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for measurement, p; i P R, and P R l are unknown parameters. i X R 3 R m ,and' X R 3 R l are known and bounded functions of the state variable. A p is nonlinear in p,andf i is nonlinear in both i and i . Our goal is to ®nd an input u such that the closed loop system has globally bounded solutions and so that X p tracks as closely as possible the state X m of a reference model speci®ed in equation (9.2), where r is a bounded scalar input. We make the following assumptions regarding the plant and the model: (A1) X p t is accessible for measurement. (A2) i P  i , where  i X min;i ; mx;i , and min;i and mx;i are known; p is unknown and lies in a known interval p min ; p mx &R. (A3) t and 't are known, bounded functions of the state variable X p . (A4) f is a known bounded function of its arguments. (A5) All elements of Ap are known, continuous functions of the parameter p. (A6) b m b p where is an unknown scalar with a known sign and upper bound on its modulus, jj mx . (A7) Ap; b p is controllable for all values of p P,with Apb m g T pA m where g is a known function of p. (A8) A m is an asymptotically stable matrix in R n with detsI À A m X R m ss kRs; k > 0 Except for assumption (A1), the others are satis®ed in most dynamic systems, and are made for the sake of analytical tractability. Assumption (A2) is needed due to the nonlinearity in the parametrization. Assumptions (A3)±(A5) are needed for analytical tractability. Assumptions (A6) and (A7) are matching conditions that need to be satis®ed in LP-adaptive control as well. (A8) can be satis®ed without loss of generality in the choice of the reference model, and is needed to obtain a scalar error model. Assumption (A1) is perhaps the most restrictive of all assumptions, and is made here to accomplish the ®rst step in the design of stable adaptive NLP-systems. Our objective is to construct the control input, u, so that the error, E X p À X m , converges to zero asymptotically with the signals in the closed loop system remaining bounded. The structure of the dynamic system in equation (9.1) and assumptions (A6) and (A7) imply that when , p, , and i are known u gp T X p rÀ m i1 f i i ; i À' T 9:42 meets our objective since it leads to a closed loop system X p A m X p b m r Our discussions in Section 9.2 indicate that an adaptive version of the Adaptive Control Systems 231 controller in (9.42), with the actual parameters replaced by their estimates together with a gradient-rule for the adaptive law, will not suce. We therefore propose the following adaptive controller: u g p T X p r À m i1 f i i ; i À' T u a t9:43 e " e c À " sat e c " 9:44 signÀ e " '; À > 0 9:45 Àsign e " G; > 0 9:46 " i sign i e " ! à i À 1 " i À i ; 1 ; i > 0 i " i " i P  i mx;i " i > mx;i min;i " i < min;i V b b ` b b X 9:47 " p À p e " ! à m1 À 2 " p À p 2 ; p > 0 p " p " p P p mx " p > p mx p min " p < p min V b ` b X 9:48 Gx p ; pgp T X p r; G g p T X p r 9:49 u a Àsignsat e c " m1 i1 a i à 9:50 where a à i min ! i PR mx i P i signe " f i À f i ! i i À i hi ; i 1; FFF; m 9:51 a à m1 jj mx min ! m1 PR mx pP signe " G À G À ! m1 p À p Âà 9:52 w i are the corresponding w i 's that realize the min-max solutions in (9.51) and (9.52), and jj mx denotes the maximum modulus of .The stability property of this adaptive system is given in Theorem 4.1 below. Theorem 4.1 The closed loop adaptive system de®ned by the plant in (9.1), the reference model in (9.2) and (9.43)±(9.52) has globally bounded solutions if pt 0 Pand i t 0 P i V i. In addition, lim t3I e " t0. Proof For the plant model in (9.1), the reference model in (9.2) and the 232 Stable adaptive systems in the presence of nonlinear parametrization control law in (9.43), we obtain the error dierential equation E A m E b m G À G m i1 f i i ; i Àf i i ; i À ' T ~ ~ G u a t 45 9:53 Assumptions (A6)±(A8) and Lemma 4.2 imply that there exists a h such that e c h T E and e c Àke c 1 G À G m i1 f i i ; i Àf i i ; i À ' T ~ ~ G u a t 45 9:54 which is very similar to the error model in (9.37). De®ning the tuning error, e " , as in (9.44), we obtain that the control law in (9.43), together with the adaptive laws in (9.45)±(9.52) lead to the following Lyapunov function: V 1 2 e 2 " 1 jj ~ T À À1 ~ 1 jj À1 ~ 2 À1 p ~ p 2 1 jj m i1 À1 i ~ 2 i 4 2 ~ p " p À p 2 jj m i1 ~ i " i À i 5 9:55 This follows from Corollary 4.2 by showing that V 0. This leads to the global boundedness of e " , ~ , ~ p, ~ and ~ i for i 1; FFF; m. Hence e c is bounded and by Lemma 4.2, E is also bounded. As a result, i ;' and the derivative e c are bounded which, by Barbalat's lemma, implies that e " tends to zero. Theorem 4.1 assures stable adaptive control of NLP-systems of the form in (9.3) with convergence of the errors to within a desired precision ". The proof of boundedness follows using a key property of the proposed algorithm. This corresponds to that of the error model discussed in Section 9.4.1, which is given by Lemma 3.2. As mentioned earlier, Lemma 3.2 is trivially satis®ed in adaptive control of LP-systems, where the inequality reduces to an equality for ! 0 determined with a gradient-rule and a 0 0. For NLP-systems, an inequality of the form of (9.18) needs to be satis®ed. This in turn necessitates the reduction of the vector error dierential equation in (9.53) to the scalar error dierential equation in (9.54). We note that the tuning functions a à i and ! à i in the adaptive controller have to be chosen dierently depending on whether f is concave/convex or not, since they are dictated by the solutions to the min±max problems in (9.51) and (9.52). The concavity/convexity considerably simpli®es the structure of these tuning functions and is given by Lemma 3.1. For general nonlinear parametrizations, Adaptive Control Systems 233 the solutions depend on the concave cover, and can be determined using Lemma 4.3. Extensions of the result presented here are possible to the case when only a scalar output y is possible, and the transfer function from u to y has relative degree one [10]. 9.4.3 Adaptive observer As mentioned earlier, the most restrictive assumption made to derive the stability result in Section 9.4.2 is (A1), where the states were assumed to be accessible. In order to relax assumption (A1), the structure of a suitable adaptive observer needs to be investigated. In this section, we provide an adaptive observer for estimating unknown parameters that occur nonlinearly when the states are not accessible. The dynamic system under consideration is of the form y p Ws; pu; Ws; p n i1 b i ps nÀ1 s n n i1 a i ps nÀi 9:56 and the coecients a i p and b i p are general nonlinear functions of an unknown scalar p. We assume that (A2-1) p lies in a known interval p min ; p mx . (A2-2) The plant in (9.56) is stable for all p P. (A2-3) a i and b i are known continuous functions of p. It is well known [1] that the output of the plant, y p , in equation (9.56) satis®es a ®rst order equation given by y p Ày p f p T ;>0 9:57 where ! 1 Ã! 1 ku 9:58 ! 2 Ã! 2 ky p 9:59 u;! T 1 ; y p ;! T 2 Âà T 9:60 f p c 0 p; cp T ; d 0 ; dp T hi T 9:61 for some functions c 0 Á, cÁ, d 0 Á,and dÁ, which are linearly related to b i Á and a i Á. à in (9.58) and (9.59) is an n À1Ân À1 asymptotically stable matrix and Ã; k is controllable. Given the output description in equation (9.57), an obvious choice for an adaptive observer which will allow the on-line estimation of the nonlinear 234 Stable adaptive systems in the presence of nonlinear parametrization parameter p, and hence a i 's and b i 's in (9.56), is given by y p À y p f T À a 0 sat e 1 " 9:62 where f f p and e 1 is the output error e 1 y p À y p . It follows that the following error dierential equation can be derived: e 1 Àe 1 f À f T À a 0 sat e 1 " 9:63 Equation (9.63) is of the form of the error model in (9.32) with k 1 , k 2 1, ' ` ` 0, m 1, f 1 Àf T , 1 , and 1 p. Therefore, the algorithm e " e 1 À " sat e 1 " " p À p e " ! 0 À " p À p; p > 0 p " p " p P p mx " p > p mx p min " p < p min V ` X 9:64 where a 0 and ! 0 are the solutions of a 0 min !PR mx pP sign e " f À f T À ! p À p ! ! 0 arg min !PR mx pP signe " f À f T À ! p À p ! The stability properties are summarized in Theorem 4.2 below: Theorem 4.2 For the linear system with nonlinear parametrization given in (9.56), under the assumptions (A2-1)±(A2-3), together with the identi®cation model in (9.62), the update law in (9.64) ensures that our parameter estimation problem has bounded errors in ~ p if pt 0 P. In addition, lim t3I e " t0. Proof The proof is omitted since it follows along the same lines as that of Theorem 4.1. We note that as in Section 9.4.2, the choices of a 0 and ! 0 are dierent depending on the nature of f . When f is concave or convex, these functions are simpler and easier to compute, and are given by Lemma 3.1. For general nonlinear parametrizations, these solutions depend on the concave cover and as can be seen from Lemma 3.4, are more complex to determine. Adaptive Control Systems 235 9.5 Applications 9.5.1 Application to a low-velocity friction model Friction models have been the focus of a number of studies from the time of Leonardo Da Vinci. Several parametric models have been suggested in the literature to quantify the nonlinear relationship between the dierent types of frictional force and velocities. One such model, proposed in [13] is of the form F F C sgn xF S À F C sgn xe À x v s 2 F v x 9:65 where x is the angular position of the motor shaft, F is the frictional force, F C represents the Coulomb friction, F S stands for static friction, F v is the viscous friction coecient, and v s is the Stribeck parameter. Another steady state friction model, proposed in [14] is of the form : F F C sgn x sgn xF S À F C 1 x=v s 2 F v x 9:66 Equations (9.65) and (9.66) show that while the parameters F C ; F S and F v appear linearly, v s appears nonlinearly. As pointed out in [14], these param- eters, including v s , depend on a number of operating conditions such as lubricant viscosity, contact geometry, surface ®nish and material properties. Frictional loading, usage, and environmental conditions introduce uncertain- ties in these parameters, and as a result these parameters have to be estimated. This naturally motivates adaptive control in the presence of linear and nonlinear parametrization. The algorithm suggested in Section 9.4.2 in this chapter is therefore apt for the adaptive control of machines with such nonlinear friction dynamics. In this section, we consider position control of a single mass system in the presence of frictional force F modelled as in equation (9.65). The underlying equations of motion can be written as x F u 9:67 where u is the control torque to be determined. A similar procedure to what follows can be adopted for the model in equation (9.66) as well. Denoting ' sgn x; x T ; f x;sgn xe À x 2 ;F C ; F v T 1=v 2 s ; F S À F C 9:68 it follows that the plant model is of the form x f x;' T u 9:69 236 Stable adaptive systems in the presence of nonlinear parametrization where f x; is convex for all x > 0 and concave for all x < 0. We choose a reference model as s 2 2! n s ! 2 n Âà x m ! 2 n r 9:70 where and ! n are positive values suitably chosen for the application problem. It therefore follows that a control input given by u Àke c À D 1 s x! 2 n r À ' T À f ; Àa 0 sat e c " 9:71 where D 1 s2! n s ! 2 n , together with the adaptive laws ~ À e " '; À > 0 9:72 ~ e " ! 0 ; > 0 9:73 ~ e " f ; > 0 9:74 with a 0 and ! 0 corresponding to the min±max solutions when signe " f is concave/convex, suce to establish asymptotic tracking. We now illustrate through numerical simulations the performance that can be obtained using such an adaptive controller. We also compare its perform- ance with other linear adaptive and nonlinear ®xed controllers. In all the simulations, the actual values of the parameters were chosen to be F C 1N; F S 1:5N; F v 0:4 Ns/m; v s 0:018 m/s 9:75 and the adaptive gains were set to À diag1; 2; 2; 10 8 9:76 The reference model was chosen as in equation (9.70) with = 0.707, ! n = 5 rad/s, r sin0:2t. Simulation 1 We ®rst simulated the closed loop system with our proposed controller. That is, the control input was chosen as in equation (9.71) with k 1, and adaptive laws as in equations (9.72)±(9.74) with " = 0.0001. 0 was set to 1370 corresponding to an initial estimate of v s = 0.027 m/s, which is 50% larger than the actual value. Figure 9.5 illustrates the tracking error, e x À x m ,the control input, u, and the error in the frictional force, e F F À F, where F is given by (9.65) and F is computed from (9.65) by replacing the true parameters with the estimated values. e, u and e F are displayed both over [0, 6 min] and [214 min, 220 min] to illustrate the nature of the convergence. We note that the position error converges to about 5  10 À5 rad, which is of the order of ", and e F to about 5  10 À3 N. The discontinuity in u is due to the signum function in f in (9.68). Adaptive Control Systems 237 Simulation 2 To better evaluate our controller's performance, we simulated another adaptive controller where the Stribeck eect is entirely neglected in the friction compensation. That is F F C sgn x F v x 9:77 so that the control input u Àke c À D 1 s x! 2 n r À F 9:78 with estimates F C and F v obtained using the linear adaptive laws as in [1]. As before, the variables e, u and e F are shown in Figure 9.6 for the ®rst 6 minutes as well as for T [214 min, 220 min]. As can be observed, the maximum position error does not decrease beyond 0.01 rad. It is worth noting that the control input in Figure 9.6 is similar to that in Figure 9.5 and of comparable 238 Stable adaptive systems in the presence of nonlinear parametrization (a) (b) (c) e (rad) u (N) e F (N) Figure 9.5 Nonlinear adaptive control using the proposed controller. (a) e vs. time, (b) u vs. time, (c) e F vs. time magnitude showing that our min±max algorithm does not have any discon- tinuities nor is it of a `high gain' nature. Note also that the error, e F does not decrease beyond 0.5 N. Simulation 3 In an attempt to avoid estimating the nonlinear parameters v s , in [15], a friction model which is linear-in-the-parameters was proposed. The frictional force is estimated in [15] as F F C sgn x F S j xj 1=2 sgn x F v x 9:79 with the argument that the square-root-velocity term can be used to closely match the friction-velocity curves and linear adaptive estimation methods similar to Simulation 2 were used to derive closed loop control. The resulting Adaptive Control Systems 239 (a) e (rad) u (N) e F (N) (b) (c) Figure 9.6 Linear adaptive control with the Stribeck eect neglected. (a) e vs. time, (b) u vs. time, (c) e F vs. time performance using such a friction estimate and the control input in equation (9.78) is shown in Figure 9.7 which illustrates the system variables e, u and e F for T [0, 6 min] and for T [360 min, 366 min]. Though the tracking error remains bounded, its magnitude is much larger than those in Figure 9.5 obtained using our controller. 9.5.2 Stirred tank reactors (STRs) Stirred tank reactors (STRs) are liquid medium chemical reactors of constant volume which are continuously stirred. Stirring drives the reactor medium to a uniform concentration of reactants, products and temperature. The stabiliza- tion of STRs to a ®xed operating temperature proves to be dicult because a 240 Stable adaptive systems in the presence of nonlinear parametrization (a) e (rad) u (N) e F (N) (b) (c) Figure 9.7 Linear adaptive control with friction model as in (9.79). (a) e vs. time, (b) u vs. time, (c) e F vs. time [...]... due to the signum function in f in (9.68) Stable adaptive systems in the presence of nonlinear parametrization e (rad) 238 u (N) (a) eF (N) (b) (c) Figure 9.5 Nonlinear adaptive control using the proposed controller (a) e vs time, (b) u vs time, (c) eF vs time Simulation 2 To better evaluate our controller's performance, we simulated another adaptive controller where the Stribeck eect is entirely neglected... curves and linear adaptive estimation methods similar to Simulation 2 were used to derive closed loop control The resulting Stable adaptive systems in the presence of nonlinear parametrization e (rad) 240 u (N) (a) eF (N) (b) (c) Figure 9.7 Linear adaptive control with friction model as in (9.79) (a) e vs time, (b) u vs time, (c) eF vs time performance using such a friction estimate and the control input... a certain threshold The adaptive controller de®ned by equations (9.85)±(9.90) guarantees the stability of the magnetic bearing system as shown by the simulations results in [11] 9.6 Conclusions In this chapter we have addressed the adaptive control problem when unknown parameters occur nonlinearly We have shown that the traditional gradient algorithm fails to stabilize the system in such a case and... to discrete-time systems as well How stable adaptive estimation can be carried out for NLP-systems has been addressed in [12] Unlike the manner in which the tuning functions are introduced in continuous-time systems considered in this chapter, in discretetime systems, the tuning function a0 is not included in the control input, but takes the form of a variable step-size t in the adaptive- law itself... heat transfer coecient, e, makes accurately predicting reaction rates nearly impossible To overcome this problem, an adaptive controller where 0 , 1 , h and e are unknown may be necessary 9.5.2.1 Adaptive control based on nonlinear parametrization The applicability of the adaptive controller discussed in Section 9.4.2 becomes apparent with the following de®nitions ! 1 1 0 ; 2 1 ; ; Ap... assures stable adaptive control of NLP-systems of the form in (9.3) with convergence of the errors to within a desired precision " The proof of boundedness follows using a key property of the proposed algorithm This corresponds to that of the error model discussed in Section 9.4.1, which is given by Lemma 3.2 As mentioned earlier, Lemma 3.2 is trivially satis®ed in adaptive control of LP-systems, where... these parameters have to be estimated This naturally motivates adaptive control in the presence of linear and nonlinear parametrization The algorithm suggested in Section 9.4.2 in this chapter is therefore apt for the adaptive control of machines with such nonlinear friction dynamics In this section, we consider position control of a single mass system in the presence of frictional force F modelled as in... numerical simulations the performance that can be obtained using such an adaptive controller We also compare its performance with other linear adaptive and nonlinear ®xed controllers In all the simulations, the actual values of the parameters were chosen to be FC 1 N; FS 1:5 N; Fv 0:4 Ns/m; vs 0:018 m/s
9:75 and the adaptive gains were set to À diag
1; 2; 2; 108
9:76 The reference... simulated the closed loop system with our proposed controller That is, the control input was chosen as in equation (9.71) with k 1, and adaptive laws as in equations (9.72)±(9.74) with " = 0.0001
0 was set to 1370 corresponding to an initial estimate of s = 0.027 m/s, which is v 50% larger than the actual value Figure 9.5 illustrates the tracking error, e x À xm ,the control input, u, and the... i;mx if i ! i;mx Simulation results have shown that our proposed controller performs better than a linear adaptive system based on linear approximations of the nonlinear plant dynamics Due to space constraints, however, the results are not shown in this chapter but the reader may refer to [11] for more details 9.5.3 Magnetic bearing system Magnetic bearings are currently used in various applications . since it leads to a closed loop system X p A m X p b m r Our discussions in Section 9.2 indicate that an adaptive version of the Adaptive Control Systems 231 controller in (9.42), with the. signum function in f in (9.68). Adaptive Control Systems 237 Simulation 2 To better evaluate our controller's performance, we simulated another adaptive controller where the Stribeck eect. linear adaptive estimation methods similar to Simulation 2 were used to derive closed loop control. The resulting Adaptive Control Systems 239 (a) e (rad) u (N) e F (N) (b) (c) Figure 9.6 Linear adaptive
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