# Controller Design for Polynomial Nonlinear Systems with Affine Uncertain Parameters

## Controller Design for Polynomial Nonlinear Systems with Affine Uncertain Parameters

Controller Design for Polynomial Nonlinear Systems with Aﬃne Uncertain Parameters TONG Chang-Fei1 ZHANG Hui1 SUN You-Xian1 Abstract By means of pol...

Controller Design for Polynomial Nonlinear Systems with Aﬃne Uncertain Parameters TONG Chang-Fei1

ZHANG Hui1

SUN You-Xian1

Abstract By means of polynomial decomposition, a control scheme for polynomial nonlinear systems with aﬃne time-varying uncertain parameters is presented. The idea of polynomial decomposition is to convert the coeﬃcients of polynomial into a matrix with free variables, so that the nonnegativity of polynomials with even orders can be checked by linear matrix inequality (LMI) solvers or bilinear matrix inequality (BMI) solvers. Control synthesis for polynomial nonlinear system is based on Lyapunov stability theorem in this paper. Constructing Lyapunov function and ﬁnding feedback controller are automatically ﬁnished by computer programming with algorithms given in this paper. For multidimension systems with relatively high-order controller, the controller constructed with full monomial base will be in numerous terms. To overcome this problem, the reduced-form controller with minimum monomial terms is derived by the proposed algorithm. Then a suboptimal control aiming at minimum cost performance with gain constraints is advanced. The control scheme achieves eﬀective performance as illustrated by numerical examples. Key words Nonlinear control, semideﬁnite programming relaxation, robust control

1

Introduction

The Lyapunov stability theorem has been a cornerstone for nonlinear system analysis for several decades. In princix) with equilibple, the theorem states that a system x˙ = f (x rium at the origin is stable if there exists a positive deﬁnite x) such that the derivative of V (x x) along the function V (x system trajectories is nonpositive. In recent years, considerable attention has been devoted to the study of polynomial nonlinear systems. Signiﬁcant progress has been made in the stability analysis of those systems by sum of squares decomposition approach[1∼4] . Stability analysis with this methodology is mainly based on Lyapunov stability theorem. Constructing Lyapunov functions is solved by SOSTOOLS which converts the problems into semidefinite programs (SDP) solved using SeDuMi which is a MATLAB toolbox for optimization over symmetric cones. Though stability analysis is solved eﬀectively to some extent, control synthesis for nonlinear systems still remains a stubborn problem since the nonlinear components of variables in the sum of squares (SOS) terms cannot be solved directly. To solve the synthesis problem, an iterative algorithm was proposed by Jarvis-Wloszek . However, the controller designed in  is not globally optimal; furthermore, the iterative algorithm may fail to get a solution in some cases, though the system may possess a stabilized controller. Polynomial nonlinear system with state feedback controller is generally depicted as follows. x) + g · u (x x) x˙ = f (x

criterion presented in  based on the so-called density function ρ has much better convexity properties. Then, Prajna exploited this criterion to solve control synthesis problems. Unfortunately, the convergence criterion via density function does not involve any information about convergence rate, so the controller designed by this scheme may have slow convergence in some instances. Furthermore, it is diﬃcult to apply density function to guaranteed cost control synthesis. Hence, it is a promising task to develop a tool for control synthesis of polynomial systems for guaranteed cost control. To the best of our knowledge, how to design an optimal controller under cost performance criterion with constrained gains remains an open problem. In this paper, we propose a novel algorithm for polynomial decomposition and exploit this methodology to study the suboptimal cost control of polynomial nonlinear systems with aﬃne time-varying uncertain parameters. Constructing Lyapunov function and ﬁnding suboptimal feedback controller are automatically ﬁnished by our software package based on algorithms proposed. In this paper, Rn denotes the set of all polynomials in n variables; Rrn the set of all polynomial vectors in n variables with r dimension; Rrn,d the set of all polynomial vectors in n variables with r dimension and the maximum degree of the elements in the vector is d; Z + the nonnegative integer set, Z + = Z + ∪ {0}; and 

(1)

x ) ∈ Rn x ) ∈ Rm where g ∈ Rn×m , f (x n , and u (x n are polynomials with f (00) = u (00) = 0 , and the equilibrium point is at origin x = 0 . The demerit of constructing Lyapunov function with SOSTOOLS is that there are two unknown polynomials combined together in nonlinear form when the set of (V, u ) satisﬁes u) < 0 ∇V · (ff + gu Given the diﬃculties with Lyapunov-based controller synthesis, it is most striking to ﬁnd that the new convergence Received November 21, 2006; in revised form May 25, 2007 Supported by National Natural Science Foundation of China (60674028, 60674071), the Key Technologies R & D Program of Zhejiang Province (2006C11066) 1. State Key Laboratory of Industrial Control Technology, Institute of Industrial Process Control, Zhejiang University, Hangzhou 310027, P. R. China DOI: 10.1360/aas-007-1321

c(n, r) =

2

r n



⎧ ⎪ ⎨

n! r! (n − r)! = 1 ⎪ ⎩ 0

r>0 r=0 r<0

Polynomial decomposition and nonnegativity validation

In this section, we set up a decomposition framework by giving several novel deﬁnitions to facilitate the expression of our decomposition algorithm. Then the nonnegativity validation problem for polynomials can be transmitted to semideﬁnite positivity tests of corresponding decomposed matrices. x) in n variables is a Deﬁnition 1. A monomial  mr r(x i x) = n function deﬁned as mr (x i=1 x i , for ri ∈ Z + , and the degree of monomial is deﬁned as deg(mr ) = n i=1 ri = r. x) ∈ R1n is a ﬁnite linear Deﬁnition 2. A polynomial f (x

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ACTA AUTOMATICA SINICA

combination of monomials, x), with x) = cr mr (x f (x

α) free variables in L(α α) with N (α α) calculated total of N (α as cr ∈ R

α) = 12 c(n + r, r)[c(n + r, r) + 1] − c(n + 2r, 2r) N (α

r

x) is denoted by deg(f ) = max deg(mr ). The degree of f (x r

Deﬁnition 3. x {r} is called the homogeneous monomial base of degree r, for x ∈ Rn . x {r} can be generated as follows. ⎡ r ⎤ x)]{0} x1 [E2 (x {1} ⎥ ⎢ xr−1 [E (x 2 x )] ⎢ 1 ⎥ ⎢ xr−2 [E (x {2} ⎥ 2 x )] ⎢ 1 ⎥ {r} ⎥ , with x {0} = 1 x =⎢ .. ⎢ ⎥ ⎢ ⎥ . ⎢ ⎥ ⎣ x1 [E2 (x x)]{r−1} ⎦ 0 {r} x)] x1 [E2 (x x) is a shift function that deletes the ﬁrst element of E2 (x x) = [x x2 , x 3 , · · · , x n ]T . The dimension of x {r} is x , i.e., E2 (x calculated using the following formula x{r} ) = c(n+r −1, r) = Dim(x

(n + r − 1)! , for x ∈ Rn (2) r!(n − 1)!

Deﬁnition 4. x |r| is called the full monomial base of degree r for x ∈ Rn . x |r| is generated from homogeneous monomial bases ⎡ {0} ⎤ x ⎢ x {1} ⎥ ⎢ ⎥ x |r| = ⎢ . ⎥ ⎣ .. ⎦ x {r} The dimension of x |r| is calculated by x|r| ) = c(n + r, r) = Dim(x

Vol. 33

(n + r)! , for x ∈ Rn r!n!

(3)

x) ∈ R1n,d can be written in the linear Any polynomial f (x form of full monomial base, i.e., x) = C x|d| , with C ∈ R1×c(n+d,d) f (x

(4)

x) is in even order, i.e., d = 2r for r ∈ Z + . Furthermore, if f (x It can be decomposed in quadratic form α)] · x |r| x) = (x x|r| )T · [Pf + L(α f (x

(5)

α) ∈ Rc(n+r,r)×c(n+r,r) are symmetric mawhere Pf and L(α α) · x|r| )T · L(α trices, and α is a free variable vector with (x |r| x = 0. Though decomposition for Pf is not unique, the set of decomposed matrices α)|(x x|r| )T · [Pf + L(α α)] · x |r| = f (x x), S(f ) = {Pf + L(α α) · x |r| = 0, f (x x) ∈ R1n,2r } (6) x|r| )T · L(α (x is unique and can be taken as the test set for nonnegativity x). check of f (x x)) ∈ Z 1×n Deﬁnition 5. J (m(x is called the expo+ x) for x ∈ Rn . If m(x x) = nent mapping of monomial m(x n ri i=1 x i , then J (m) = [r1 , r2 , · · · , rn ]. α) ∈ Rc(n+r,r)×c(n+r,r) Deﬁnition 6. M (f ) = Pf + L(α is called the quadratic decomposed matrix of polynomial x) ∈ R1n,2r , where Pf and L(α α) satisfy (5). There is a f (x

(7)

J ) : Z 1×n Deﬁnition 7. Loc (J → Z + is the index map+ x) with respect to J in the full monoping of monomial m(x J ) is calculated by mial base. Loc (J J) = Loc (J

1 + c(n + s − 1, s − 1)+ n−1  c(n − i + s−si − 1, s − si − 1)

(8)

i=1

i  where s = n i=1 ri , si = j=1 rj for J = [r1 , r2 , · · · , rn ]. J ) locates the index of m(x x) in the full monomial So Loc (J base x |r| . x) is Algorithm 1 (Polynomial decomposition). f (x x|2r| )i the ith element depicted in (4) and (5). Denote by (x in full monomial base x |2r| . C i is the ith element of C , and Pjk and Ljk are the elements in jth row and kth column of α), respectively. L(α α) is independent of the coefPf and L(α x), and can be generated by monomial ﬁcient array C of f (x base x |r| . Denote an indicator function by vij  vij = 2, for i = j (9) vij = 1, for i = j α). Set X = x |r| ·(x x|r| )T , i = 0, q = 1, Step 1. Generate L(α α) = 0 for initialization. Step 1 is accomplished in and L(α the following three substeps. α)), go Substep 1.1 Set i = i + 1, j = i. If i > Dim(L(α to Step 2; else, go to Substep 1.2. Substep 1.2 If Lij = 0, go to Substep 1.3; else, set t = 0, ﬁnd out all the elements equal to Xij in the upper triangular part of X and denote the elements set by {Xkl }. If {Xkl } is not empty, set Lkl = vklα q , t = t + α q , and increase q by 1 for every Xkl in the set. Finally, set Lij = Lji = −vij t and go to Substep 1.3. Substep 1.3 Set j = j +1. If j > Dim(L), go to Substep 1.1; else, go to Substep 1.2. Step 2. Construct Pf . Set Pf = 0 for initialization. For x|2r| )i ), every coeﬃcient C i in C , if C i = 0, set J i = J ((x (1) (2) (1) J i /2), and J i = J i − J i for pre-procedure. J i = ﬂoor(J The decomposition of J i is taken in three substeps. J (2) Substep 2.1 Set t = sum(J i ) − r. If t ≤ 0, go to Substep 2.3, else go to Substep 2.2. (2) (1) Substep 2.2 Set J e = J i − J i . Denote the mth element of J e by J e (m), for m from 1 to n. If J e (m) > 0, (1) (1) set J i (m) = J i (m) + 1 and t = t − 1. If t ≤ 0, reset (2) (1) J i = J i − J i and go to Substep 2.3. J (1) J (2) Substep 2.3 Set k = Loc (J i ) and l = Loc (J i ) which are indices of the two decomposed monomials in full monomial base x |r| , and ﬁnally set Pkl = Plk = vklC i /2. When all coeﬃcients C i s are processed and Pf is constructed, go to Step 3. α). Step 3. Set M (f ) = Pf + L(α x) ∈ R1n,2r , the suﬃcient condiLemma 1 . Given f (x x) ≥ 0, ∀x x ∈ Rn is that, f (x x) can be rewritten tion for f (x  x). The necx) = ki=1 fi2 (x in the sum of squares form: f (x essary condition also holds in following three cases: 1) n = 2; 2) r = 1; 3) n = 3, r = 2. x) ∈ R1n,2r , the following stateTheorem 1. Given f (x ments are equivalent:

TONG Chang-Fei et al.: Controller Design for Polynomial Nonlinear Systems · · ·

No. 12

x) can be rewritten in the sum of squares form: 1) f (x  x). x) = ki=1 fi2 (x f (x α) α) ≥ 2) There exists α ∈ RN (α such that M (f ) = Pf +L(α 0. x)) = 2r, and Proof. Statement 1) to 2). Since deg(f (x x)) ≤ r for any fi (x x), it can be written in the linear deg(fi (x x) = C i x|r| , C i ∈ R1×c(n+r,r) . Thus, form of x |r| , i.e., fi (x one gets k x) = (x x|r| )T ( x|r| f (x CT i C i )x i=1

k

T i=1 C i C i

By (6) and Deﬁnition 6, ≥ 0 ∈ S(f ). Statement 2) is derived. Statement 2) to 1). If statement 2) holds, from singular value decomposition, one gets M (f ) = U T ΛU . Choose 1

x) = (Λ 2 Ux x|r| )i . Then statement 1) is obtained. fi (x  From Lemma 1 and Theorem 1, one can see that, the x) can be relaxed to matrix inpositivity validation of f (x equality problem, which can be solved numerically. There is no relaxation gap in the three cases as mentioned in Lemma 1.

3

Suboptimal cost control

In this section, we give the design of cost control via polynomial decomposition approach, and the designed controller is aimed at minimum average cost performance with gain constraints. Consider the polynomial nonlinear system with aﬃne uncertainties described, namely, x, δ , u ) = f 0 (x x)+ x˙ = f (x

l

x)+ δ if i (x

i=1

k

x) (10) δ i gi−lu (x

i=l+1

x ) ∈ Rn x ) ∈ Rm where gi ∈ Rn×m , f i (x n , and u (x n with f (0, δ , 0) = 0. The uncertain parameters are deﬁned as δ i ∈ + ˙ ˙− ˙+ [δδ − i , δ i ] with bounded time varying rates as δ i ∈ [δ i , δ i ]. The set of uncertain parameters can be presented in polytopic form ∆(δδ ) × Λ(δ˙ ), where ∆(δδ ) and Λ(δ˙ ) are convex hull of δ and δ˙ , respectively. Deﬁne ∆0 (δδ ) and Λ0 (δ˙ ) by + ∆0 (δδ ) = {col(δδ 1 , δ 2 , · · · , δ k )|δδ i ∈ {δδ − i , δ i }, i = 1, 2, · · · , k} −

+

Λ0 (δ˙ ) = {col(δ˙ 1 , δ˙ 2 , · · · , δ˙ k )|δ˙ i ∈ {δ˙ i , δ˙ i }, i = 1, 2, · · · , k} ∆(δδ ) can be generated by ∆0 (δδ ) ∆(δδ ) = {δδ =

2k j=1

λj δ (j) |λj ≥ 0, δ (j) ∈ ∆0 (δδ ),

j = 1, 2, · · · , 2k ,

2k j=1

λj = 1}

Λ(δ˙ ) can also be generated by Λ0 (δ˙ ) in the same way. We call ∆0 (δδ ) and Λ0 (δ˙ ) vertices sets of ∆(δδ ) and Λ(δ˙ ). One can see that they are with ﬁnite elements. Then, a problem with set ∆(δδ )×Λ(δ˙ ) can be tested by ∆0 (δδ )×Λ0 (δ˙ ) when it is convex with respect to δ and δ˙ , which converts an inﬁnite test problem into a ﬁnite one. Theorem 2. Consider the nonlinear system described x) = in (10). If there exists a state feedback controller u (x u 1 , u 2 , · · · , u m ]T ∈ Rm x) = [u n,d with u (0) = 0 and u i (x x|d| ) for i = 1, 2, · · · , m, a parameter-dependent LyaK i E2 (x x with P (δδ ) = P0 + x, δ ) = x T P (δδ )x punov function V (x

k

δ

i=1 δ i Pi ,

1323

such that P (δδ (j) ) > 0, j = 1, 2, · · · , 2k

xT P (δδ (j) )ff (x x, δ (j) , u ) + x T Rx x+ M [2x

k

(11)

(t) δ˙ i x T Pix ] ≤ 0,

i=1

j = 1, 2, · · · , 2k , t = 1, 2, · · · , 2k (12) x)] ≥ 0, i = 1, 2, · · · , l xT Pif i (x (13) M [x xT Pi gi−lu (x x)] ≥ 0, i = l + 1, l + 2, · · · , k M [x

(14)

(t)

∈ Λ0 (δ˙ ), and Pi s are symmetric where δ (j) ∈ ∆0 (δδ ), δ˙ matrices for i = 0, 1, · · · , k, then, system (10) is globally stable and converges to the origin with guaranteed cost performance  ∞ x(t)dt ≤ x (t0 )T P (δδ )x x(t0 ) x (t)T Rx (15) t0

Proof. −P (δδ ) is convex for δ since it is aﬃne with δ ; thus, −P (δδ ) < 0 can be tested in vertices set ∆0 (δδ ). Hence, x, δ ) and (11), we get, for any δ ∈ from the deﬁnition of V (x x, δ ) = 0 only when x = 0, and V (x x, δ ) > 0 with ∆(δδ ), V (x x) → ∞ for any x ∈ Rn /{0}. Diﬀerentiating lim V (x

x||→∞ ||x

x, δ ) along the system trajectory of (10), we have V (x x, δ ) = 2x xT P (δδ )ff (x x, δ , u ) + V˙ (x

k

δ˙ ix T Pix

i=1

x, δ ) is convex for δ and δ˙ ; thus By (13) and (14), V˙ (x T ˙ x, δ ) + x Rx x ≤ 0 can be tested by (12), which yields V (x (15) when integrated from t0 to inﬁnity.  In practice, we modify condition (11) to P (δδ (j) ) ≥ ε with ε ≥ 0.001 to avoid numerical problems. For example, for P (δδ ) > 0, if all eigenvalues of P (δδ ) are smaller than 10−9 , condition (12) may lose constraint eﬀect by solvers under the tolerance of 10−9 and produce wrong results. Usually, x) is chosen, where the degree d in u (x  deg(ff ), for deg(ff ) is odd (16) d= deg(ff ) + 1, for deg(ff ) is even The controller designed by Theorem 2 usually has numerous monomials in practice. Denote the number of monomix) by N (u u). Then, it is calculated as follows. als in u (x u) = −m + m N (u

(n + d)! n!d!

(17)

u) = 19 when x ∈ R3 and u ∈ R13,3 , which For example, N (u means that there are 19 monomials in the single dimension controller of 3 states feedback when the degree is 3. Actually, some monomial terms in the controller contribute little to the stabilization performance and can be removed. However, how to ﬁnd out the redundant terms is still a nonconvex problem as mentioned in . In this paper, we minx), which imize the norm 1 of the coeﬃcients (gains) in u (x approximately tries to minimize the number of nonzero terms. The reason of norm 1 approximation is that, for amplitude distribution of the optimal residual, it tends to have more zeroes and very small residuals compared to norm 2 approximation solution. For optimal control, we aim at ∞ x(t)dt, which is reminimizing the cost function t0 x (t)T Rx laxed to minimization of the corresponding upper bound

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ACTA AUTOMATICA SINICA

x(t0 )T P (δδ )x x(t0 ) in our control scheme. (15) emphasizes the dependence of the performance criterion on x (t0 ) and P (δδ ). In order to ﬁnd an optimal control using the cost performance criterion, usually, it is necessary to eliminate the dependence on x (t0 ). Mathematically, a simple way to deal with this problem is to average the performance obtained for a linearly independent set of initial states, i.e., to assume the initial states to be random variables uniformly distributed on the surface of the n-dimensional unit sphere x(t0 )x x(t0 )T ] = In . Then, we have with E[x x(t0 )] x(t0 )T P (δδ )x E[x

=

x(t0 )x x(t0 )T )] = E[tr(P (δδ )x T

x(t0 ) ]) = x(t0 )x tr(E[P (δδ )x E[tr(P (δδ ))] Finally, minimizing cost performance is relaxed to minimiz k ing 21k 2j=1 tr(P (δδ (j) )) with δ (j) ∈ ∆0 (δδ ). The detailed algorithm for control synthesis is presented in Algorithm 2. Algorithm 2 (Control synthesis). We exploit PENBMI with YALMIP interface to solve the optimization problem derived from polynomial decomposition. The controller design algorithm is processed in the following two steps. Step 1. Set appropriate values for ε, and τlim which is a limit gate for monomial removals, and then, solve the following optimization problem:

Vol. 33

where δ1 ∈ [0.5, 1.5] with δ˙1 ∈ [−0.5, 0.5], δ2 ∈ [1.0, 2.0] with δ˙2 ∈ [−0.5, 0.5], and δ3 ∈ [0.9, 1.1] with δ˙3 ∈ x|3| ), which has x) = K E2 (x [−0.01, 0.01]. We pick up u(x x). Choose R = I2 . Then the cost the same order as f (x function is l2 norm of x (t). Setting ε = 0.01 and τlim = 0.1, x) and u x) in Step 1 as and using Algorithm 2, we get u(x ˜(x follows x) = −1.451x1 − 2.4876x2 − 2.865x21 − 0.014318x22 + u(x 1.3305 × 10−16 x1 x2 − 0.18563x31 − 0.0539x32 + 6.8569 × 10−15 x1 x22 − 1.6481 × 10−16 x21 x2 x) = k1 x1 + k2 x2 + k3 x21 + k4 x31 u ˜(x If the control gains are constrained as −20 ≤ ki ≤ 20, for i = 1, 2, 3, 4, i.e., T = −20 and T¯ = 20, then, we get the results as follows     6.8267 0.3179 −3.0034 −0.1156 P0 = , P1 = 0.3179 0.0669 −0.1156 0.0156     0.0000 −0.0000 −0.0071 0.0000 P2 = , P3 = −0.0000 −0.0051 0.0000 0.0000 3 x, δ ) = x T (P0 + x V (x δi Pi )x i=1

x) = −20x1 − 14.2099x2 − 20x21 + 1.9549x31 u∗ (x 8 1 tr(P (δδ (j) )) = 3.8981 8 j=1

min

δ ),K,α α P (δ

s.t. 1) 2)



Ni m  

Bij , Ni = c(n + d, d) − 1

i=1 j=1

−Bij ≤ Kij ≤ Bij Bij≥0

(11) ∼ (14)

i = 1, 2, · · · , m j = 1, 2, · · · , Ni

hold.

x). Remove where Kij s are coeﬃcients of monomials in u (x the corresponding monomial term when |Kij | ≤ τlim in x). Construct a reduced controller with the the solved u (x ˜ (x x) with u ˜ i (x x) = remaining monomials and denoted it by u li x j=1 Kij mij (x ) for i = 1, 2, · · · , m, where li is the number ˜ i (x x). of monomials in u ˜ (x x) for u (x x) and solve the following Step 2. Substitute u optimization problem: min

δ ),K,α α P (δ

1 2k

2k 

Pick up δ1 = 1 + 0.5 sin(t), δ2 = 1.5 + 0.5 cos(t), and δ3 = 1 + 0.1 sin(0.1t) in our numerical simulation. The phase x) is illustrated portrait of the closed loop system with u ˜∗ (x in Fig. 1. The sizes of arrow lines in Fig.1 are proportional to ||x˙ (t)||22 , which signify the convergence rate of x (t). All trajectories converge to the origin as shown in the ﬁgure. Choose initial states on the unit circle plane, e.g., x (t0 ) = [sin( k8 π), cos( k8 π)], for k = 0, 1, · · · , 15. The evolutions of the closed loop system are shown in Fig. 2.

tr(P (δδ (j) ))

j=1

s.t. 1)

T ij ≤ Kij ≤ T¯ij , i = 1, · · · , m, j = 1, · · · , li

2)

(11) ∼ (14)hold.

Fig. 1

Phase portrait of the closed loop system in Example 1

Fig. 2

Evolutions of the closed loop system with initial states on the unit circle plane in Example 1

where T ij s and T¯ij s are the lower and upper bounds constraints for Kij s, respectively, which are given in speciﬁc systems to meet physical limitations or other restrictions. The cost performance in (15) cannot be minimized directly. Our scheme is to minimize the average upper bound ˜ ∗ (x x) in Algorithm 2 is a subcost. In this sense, the solved u optimal solution.

4

Numerical examples Example 1. Consider the polynomial described system  x˙ 1 = x2 − δ1 x31 + δ2 x21 (18) x˙ 2 = δ3 u

TONG Chang-Fei et al.: Controller Design for Polynomial Nonlinear Systems · · ·

No. 12

Example 2. Consider the following nonlinear system  x˙ 1 = 2 sin x2 (19) x˙ 2 = x21 + u We get 2-order Taylor series approximation to sin x2 at zero as follows sin x2 = x2 + R2 (x2 ) where R2 (x2 ) is the residual series of sin x2 with an order greater than 2. From Lagrangian mean-value theorem, (3) R2 (x2 ) = sin 3! x2 |x2 =ξ ·x32 = − 16 sin ξ ·x32 , with 0 < ξ < x2 . Take ξ = θx2 , for 0 < θ < 1, and choose δ = sin(θx2 ). Then system (19) can be approximated as  x˙ 1 = 2x2 − 13 δx32 (20) x˙ 2 = x21 + u with δ ∈ [−1, 1] and δ˙ ∈ (−1, 1). Choose R = I2 , deg(u) = 3, ε = 0.01, τlim = 0.001, T = −20, and T¯ = 20, applying Algorithm 2, then we get the suboptimal cost controller x) = −8.9918x1 −20x2 −x21 −0.051532x21 x2 −20x32 (21) u∗ (x The phase portrait and evolutions of closed loop system (19) are illustrated in Figs 3 and 4, respectively.

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on minimum norm 1 of controller gains and subsequently optimizing on minimal average trace of P (δδ ) at the vertices of ∆(δδ ). Numerical examples show that the proposed control scheme exhibits eﬀective performance for polynomial nonlinear systems with aﬃne uncertain parameters. References 1 Parrilo P A. Structured Semideﬁnite Programs and Semialgebraic Geometry Methods in Robustness and Optimization [Ph. D. dissertation], California Institute of Technology, USA 2000 2 Parrilo P A. Nonlinear stability via sum of squares programming. In: Proceedings of the 2004 American Control Conference. Boston, USA: IEEE, 2004. 3272 3 Papachristodoulou A. Analysis of nonlinear time-delay systems using the sum of squares decomposition. In: Proceedings of the 2004 American Control Conference. Boston, USA: IEEE, 2004. 4153∼4158 4 Papachristodoulou A, Prajna S. A tutorial on sum of squares techniques for systems analysis. In: Proceedings of the 2005 American Control Conference. Portland, USA: IEEE, 2005. 2686∼2700 5 Sturm J F. A matlab toolbox for optimization over symmetric cones. Optimization Methods and Software, 1999, 11(14): 625∼653 6 Jarvis-Wloszek Z W. Lyapunov Based Analysis and Controller Synthesis for Polynomial Systems Using Sum-ofSquares Optimization [Ph. D. dissertation], University of California, 2003 7 Rantzer A. A dual to Lyapunov s stability theorem. Systems and Control Letters, 2001, 42: 161∼168 8 Prajna S, Parrilo P A, Rantzer A. Nonlinear control synthesis by convex optimization. IEEE Transactions on Automatic Control, 2004, 49(2): 310∼314 9 Parrilo P A. Semideﬁnite programming relaxations for semialgebraic problems. Mathematical Programming Series B, 2003, 96(2): 293∼320

Fig. 3

Phase portrait of the closed loop system (19) in Example 2

10 Hassibi A, How J P, Boyd S. Low-authority controller design via convex optimization. Journal of Guidance, Control and Dynamics, 1999, 22(6): 862∼872 11 Henrion D, Lofberg J, Kocvara M, Stingl M. Solving molynomial static output feedback problems with penbmi. In: Proceedings of IEEE Conference on Decision and Control. USA: IEEE, 2005. 7581∼7586 12 Lofberg J. YALMIP : a toolbox for modeling and optimization in MATLAB. In: Proceedings of International Symposium on Computer Aided Control System Design. Taiwan, China: IEEE, 2004. 284∼289

Fig. 4

5

Evolutions of the closed loop system (19) with initial states on the unit circle plane in Example 2

Conclusion

This paper presents an algorithm for polynomial decomposition, which can eﬃciently check the nonnegativity of polynomials with high orders. The proposed decomposition method is exploited for control synthesis of polynomial nonlinear systems with constrained controller gains. The state feedback control law underlying cost performance with minimum nonzero monomial terms is obtained via optimizing

TONG Chang-Fei Ph. D. candidate at Institute of Industrial Process Control of Zhejiang University. His research interest covers robust control and nonlinear control. Corresponding author of this paper. E-mail: [email protected] ZHANG Hui Received his Ph. D. degree from Zhejiang University in 2004. He is now an associate professor, Zhejiang University. His research interest covers stochastic systems and information theoretical approaches to control systems. E-mail: [email protected] SUN You-Xian Professor in Department of Control Science and Engineering at Zhejiang University, the academician of Chinese Academy of Engineering. His research interest covers process control, robust control theory and its application, fault tolerance control theory and its application, and modelling and computer control of pulp and paper-making processes.