Open access peer-reviewed chapter

Robust H Fuzzy Control Design for Nonlinear Two-Time Scale System with Markovian Jumps based on LMI Approach

By Wudhichai Assawinchaichote, Sing Kiong Nguang and Non-members

Published: October 1st 2009

DOI: 10.5772/8230

Downloaded: 1048

1. Introduction

Markovian jump systems consists of two components; i.e., the state (differential equation) and the mode (Markov process). The Markovian jump system changes abruptly from one mode to another mode caused by some phenomenon such as environmental disturbances, changing subsystem interconnections and fast variations in the operating point of the system plant, etc. The switching between modes is governed by a Markov process with the discrete and finite state space. Over the past few decades, the Markovian jump systems have been extensively studied by many researchers; see (Kushner, 1967; Dynkin, 1965; Wonham, 1968; Feng et al., 1992; Souza & Fragoso, 1993; Boukas & Liu, 2001 ; Boukas & Yang, 1999; Rami & Ghaoui, 1995; Shi & Boukas, 1997; Benjelloun et al., 1997; Boukas et al., 2001 ; Dragan et al., 1999). This is due to the fact that jumping systems have been a subject of the great practical importance. For the past three decades, singularly perturbed systems have been intensively studied by many researchers; see (Dragan et al., 1999; Pan & Basar, 1993; Pan & Basar, 1994; Fridman, 2001; Shi & Dragan, 1999; Kokotovic et al., 1986).

Multiple time-scale dynamic systems also known as singularly perturbed systems normally occur due to the presence of small “parasitic” parameters, typically small time constants, masses, etc. In state space, such systems are commonly modelled using the mathematical framework of singular perturbations, with a small parameter, say ε, determining the degree of separation between the “slow” and “fast” modes of the system. However, it is necessary to note that it is possible to solve the singularly perturbed systems without separating between slow and fast mode subsystems. But the requirement is that the “parasitic” parameters must be large enough. In the case of having very small “parasitic” parameters which normally occur in the description of various physical phenomena, a popular approach adopted to handle these systems is based on the so-called reduction technique. According to this technique the fast variables are replaced by their steady states obtained with “frozen” slow variables and controls, and the slow dynamics is approximated by the corresponding reduced order system. This time-scale is asymptotic, that is, exact in the limit, as the ratio of the speeds of the slow versus the fast dynamics tends to zero.

In the last few years, the research on singularly perturbed systems in the H sense has been highly recognized in control area due to the great practical importance. H optimal control of singularly perturbed linear systems under either perfect state measurements or imperfect state measurements has been investigated via differential game theoretic approach. Although many researchers have studied the H control design of linear singularly perturbed systems for many years, the H control design of nonlinear singularly perturbed systems remains as an open research area. This is due to, in general, nonlinear singularly perturbed systems can not be decomposed into slow and fast subsystems.

Recently, a great amount of effort has been made on the design of fuzzy H for a class of nonlinear systems which can be represented by a Takagi-Sugeno (TS) fuzzy model; see (Nguang & Shi, 2001; Han & Feng, 1998; Chen et al., 2000; Tanaka et al., 1996). Recent studies (Nguang & Shi, 2001; Han & Feng, 1998; Chen et al., 2000; Tanaka et al., 1996; Wang et al., 1996) show that a fuzzy model can be used to approximate global behaviors of a highly complex nonlinear system. In this fuzzy model, local dynamics in different state space regions are represented by local linear systems. The overall model of the system is obtained by “blending” of these linear models through nonlinear fuzzy membership functions. Unlike conventional modelling which uses a single model to describe the global behavior of a system, fuzzy modelling is essentially a multi-model approach in which simple submodels (linear models) are combined to describe the global behavior of the system. Employing the existing fuzzy results (Nguang & Shi, 2001; Han & Feng, 1998; Chen et al., 2000; Tanaka et al., 1996; Wang et al., 1996) on the singularly perturbed system, one ends up with a family of ill-conditioned linear matrix inequalities resulting from the interaction of slow and fast dynamic modes. In general, ill-conditioned linear matrix inequalities are very difficult to solve.

What we intend to do in this chapter is to design a robust H fuzzy controller for a class of uncertain nonlinear two time-scale dynamic systems with Markovian jumps. First, we approximate this class of uncertain nonlinear two time-scale dynamic systems with Markovian jumps by a Takagi-Sugeno fuzzy model with Markovian jumps. Then based on an LMI approach, we develop a technique for designing a robust H fuzzy controller such that the L 2-gain of the mapping from the exogenous input noise to the regulated output is less than a prescribed value. To alleviate the ill-conditioned linear matrix inequalities resulting from the interaction of slow and fast dynamic modes, these ill-conditioned LMIs are decomposed into ε-independent LMIs and ε-dependent LMIs. The ε-independent LMIs are not ill-conditioned and the ε-dependent LMIs tend to zero when ε approaches to zero. If ε is sufficiently small, the original ill-conditioned LMIs are solvable if and only if the ε-independent LMIs are solvable. The proposed approach does not involve the separation of states into slow and fast ones, and it can be applied not only to standard, but also to nonstandard singularly perturbed systems.

This chapter is organized as follows. In Section 2, system descriptions and definition are presented. In Section 3, based on an LMI approach, we develop a technique for designing a robust H fuzzy controller such that the L 2-gain of the mapping from the exogenous input noise to the regulated output is less than a prescribed value for the system described in Section 2. The validity of this approach is demonstrated by an example from a literature in Section 4. Finally, conclusions are given in Section 5.

2. System Descriptions and Definitions

The class of uncertain nonlinear two time-scale dynamic systems with Markovian jumps under consideration is described by the following TS fuzzy model with Markovian jumps:

Eεx˙(t)=i=1rμi(υ(t))[[Ai(η(t))+ΔAi(η(t))]x(t)+[B1i(η(t))+ΔB1i(η(t))]ω(t)+[B2i(η(t))+ΔB2i(η(t))]u(t)]x(0)=0z(t)=i=1rμi(υ(t))[[C1i(η(t))+ΔC1i(η(t))]x(t)+[D12i(η(t))+ΔD12i(η(t))]u(t)]E1

whereEε=[I00εI]ε0is the singular perturbation parameter,υ(t)=[υ1(t)υϑ(t)]is the premise variable that may depend on states in many cases,μi(υ(t))denote the normalized time varying fuzzy weighting functions for each rule, ϑ is the number of fuzzy sets,x(t)nis the state vectoru(t)mis the input,w(t)pis the disturbance which belongs to L 2[0,∞),z(t)sis the controlled output, the matrix functionsAi(η(t))B1i(η(t))B2i(η(t))C1i(η(t))D12i(η(t))ΔAi(η(t))ΔB1i(η(t))ΔB2i(η(t))ΔC1i(η(t))andΔD12i(η(t))are of appropriate dimensions.{(η(t))}is a continuous-time discrete-state Markov process taking values in a finite set S = {1,2, , s} with transition probability matrixPrΔ__{Pιk(t)}given by

Pιk(t)=Pr(η(t+Δ)=k|η(t)=ι)={λιkΔ+O(Δ)ifιk1+λιιΔ+O(Δ)ifι=kE2

where Δ > 0, andlimΔ0O(Δ)Δ=0. Hereλιk0is the transition rate from mode ι (system operating mode) to modek(ιk), and

λιι=k=1,kιsλιkE3

For the convenience of notations, we letμiΔ__μi(υ(t))η=η(t)and any matrixM(μι)Δ__M(μη=ι). The matrix functionsΔAi(η)ΔB1i(η)ΔB2i(η)ΔC1i(η)andΔD12i(η)re-present the time-varying uncertainties in the system and satisfy the following assumption.

Assumption 1:

ΔAi(η)=F(x(t)ηt)H1i(η)ΔB1i(η)=F(x(t)ηt)H2i(η)ΔB2i(η)=F(x(t)ηt)H3i(η)ΔC1i(η)=F(x(t)ηt)H4i(η)

andΔD12i(η)=F(x(t)ηt)H5i(η)

whereHji(η)j=1,2,,5are known matrices which characterize the structure of the uncertainties. Furthermore, there exists a positive function ρ(η) such that the following inequality holds:

F(x(t)ηt)ρ(η)E4

We recall the following definition.

Definition 1: Suppose γ is a given positive number. A system of the form (1) is said to have the L 2-gain less than or equal to γ if

Ε[0Tf{zT(t)z(t)γ2wT(t)w(t)}dt]0x(0)=0E5

whereΕ[]stands for the mathematical expectation, for all Tf and all w(t) ∈ L 2 [0, Tf].

Note that for the symmetric block matrices, we use (*) as an ellipsis for terms that are induced by symmetry.

3. Robust H Fuzzy Control Design

This section provides the LMI-based solutions to the problem of designing a robust H fuzzy controller that guarantees the L 2-gain of the mapping from the exogenous input noise to the regulated output to be less than some prescribed value.

First, we consider the following H∞ fuzzy state feedback which is inferred as the weighted average of the local models of the form:

u(t)=j=1rμjKj(l)x(t)E6

Then, we describe the problem under our study as follows.

Problem Formulation: Given a prescribed H performance γ > 0, design a robust H fuzzy state-feedback controller of the form (6) such that the inequality (5) holds.

Before presenting our first main result, the following lemma is needed.

Lemma 1: Consider the system (1). Given a prescribed H performance γ > 0, for ι = 1, 2, , s, if there exist matricesPε(ι)=PεT(ι), positive constants δ(ι) and matrices Yj (ι), j = 1, 2, , r such that the following ε-dependent linear matrix inequalities hold:

Pε(ι)0E7
ψii(ιε)0,i=1,2,rE8
ψij(ιε)+ψji(ιε)0,ijrE9

where

Ψij(ιε)=(Φij(ιε)(*)T(*)T(*)TR(ι)B1iT(ι)γR(ι)(*)T(*)Tφij(ιε)0γR(ι)(*)TZT(ιε)00P(ι,ε))E10
Φij(ιε)=Ai(ι)Eε1Pε(ι)+Eε1Pε(ι)AiT(ι)+B2i(ι)Yj(ι)+YjT(ι)B2iT(ι)+λιιEε1Pε(ι)E11
ϕij(ιε)=C˜1i(ι)Eε1Pε(ι)+D˜12i(ι)Yj(ι)E12
R(ι)=diag{δ(ι)IIδ(ι)II}E13
Z(ι,ε)=(λι1Eε1Pε(ι)λι(ι1)Eε1Pε(ι)λι(ι+1)Eε1Pε(ι)λιsEε1Pε(ι))E14
P(ι,ε)=diag{Eε1Pε(1)Eε1Pε(ι1)Eε1Pε(ι+1)Eε1Pε(s)}E15

with

B˜1i(ι)=[IIIB1i(ι)]C˜1i(ι)=[γρ(ι)H1iT(ι)2(ι)ρ(ι)H4iT(ι)02(ι)C1iT(ι)]TD˜12i(ι)=[02(ι)ρ(ι)H5iT(ι)γρ(ι)H3iT(ι)2(ι)D12iT(ι)]T(ι)=(I+ρ2(ι)i=1rj=1r[H2iT(ι)H2j(ι)])12

then the inequality (5) holds. Furthermore, a suitable choice of the fuzzy controller is

u(t)=j=1rμjKεj(ι)x(t)E16

where

Kεj(ι)=Yj(ι)(Pε(ι))1EεE17

Proof: The closed-loop state space form of the fuzzy system model (1) with the controller (16) is given by

Eεx˙(t)=i=1rj=1rμiμj([Ai(ι)+B2i(ι)Kεj(ι)]x(t)+[ΔAi(ι)+ΔB2i(ι)Kεj(ι)]x(t)+[B1i(ι)+ΔB1i(ι)]w(t))x(0)=0E18

or in a more compact form

Eεx˙(t)=i=1rj=1rμiμj([Ai(ι)+B2i(ι)Kεj(ι)]x(t)+B˜1i(ι)R(ι)w˜(t))x(0)=0E19

where

B˜1i(ι)=[IIIB1i(ι)]E20
w˜(t)=R-1(ι)[F(x(t)ιt)H1i(ι)x(t)F(x(t)ιt)H2i(ι)w(t)F(x(t)ιt)H3i(ι)Kεj(ι)x(t)w(t)]E21

Consider a Lyapunov functional candidate as follows:

V(x(t)ιε)=γxT(t)Qε(ι)x(t)ιSE22

Note that (ι) is constant for eachι. For this choice, we have V(0, ι 0 , ε) = 0 and V(x(t),ι,ε)→∞ only when ||x(t)|| → ∞.

Now let consider the weak infinitesimal operatorΔ˜of the joint process {(x(t), ı, ε), t ≥ 0}, which is the stochastic analog of the deterministic derivative. {(x(t), ı, ε), t ≥ 0} is a Markov process with infinitesimal operator given by (Souza & Fragoso, 1993),

Δ˜V(x(t)ιε)=γx˙T(t)Qε(ι)x(t)+γxT(t)Qε(ι)x˙(t)+γxT(t)k=1sλιkQε(k)x(t)=i=1rj=1rμiμj(γxT(t)Qε(ι)[(Ai(ι)+B2i(ι)Kεj(ι))]x(t)+γxT(t)[Ai(ι)+B2i(ι)Kεj(ι)]TQε(ι)x(t)+γxT(t)Qε(ι)B˜1i(ι)R(ι)w˜(t)+γw˜T(t)R(ι)B˜1iT(ι)Qε(ι)x(t)+γxT(t)k=1sλιkQε(k)x(t))E23

Adding and subtracting2(ι)zT(t)z(t)+γ2i=1rj=1rm=1rn=1rμiμjμmμn[w˜T(t)R(ι)w˜(t)]to and from (23), we get

Δ˜V(x(t)ιε)=2(ι)zT(t)z(t)+γ2i=1rj=1rm=1rn=1rμiμjμmμn[w˜T(t)R(ι)w˜(t)]+2(ι)zT(t)z(t)+γ2i=1rj=1rm=1rn=1rμiμjμmμn[x(t)w˜(t)]T×(([Ai(ι)+B2i(ι)Kεj(ι)]TQε(ι)Qε(ι)[Ai(ι)+B2i(ι)Kεj(ι)]+k=1sλιkQε(k))(*)TR(ι)B˜1iT(ι)Qε(ι)γR(ι))[x(t)w˜(t)]E24

Now let us consider the following terms:

γ2i=1rj=1rm=1rn=1rμiμjμmμn[w˜T(t)R(ι)w˜(t)]ρ2(ι)γ2δ(ι)i=1rj=1rm=1rn=1rμiμjμmμnxT(t)×{H1iT(ι)H1m(ι)+KεjT(ι)H3iT(ι)H3m(ι)Kεn(ι)}x(t)+2(ι)γ2wT(t)w(t)E25

and

2(ι)zT(t)z(t)22(ι)i=1rj=1rm=1rn=1rμiμjμmμnxT(t){[C1i(ι)+D12i(ι)Kεj(ι)]T×[C1m(ι)+D12m(ι)Kεn(ι)]+ρ2(ι)[H4i(ι)+H5i(ι)Kεj(ι)]T×[H4m(ι)+H5m(ι)Kεn(ι)]}x(t)E26

where(ι)=(1+ρ2(ι)i=1rj=1r[H2iT(ι)H2j(ι)])12Hence,

γ2i=1rj=1rm=1rn=1rμiμjμmμn[w˜T(t)R(ι)w˜(t)]+2(ι)zT(t)z(t)i=1rj=1rm=1rn=1rμiμjμmμn(xT(t)[C˜1i(ι)+D˜12i(ι)Kεj(ι)]TR-1(ι)×[C˜1m(ι)+D˜12m(ι)Kεn(ι)]x(t)+2(ι)γ2wT(t)w(t)E27

where

C˜1i(ι)=[γρ(ι)H1iT(ι)2(ι)ρ(ι)H4iT(ι)02(ι)C1iT(ι)]TD˜12i(ι)=[02(ι)ρ(ι)H5iT(ι)γρ(ι)H3iT(ι)2(ι)D12iT(ι)]T

Substituting (27) into (24), we have

Δ˜V(x(t)ιε)2(ι)zT(t)z(t)+γ22(ι)wT(t)w(t)+γ2i=1rj=1rm=1rn=1rμiμjμmμn×[x(t)w˜(t)]TΦijmn(ιε)[x(t)w˜(t)]E28

where

Φijmn(ιε)=(((Ai(ι)+B2i(ι)Kεj(ι))TQε(ι)+Qε(ι)[Ai(ι)+B2i(ι)Kεj(ι)]+1γ[C˜1i(ι)+D˜12i(ι)Kεj(ι)]T×R-1(ι)[C˜1m(ι)+D˜12m(ι)Kεn(ι)]+k=1sλιkQε(k))(*)TR(ι)B˜1iT(ι)Qε(ι)γR(ι))

Using the fact

i=1rj=1rm=1rn=1rμiμjμmμnMijT(ι)Nmn(ι)12i=1rj=1rμiμj[MijT(ι)Mij(ι)+Nij(ι)NijT(ι)]

we can rewrite (28) as follows:

Δ˜V(x(t)ιε)2(ι)zT(t)z(t)+γ22(ι)wT(t)w(t)+γ2i=1rj=1rμiμj[x(t)w˜(t)]TΦij(ιε)[x(t)w˜(t)]=2(ι)zT(t)z(t)+γ22(ι)wT(t)w(t)+γi=1rμi2[x(t)w˜(t)]TΦii(ιε)[x(t)w˜(t)]+γi=1rijrμiμj[x(t)w˜(t)]T(Φij(ιε)+Φij(ιε))[x(t)w˜(t)]E29

where

Φij(ιε)=(((Ai(ι)+B2i(ι)Kεj(ι))TQε(ι)+Qε(ι)(Ai(ι)+B2i(ι)Kεj(ι))+1γ[C˜1i(ι)+D˜12i(ι)Kεj(ι)]T×R-1(ι)[C˜1m(ι)+D˜12m(ι)Kεn(ι)]+k=1sλιkQε(k))(*)TR(ι)B˜1iT(ι)Qε(ι)γR(ι))E30

Pre and post multiplying (30) by

Ξ(ι)=(Pε(ι)00I)

withPε(ι)=Qε1(ι)we obtain

Ξ(ι)Φij(ιε)Ξ(ι)=((Pε(ι)AiT(ι)+YjT(ι)B2iT(ι)+Ai(ι)Pε(ι)+B2i(ι)Yj(ι)+1γ[C˜1i(ι)Pε(ι)+D˜12i(ι)Yj(ι)]T×R-1(ι)[C˜1m(ι)Pε(ι)+D˜12m(ι)Yj(ι)]+k=1sλιkPε(ι)Pε1(k)Pε(ι))(*)TR(ι)B˜1iT(ι)γR(ι))E31

Note that (31) is the Schur complement of Ψ ij (ι, ε) defined in (10). Using (8)-(9), we learn that

Φii(ιε)0,i=1,2,rE32
Φij(ιε)+Φji(ιε)0,ijrE33

Following from (29), (32) and (33), we know that

Δ˜V(x(t)ιε)2(ι)zT(t)z(t)+γ22(ι)wT(t)w(t)E34

Applying the operatorE[0Tf()dt]on both sides of (34), we obtain

E[0TfΔ˜V(x(t)ιε)dt]E[0Tf(2(ι)zT(t)z(t)+γ22(ι)wT(t)w(t))dt]E35

From the Dynkin’s formula [2], it follows that

E[0TfΔ˜V(x(t)ιε)dt]=E[V(x(Tf)ι(Tf)ε)]E[V(x(0)ι(0)ε)]E36

Substitute (36) into (35) yields

0E[0Tf(2(ι)zT(t)z(t)+γ22(ι)wT(t)w(t))dt]E[V(x(Tf)ι(Tf)ε)]+E[V(x(0)ι(0)ε)]

Using (34) and the fact that we have

E[0Tf{zT(t)z(t)γ2wT(t)w(t)}dt]0E37

Hence, the inequality (5) holds. This completes the proof of Lemma 1.

Remark 1: The linear matrix inequalities given in Lemma 1 becomes ill-conditioned when ε is sufficiently small, which is always the case for the uncertain nonlinear two time-scale dynamic systems. In general, these ill-conditioned linear matrix inequalities are very difficult to solve. Thus, to alleviate these ill-conditioned linear matrix inequalities, we have the following theorem which does not depend on ε.

Now we are in the position to present our first result.

Theorem 1: Consider the system (1). Given a prescribed H performance γ > 0, for ι = 1, 2, , s, if there exist matrices P(ι), positive constants δ(ι) and matrices Yj (ι), j = 1, 2, , r such that the following ε-independent linear matrix inequalities hold:

EP(ι)+P(ι)D0E38
Ψii(ι)0i=1,2,rE39
Ψij(ι)+Ψji(ι)0ijrE40

whereEP(ι)=PT(ι)EP(ι)D=DPT(ι)E=(I000)D=(000I)

Ψij(ι)=(Φij(ι)(*)T(*)T(*)TR(ι)B1iT(ι)γR(ι)(*)T(*)Tφij(ι)0γR(ι)(*)TZT(ι)00P(ι))E41
Φij(ι)=Ai(ι)P(ι)+PT(ι)AiT(ι)+B2i(ι)Yj(ι)+YjT(ι)B2iT(ι)+λιιP¯˜(ι)E42
ϕij(ι)=C˜1i(ι)P(ι)+D˜12i(ι)Yj(ι)E43
R(ι)=diag{δ(ι)IIδ(ι)II}E44
Z(ι)=(λι1P¯˜(ι)λι(ι1)P¯˜(ι)λι(ι+1)P¯˜(ι)λι1P¯˜(ι))E45
P(ι)=diag{P¯˜(1)P¯˜(ι1)P¯˜(ι+1)P¯˜(s)}E46
P¯˜(ι)=P(ι)+PT(ι)2E47

with

B˜1i(ι)=[IIIB1i(ι)]C˜1i(ι)=[γρ(ι)H1iT(ι)2(ι)ρ(ι)H4iT(ι)02(ι)C1iT(ι)]TD˜12i(ι)=[02(ι)ρ(ι)H5iT(ι)γρ(ι)H3iT(ι)2(ι)D12iT(ι)]T(ι)=(I+ρ2(ι)i=1rj=1r[H2iT(ι)H2j(ι)])12

then there exists a sufficiently smallε^0such that the inequality (5) holds forε(0,ε]Furthermore, a suitable choice of the fuzzy controller is

u(t)=j=1rμjKj(ι)x(t)E48

where

Kj(ι)=Yj(ι)(P(ι))1E49

Proof: Suppose there exists a matrix P(ι) such that the inequality (38) holds, then P(ι) is of the following form:

P(ι)=(P1(ι)0P2T(ι)P3(ι))E50

withP1(ι)=P1T(ι)0andP3(ι)=P3T(ι)0. Let

Pε(ι)=Eε(P(ι)+εP˜(ι))E51

with

P~(ι)=(0P2(l)00)E52

Substituting (50) and (52) into (51), we have

Pε(ι)=(P1(ι)εP2(ι)εP2T(ι)εP3(ι))E53

Clearly,Pε(ι)=PεT(ι), and there exists a sufficiently smallε^such that forε(0,ε^]Pε(ι)0.

Using the matrix inversion lemma, we learn that

Pε1(ι)=[P1(ι)+εMε(ι)]Eε1E54

whereMε(ι)=P1(ι)P˜(ι)(I+εP1(ι)P˜(ι))1P1(ι). Substituting (51) and (54) into (10), we obtain

Ψij(ι)+ψij(ι)E55

where the ε-independent linear matrix Ψ ij (ι) is defined in (41) and the ε-dependent linear matrix is

Ψij(ι)=ε((Ai(ι)P˜(ι)+P˜T(ι)AiT(ι)+B2i(ι)Yεj(ι)+YεjT(ι)B2iT(ι)+λιιP¯˜(ι))(*)T(*)T(*)T00(*)T(*)T(C˜1i(ι)P˜(ι)+D˜12i(ι)Yεj(ι))T00(*)TZεT(ι)00Pε(ι))E56

withZε(ι)=(λι1P¯^(ι)λι(ι1)P¯^(ι)λι(ι+1)P¯^(ι)λι1P¯˜(ι))Pε(ι)=diag{P¯^(1)P¯^(ι1)P¯^(ι+1)P¯^(s)}P¯^(ι)=P˜(ι)+P˜T(ι)2andYεj(ι)=Kj(ι)Mε1(ι). Note that the ε- dependent linear matrix tends to zero when ε approaches zero.

Employing (38)-(40) and knowing the fact that for any given negative definite matrix W, there exists an ε > 0 such that W + εI < 0, one can show that there exists a sufficiently smallε^0such that forε(0,ε^](8) and (9) hold. Since (7)-(9) hold, using Lemma 1, the inequality (5) holds forε(0,ε^].

4. Illustrative Example

Consider a modified series dc motor model based on (Mehta & Chiasson, 1998) as shown in Figure 1 which is governed by the following difference equations:

Jdω˜(t)dt=KmLfi˜2(t)(D+ΔD)ω˜(t)Ldi˜(t)dt=Ri˜(t)KmLfi˜(t)ω˜(t)+V˜(t)E57

whereω˜(t)=ω(t)ωref(t)is the deviation of the actual angular velocity from the desired angular velocity,i˜(t)=i(t)iref(t)is the deviation of the actual current from the desired current,V˜(t)=V(t)Vref(t)is the deviation of the actual input voltage from the desired input voltage, J is the moment of inertia, Km is the torque/back emf constant, D is the viscous friction coefficient, andRa,Rf,LaandLfare the armature resistance, the field winding resistance, the armature inductance and the field winding inductance, respectively, withRΔ__Rf+RaandLΔ__Lf+La. Note that in a typical series-connected dc motor, the conditionLfLaholds. When one obtains a series connected dc motor,i(t)=ia(t)=if(t)we haveLfLa. Now let us assume that |ΔJ| ≤ 0.1J.

Figure 1.

A modified series dc motor equivalent circuit.

Givingx1(t)=ω˜(t),x2(t)=i˜(t)andu(t)=V˜(t), (57) becomes

[x˙1(t)εx˙2(t)]=[DJ+ΔJKmLfJ+ΔJx2(t)KmLfx2(t)R][x1(t)x2(t)]+[01]u(t)E58

where ε = L represents a small parasitic parameter. Assume that, the system is aggregated into 3 modes as shown in Table 1:

ModeMoment of InertiaJ ( ) ± ∆ J ( )(kg· m 2 )
1Small0.0005 ± 10%
2Normal0.005 ± 10%
3Large0.05 ± 10%

Table 1.

System Terminology.

The transition probability matrix that relates the three operation modes is given as follows:

Pιk=[0.670.170.160.300.470.230.260.100.64]

The parameters for the system are given as R = 10 Ω, Lf = 0.005 H, D = 0.05 N m/rad/s and Km = 1 N m/A. Substituting the parameters into (58), we get

[x˙1(t)εx˙2(t)]=[0.05J(ι)0.005J(ι)x2(t)0.005x2(t)10][x1(t)x2(t)]+[000.10]w(t)+[01]u(t)+[0.05ΔJ(ι)0.005ΔJ(ι)x2(t)00][x1(t)x2(t)]z(t)=[1001][x1(t)x2(t)]+[01]u(t)E59

wherex(t)=[x1T(t)x2T(t)]Tis the state variables,w(t)=[w1T(t)w2T(t)]Tis the disturbance input, u(t) is the controlled input and z(t) is the controlled output.

The control objective is to control the state variable x2(t) for the range x2(t) ∈ [N1 N2]. For the sake of simplicity, we will use as few rules as possible.

Note that Figure 2 shows the plot of the membership function represented by

M1(x2(t))=x2(t)+N2N2N1andM2(x2(t))=x2(t)+N2N2N1

Knowing that x2(t) ∈ [N1 N2], the nonlinear system

Figure 2.

Membership functions for the two fuzzy set.

(59) can be approximated by the following TS fuzzy model

Eεx˙(t)=i=1rμi[[Ai(ι)+ΔAi(ι)]x(t)+B1iω(t)+B2iu(t)]x(0)=0,z(t)=i=1rμi[C1ix(t)+D12iu(t)]

where μi is the normalized time-varying fuzzy weighting functions for each rule, i = 1, 2, x(t)

=[x1(t)x2(t)]Eε=[100ε]ΔA1(ι)=F(x(t)ιt)H11(ι)ΔA2(ι)=F(x(t)ιt)H12(ι)

A1(1)=[10010N10.005N110]A2(1)=[10010N20.005N210]A1(2)=[10N10.005N110]A2(2)=[10N20.005N210]A1(3)=[10.1N10.005N110]A2(3)=[10.1N20.005N210]

B11(ι)=B12(ι)=[000.10]B21(ι)=B22(ι)=[01]C11(ι)=C12(ι)=[1001]

andD121(ι)=D122(ι)=[01]

with ||F(x(t),ι,t)|| ≤ 1. Then we have

H11(ι)=[0.05J(ι)0.05J(ι)N100]andH12(ι)=[0.05J(ι)0.05J(ι)N200]

In this simulation, we select N1 = −3 and N2 = 3. Using the LMI optimization algorithm and Theorem1 with ε = 0.005, γ = 1 and δ(1) = δ(2) = δ(3) = 1, we obtain the results given in Figure 3, Figure 4 and Figure 5

Remark 2: Employing results given in (Nguang & Shi, 2001; Han & Feng, 1998; Chen et al., 2000; Tanaka et al., 1996; Wang et al., 1996) and Matlab LMI solver [28], it is easy to realize that when ε< 0.005 for the state-feedback control design, LMIs become ill-conditioned and Matlab LMI solver yields an error message, “Rank Deficient”. However, the state-feedback fuzzy controller proposed in this paper guarantee that the inequality (5) holds for the system (59). Figure 3 shows the result of the changing between modes during the simulation with the initial mode at mode 1 and ε = 0.005. The disturbance input signal, w(t), which was used during simulation is given in Figure 4. The ratio of the regulated output energy to the disturbance input noise energy obtained by using the H fuzzy controller is depicted in Figure 5. The ratio of the regulated output energy to the disturbance input noise energy tends to a constant value which is about 0.0094. Soγ=0.0094=0.0970which is less than the pres-cribed value 1. Finally, Table 2 shows the performance index, γ, for different values of ε.

Figure 3.

The result of the changing between modes during the simulation with the initial mode at mode.

Figure 4.

The disturbance input, w(t).

Figure 5.

The ratio of the regulated output energy to the disturbance noise energy, ( ∫ 0 T f z T ( t ) z ( t ) d t ∫ 0 T f w T ( t ) w ( t ) d t ) with ε = 0.005.

The performance index γ
εState-feedback control design
0.0050.0970
0.100.4796
0.300.8660
0.400.9945
0.41> 1

Table 2.

The performance index γ for different values of ε.

5. Conclusion

This chapter has investigated the problem of designing a robust H controller for a class of uncertain nonlinear two time-scale dynamic systems with Markovian jumps that guarantees the L2 - gain from an exogenous input to a regulated output to be less or equal to a prescribed value. First, we approximate this class of uncertain nonlinear two time-scale dynamic systems with Markovian jumps by a class of uncertain Takagi-Sugeno fuzzy models with Markovian jumps. Then, based on an LMI approach, sufficient conditions for the uncertain nonlinear two time-scale dynamic systems with Markovian jumps to have an H performance are derived. The proposed approach does not involve the separation of states into slow and fast ones and it can be applied not only to standard, but also to nonstandard uncertain nonlinear two time-scale dynamic systems. An illustrative example is used to illustrate the effectiveness of the proposed design techniques.

© 2009 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike-3.0 License, which permits use, distribution and reproduction for non-commercial purposes, provided the original is properly cited and derivative works building on this content are distributed under the same license.

How to cite and reference

Link to this chapter Copy to clipboard

Cite this chapter Copy to clipboard

Wudhichai Assawinchaichote, Sing Kiong Nguang and Non-members (October 1st 2009). Robust <i>H</i><sub>&#x221e;</sub> Fuzzy Control Design for Nonlinear Two-Time Scale System with Markovian Jumps based on LMI Approach, Advanced Technologies, Kankesu Jayanthakumaran, IntechOpen, DOI: 10.5772/8230. Available from:

chapter statistics

1048total chapter downloads

More statistics for editors and authors

Login to your personal dashboard for more detailed statistics on your publications.

Access personal reporting

Related Content

This Book

Next chapter

Data Compression and De-compression by Causal Filters with Variable Finite Memory

By A. Torokhti and S. Miklavcic

Related Book

First chapter

A Survey of Decentralized Adaptive Control

By Karel Perutka

We are IntechOpen, the world's leading publisher of Open Access books. Built by scientists, for scientists. Our readership spans scientists, professors, researchers, librarians, and students, as well as business professionals. We share our knowledge and peer-reveiwed research papers with libraries, scientific and engineering societies, and also work with corporate R&D departments and government entities.

More about us