a global minimum. In this paper, we propose the use the quasi- Newton approach for solved the obtained problem. The idea of quasi-newton method consists in approximating the inverse Hessian [9],[3],[33]. This represents the important advantage of the quasi-newton method since no matrix inverse has to be performed. It can be mentioned that the Broyden-Fletcher- Goldfarb-Shanno (BFGS) formula is more often adopted to approximate the inverse Hessian. This choice can be justified by the following reasons: no requirement for second order derivatives, very fast convergence and recommended for medium sizes problems ( parameters). This paper is organized as follows. Section II presents the model and its assumptions. Section III recalls the gradient and the Levenberg-Marquardt methods for the identification of time delay systems. In section IV, we propose a new solution based on the quasi-newton BFGS algorithm. Sections IV and V illustrate the performance of this method a simulation example and an experimental validation carried out on a level control process II. MODEL AND ASSUMPTIONS We consider the problem of estimating the time delay and the parameters of the following system: A(q )y()=q d B(q )u()+v() () where u() and y() are the system input and output, respectively, v() is a white noise and d is the time delay. A(q )
and B(q ) are two polynomials with the unit bacward shift operator q [i.e. q y()=y( )] having the orders n a and n b respectively, defined by: A(q )=+ B(q )= n a i= n b i= a i q i = +a q +...+ a na q n a b i q i = b q +...+ b nb q n b Using these expression, equation () can be rewritten as: y()= n a i= a i y( i)+ n b i= b i u( i d) () The model described above will be studied under the following assumptions: A. The polynomials A(q ) and B(q ) are coprime. A. The orders n a and n b of the model are nown. A3. The input sequence u() is a stationary ergodic process, independent of v() and is persistently exciting. A4. The disturbance v() is a sequence of independent, identically distributed random variable with zero mean and finite variance σ v A5. The input, the output and the noise are causal, i.e. u()=, y()= and v()= for. Problem statement: The goal is to develop an algorithm to estimate, simultaneously, the time delay d and the parameters (a,...,a na,b,...,b nb ). III. EXISTING METHODS Several approaches have been proposed in the litterature of the simultaneous identification of time delay and dynamic parameters of time delay systems[4]-[6],[9],[],[],[4],[6]- [8],[]-[4],[6]. These methods can be classified in different categories of solution such as the least square solution, etc. Only the optimization solution is considered in this paper. It consists in minimizing a quadratic criterion in term of the prediction error e() given by: J()= e ()= [y() ŷ()] (3) where ŷ() is the estimate of y() at iteration defined by: ŷ()= n a i= â i ( )y( i)+q ˆd( ) n b ˆb i ( )u( i) i= The estimated output ŷ() can be rewritten as: (4) ŷ()= ˆθ T ( )φ() (5) where ˆθ() the estimated parameter vector given by: ˆθ( )=[â ( ),...,â na ( ), ˆb ( ),..., ˆb nb ( )] T φ()=[ y( ),.., y( n a ),u( d),..,u( n b d)] In order to identify simultaneously the time delay and the parameters of a delay system, we have proposed to define these ones in the same estimated vector ˆθ G which we associate the corresponding observation vector φ G [],[3] such as: ˆθ G ()=[â (),...,â na (), ˆb (),..., ˆb nb (), ˆ d()] (6) φ G ()= y( ).. y( n a ) q d( ) ˆ u( ). q d( ) ˆ u( n b ) n b ˆb i= i ( )q d( ) u( i) where: u( i)= u( i) u( i ) (see appendix). We have proposed methods which consist in minimizing the quadratic criterion (3) using, in a first time the gradient search. It leads to the following iterative algorithm of computing ˆθ G () as follows: J() ˆθ G ()= ˆθ G ( ) µ() ˆθ G ( ) Deriving the quadratic criterion J() by the estimated parameter vector ˆθ( ), we obtain the following expression: J() ˆθ G ( ) = e() ŷ() e()= e() (9) ˆθ G ( ) ˆθ G ( ) Therefore, we get: (7) (8) J() ˆθ G ( ) = φ G()e() () Thus, the gradient algorithm is given by: ˆθ G ()= ˆθ G ( ) µ()φ G ()e() () where µ() is a positive scalar called step size. Then, we have proposed another method which minimizes the same criterion (3) using the Levenberg-Marquardt search: ˆθ G () = ˆθ G ( ) µ()[h()+λ I] J() ˆθ G ( ) where λ is a scalar and H() the Hessian of J() as: H() = J() ˆθ G ( ) ˆθ G ( ) T The second derivatives of the criterion (3) was approximated in [] using the small residual algorithm in [5] as: H() φ G ()φg T () () And using the expression (), the Levenberg-Marquardt algorithm is given by: ˆθ G ()= ˆθ G ( )+ µ()[φ G ()φ T G ()+λi] φ G ()e() (3) Note: If λ is too high comparing with the Hessian, this algorithm converges to the gradient one. Else, it converges to the Newton algorithm. 474
IV. PROPOSED APPROACHES The BFGS algorithm is one of the most efficient quasi- Newton methods for unconstrained optimization [7],[8]. It is by far the most popular quasi-newton update formula. This algorithm was proposed by Broyden, Fletcher, Goldfarb, and Shanno individually. It allows to compute ˆθ G as follows: ˆθ G ()= ˆθ G ( ) µ()[h()] J() (4) ˆθ G ( ) Using (), this expression can be rewritten as follows: ˆθ G ()= ˆθ G ( )+ µ()[h()] φ G ()e() (5) The main idea is to approximate the Hessian H() by B() as follows: B()=B( )+ W()W T () W T ()V() + B( )V()V T ()B T ( ) V T ()B( )V() (6) where B() is called the Broyden and W() is the gradient variation given by: J() ˆθ J() W()= G () ˆθ G ( ) µ() (7) V()=[B( )] φ G ()e() In order to compute the term J(), we opt for deriving J() ˆθ G () by the expression (5) of ˆθ G (). Using the series expansion of this derivation, the second order approximation is given by: J() ˆθ G () = + J() ˆθ G ( ) J() ˆθ G ( ) ˆθ G T ϕ()e()) ( )(µ()[h()] Using (7) and (), The obtained formula of W() is: W()= (ϕ()ϕt ())(µ()[h()] ϕ()e()) µ() (8) The proposed algorithm, deduced from equations (4)-(8), is summarized in algorithm. Data: u, y, θ G. N: the measurement number. choose µ(). begin For =:N Construct the observation vector φ G () using (7) Compute: V()=[H( )] ϕ()e() W()= (ϕ()ϕt ())(µ()[h()] ϕ()e()) µ() H()=H( )+ W()W T () V T ()H( )V() ˆθ G ()= ˆθ G ( )+ µ()[h()] ϕ()e() W T ()V() H( )V()V T ()H T ( ) Algorithm : BFGS Algorithm V. RESULTS We present now two examples to illustrate the effectiveness of proposed approaches. Therefore, the main purpose of these simulation examples is to compare the performance of the proposed approaches with that of gradient and Levenberg- Marquardt approaches. A. Example The simulations are performed under the following conditions: The system to be identified is persistently excited by a pseudo random binary sequence (PRBS). The estimation starts with zero initial values for the parameters and the time delay. The additive noise v() is a white noise sequence with zero mean and constant variance σ v = 4. The evaluation of the parameter error is considered to study the performance of each studied method. δ = θ ˆθ θ The simulated example is a second-order plus time delay process defined by: G(q )=q d b q + b q +a q + a q where a =., a =.36, b =.5, b =.35 and d =. The simulation results are given in Fig., Fig., Fig.3 and Fig.4...4.6 a â.8 4 6.5 b.5 ˆb 4 6.5 a â.5 4 6 b.5 4 6 d 4 6 Fig.. Real and estimated evolutions of the time delay and parameters using the gradient method. 475
.5 a â 4 6.5 b ˆb.5 4 6.5 a â.5 4 6.8 b.6.4. 4 6 The estimated parameters of the three methods converge to their true values The best results in term of speed of convergence are given by the proposed method. B. Example We consider a level control process. This process is composed of a set of eight identical tans illustrated in Fig.5. It is easy to remar that the output of tan (j), q j represent the input of tan (j+), q j+. Using this notation and assuming that the levels in the tans are in an operating point, we can approximate the dynamic behaviour of the level in each tan by a simple linear model: d 4 6 Fig.. Real and estimated evolutions of the time delay and parameters using the Levenberg-Marquardt method. S dh j dt = q j q j+ (9) q j+ = Kh j where S is the section of the tan and K is a constant representing the properties of the tan...4.6 a â.8 4 6.6.4. b ˆb. 4 6.5 a â.5 4 6 b.5 4 6 d 4 6 Fig. 3. Real and estimated evolutions of the time delay and parameters using the BFGS method..5.5 Gradient Levenberg-Marquardt BFGS Fig. 5. where Level control process. Thus, the transfer function of each tan is given by: H j (p)= K(T p+) q j(p) () T = S K 3 4 5 6 Fig. 4. The parameter estimation errors δ. Based on the results presented in figures Fig., Fig., Fig.3 and Fig.4, we observe that: Then, we can deduce that the transfer function relating q (t) with the level in the tan eight h 8 (t) by: where H 8 (p)= K e (T p+) 8 q (p) () K e = f rac 476
The above system is a connexion in series of eight first order systems. Consequently, it can be approximated by a first order system with delay [3] defined by: K a H 8 (p)=e τ p +T a p q (p) () The delay τ, is introduced by the accumulation of time lags of the first order systems and T a and K a are the constant time and the constant of the time delay system. The discrete transfer function of () is given by: H(q )=q d b q + b q +a q (3) As a numerical example, we consider the step response of this systems with T =, K = and sampling time T e = s (Fig.6). In this case, the transfer function representing the dynamic behaviour of the global system is: H(p)= (p+) 8 (4) Fig.6 shows the presence of an important time delay. It..4.6 â.8 5.8.6.4. 5.8.6.4. ˆb 5.5 5 Fig. 7. Real and estimated evolutions of the time delay and parameters using the gradient method...8.6.4.4..8.6.4.6 â.8 5.8.6. ˆb 5 Fig. 6.. 3 4 5 6 The step response..4. 5.5 5 is therefore possible to approximate the model of a very high-order complex dynamic process with a simplified model defined by (). In that way, we apply the three methods (gradient, Levenberg-Marquardt and BFGS) in order to estimate the time delay and the parameters. The obtained results are presented in Fig.7, Fig.8 and Fig.9 which show that the BFGS method gives the best results in terms of speed of convergence and precision. VI. CONCLUSION In this paper, we have considered the problem of simultaneous identification of the time delay and dynamic parameters of monovariable time delay discrete. Indeed, we have recalled our previous approach which consists in defining the time delay and the dynamic parameters in the same estimated vector and in building the corresponding observation vector. Then, this formulation is used to develop a method to identify the delay and the dynamic parameters by minimizing a quadratic criterion using either the gradient method or the Levenberg- Marquardt method. To improve the performance of this approach, we have advocated the use of the quasi-newton BFGS algorithm to solve the obtained system. Simulation and experimental examples are presented to illustrate the effectiveness of the proposed method and to compare their performance in terms of convergence speed and precision. Fig. 8. Real and estimated evolutions of the time delay and parameters using the Levenberg-Marquardt method...4.6 â.8 5.8.6.4. 5.8.6.4. ˆb 5.5 5 Fig. 9. Real and estimated evolutions of the time delay and parameters using the BFGS algorithm. APPENDIX A APPROXIMATION OF Ln(q) The shift operator and the bacward difference are given by respectively (5) and (6): 477 qu()=u(+) (5)
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