- About this Journal ·
- Abstracting and Indexing ·
- Aims and Scope ·
- Annual Issues ·
- Article Processing Charges ·
- Articles in Press ·
- Author Guidelines ·
- Bibliographic Information ·
- Citations to this Journal ·
- Contact Information ·
- Editorial Board ·
- Editorial Workflow ·
- Free eTOC Alerts ·
- Publication Ethics ·
- Reviewers Acknowledgment ·
- Submit a Manuscript ·
- Subscription Information ·
- Table of Contents

Mathematical Problems in Engineering

Volume 2012 (2012), Article ID 481942, 29 pages

http://dx.doi.org/10.1155/2012/481942

## Optimal Fuzzy Control for a Class of Nonlinear Systems

Department of Cybernetics and Artificial Intelligence, Faculty of Electrical Engineering and Informatics, Technical University of Košice, Letná 9, 042 00 Košice, Slovakia

Received 4 May 2012; Accepted 8 July 2012

Academic Editor: Massimo Scalia

Copyright © 2012 Dušan Krokavec and Anna Filasová. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

#### Abstract

The paper presents conditions suitable in design giving quadratic performances to stabilizing controllers for given class of continuous-time nonlinear systems, represented by Takagi-Sugeno models. Based on extended Lyapunov function and slack matrices, the design conditions are outlined in the terms of linear matrix inequalities to possess a stable structure closest to LQ performance, if premise variables are measurable. Simulation results illustrate the design procedure and demonstrate the performances of the proposed control design method.

#### 1. Introduction

Since a generic method for design of a controller valid for all types of nonlinear systems has not been developed yet, an alternative seems to be fuzzy approach which benefits from the advantages of the approximation techniques approximating nonlinear system model equations. Using the Takagi-Sugeno (TS) fuzzy model [1], the nonlinear system is represented as a collection of fuzzy rules, where each rule utilizes the local dynamics by a linear system model. Since TS fuzzy models can well approximate a large class of nonlinear systems, and the TS-model-based approach can apprehend the nonlinear behavior of a system while keeping the simplicity of the linear models, by employing TS fuzzy models a control design methodology exploits fully advantage of the modern control theory, especially in the state space optimal and robust control.

The main idea of the TS-model-based controller design is to derive control rules so as to compensate each rule of a fuzzy system, determining the local feedback gains. It is known that the separate stabilization of these local modes does not ensure the stability of the overall system, and global design conditions have to be used to guarantee the global stability and control performance. Therefore, a range of stability conditions have been developed for TS fuzzy systems (see e.g., [2–5]), most of them relying on the feasibility of an associated system of linear matrix inequalities (LMIs) [6]. Therefore, the state control based on fuzzy TS system model gives control structures which can be designed using technique derived from equivalent LMIs (some principles and results are reported e.g., in [7–10]).

The problem of controlling a system in such way to optimize a performance index that represents the actual operating performance of a system has been an area of study for several decades [11–14]. In particular, if the attention is restricted to linear quadratic (LQ) control, several works following this approach over the years have been reported in the literature, where some new ones being for example, in [15]. Specifically, this approach has been often made in diverse practical problems for finite-time interval with time-varying feedback gains and full state measurable variables, to bring dynamical systems to a desired final states, as special interest in aircraft, spacecraft, and robots control and diagnosis [16, 17].

Following the given ideas in LQ control [18], the main contribution of the paper is to present new conditions for designing the stabilizing fuzzy state control with LQ performance for nonlinear MIMO systems approximated by a TS model and exploiting measurable premise variables. The proposed design method prefers methodology given in [7, 10] but is constructed on the extended form of quadratic Lyapunov function and enhanced evaluation of its time derivative [19, 20]. Because the Lyapunov synthesis approach is exploited to express global stability conditions in the form of a set of LMIs, resulting conservativeness of stability conditions is reduced, since while a common symmetric positive definite Lyapunov matrix verifying all inequalities is required, this approach eliminates products of this matrix with system model matrix parameters and extensive exploits affine properties of TS models.

The remainder of this paper is organized as follows. In Section 2 the general structure of TS models and LQ control is briefly described, and in Section 3 basic preliminaries are presented. The control design problem for systems with measurable premise variables is given in Sections 4 and 5, where especially new design conditions are derived and proven. Section 6 gives a numerical example to illustrate the effectiveness of the proposed approach and to confirm the validity of the control scheme. The last section draws conclusion remarks.

Throughout the paper, the following notations are used: , denotes the transpose of the vector and matrix , respectively, diag denotes a block diagonal matrix, for a square matrix (resp. ) means that is a symmetric positive definite matrix (resp., negative definite matrix), the symbol represents the th order unit matrix, denotes the set of real numbers and the set of all real matrices.

#### 2. General Methodologies

##### 2.1. Takagi-Sugeno Fuzzy Models

The systems under consideration is one class of multiinput and multioutput nonlinear (MIMO) dynamic systems, which in the state-space form is represented as where , , are vectors of the state, input, and output variables, respectively, and and are real finite values matrices. It is assumed that , and that is bounded in associated sectors, that is, in the regions within the system will operate.

It is considered that the number of the nonlinear terms in the nonlinear part of model is , and that there exists a set of nonlinear sector functions of this properties where is the number of sector functions, and is the vector of premise variables. A premise variable represents any measurable variable and can be in a simple case a state variable.

Using a TS model, the conclusion part of a single rule consists no longer of a fuzzy set but determines a function with state variables as arguments, and the corresponding function is a local function for the fuzzy region that is described by the premise part of the rule [21]. Thus, using linear functions, a system state is described locally (in fuzzy regions) by linear models, and at the boundaries between regions a suitable interpolation is used between the corresponding local models. Thus, given a pair of (), the final state of the systems is inferred as follows where is the linear model associated with the combination of sector function indexes. Constructing the aggregated function set from all combinations of the sector functions, for example, ordered as follows, implies that where is the th aggregated normalized membership function satisfying conditions: and linear consequent equation represented by (2.8) is called a linear subsystem.

Therefore, the TS fuzzy approximation of (2.1) leads to (2.7), (2.8) where the matrix is Jacobian matrix of with respect to , and is the center of the th sector (fuzzy region).

Now, the TS fuzzy model for (2.1), (2.2) takes the form

*Assumption 2.1. *The matrices , are the same for all local models.

*Assumption 2.2. *The pair () is locally controllable where
and is of full column rank.

##### 2.2. Linear Quadratic Control Background

In order to build up the background of the proposed method, some basics on the continuous-time LQ control are recalled. Considering the linear model (2.1), (2.2), that is, , the control design is possed as an optimal problem with certain combined quadratic performance on and , and the control task is formulated as follows: find the nonzero control defined on such that the state is driven to the state coordinate origin at , and the following performance index is minimized where takes the form: is finite, , , , , and , .

Proposition 2.3 (equivalent performance index). * If the linear system from (2.1), (2.2) is controllable, then the LQ control design task is optimized with respect to the equivalent quadratic cost function (performance index):
**
where
**, , , , respectively. *

*Proof (compare e.g. [18]). *Since now the system (2.1), (2.2) is linear in , the quadratic Lyapunov function candidate can be chosen as
and the derivative of the Lyapunov function candidate takes the form:
respectively, where
Defining, at the time instant , the cumulative function as
which, in turn, is equivalent to
then adding (2.23) to (2.14), subtracting (2.24) from (2.14), and setting , the performance index (2.14) is brought to the form (2.17), where
It is evident that with then (2.16), (2.22) imply (2.19).

Proposition 2.4 (infinite horizon LQ control). *LQ control that the control law gain has become constant value is given by
**
where is a solution of the algebraic Riccati equation (ARE)
*

*Proof (see e.g., [18]). *Considering , , , then (2.18), (2.19) imply
respectively. It is obvious that (2.29) implies (2.27), and by substituting (2.26), (2.27) into (2.30), (2.28) is obtained.

Note, it makes no practical sense to have a terminal cost term with terminal time being infinite in the performance index.

Summarizing, (2.25) specifies the form of derivative of generalized Lyapunov function to formulate the infinite horizon LQ control design conditions using LMI.

#### 3. Basic Preliminaries

The main concern of this section is to present basic concepts of nonlinear fuzzy control design for systems represented by TS model. Presented structure is partly motivated by minimizing the number of LMIs with respect to LMI solvers limitations.

*Definition 3.1. *Considering the general form of (2.11):
and using the same set of membership function, the nonlinear fuzzy state controller is defined as

Proposition 3.2. * If the set of aggregated normalized membership functions (2.9) satisfies (2.10) then
*

*Proof. *Considering the conditions (2.10) imply
Providing the base of mathematical induction principle the number of functions is chosen as . Thus, left-hand side of (3.3) implies
and right-hand side of (3.3) specifies
Applying the Schur complement property to and it is obvious that
and the condition is satisfied in the sense of the proposition.

Since the statement holds true for at least one value, it is assumed that it holds true for an arbitrary fixed value
To prove that the induction hypothesis holds true for all let the th membership function is included in prescribed way, that is,
where here, and hereafter, denotes the symmetric item in a symmetric matrix.

Now, comparing the Schur complements of and , the first complement is satisfied since
and the second gives
Thus, (3.10), (3.11) imply (3.3).

Proposition 3.3. *The equilibrium of the system (3.1) under control (3.2) is globally quadratic stable if there exists a positive definite symmetric matrix such that
**
for for all , , and , respectively, where
*

*Proof. *Substituting (3.2) into (3.1) results in
Since for all , it yields
and also, owing to the symmetry in summations:
Thus, adding (3.16), (3.17) gives
Rearranging the computation, (3.18) can be written as
respectively. Defining Lyapunov function candidate of the form:
where is a positive definite symmetric matrix, then after evaluation the derivative of (3.20) with respect to on a system trajectory it yields
respectively. Then (3.21) can be compactly written as
where
Thus, (3.24) implies (3.12), (3.13). This concludes the proof.

*Remark 3.4. *If for all then
and (3.13) for takes the form
which implies
It is evident that with satisfying (3.12) also (3.27) is satisfied.

Proposition 3.5. * The equilibrium of the system (3.1) under control (3.2) is globally asymptotically stable if there exist a positive definite symmetric matrix and matrices such that
**
for all and , and , respectively, where
**
Then, the set of control law gain matrices are given as follows:
*

* Proof. * Since is considered to be a positive definite matrix, it is obvious that is also positive definite, and premultiplying left-hand and right-hand side of (3.12), as well as (3.13) by leads to the inequalities:

Thus, with the notation
(3.31) implies (3.29), respectively. This concludes the proof.

Proposition 3.6. * The equilibrium of the fuzzy system (2.11) controlled by the fuzzy controller (3.2) is globally asymptotically stable if there exists a positive definite matrix and matrices such that
**
for , , where
**
The set of control law gain matrices is given by (3.30). *

*Proof. * It Implies directly from Remark 3.4 and Proposition 3.5.

#### 4. Fuzzy Controller with Quadratic Performances

The controller design is accomplished using the concept of asymptotic stability by analyzing the existence of an extended Lyapunov function. The fuzzy static output controller is designed using the concept of parallel distributed compensation, in which the fuzzy controller shares the same sets of normalized membership functions like the TS fuzzy system model. The goal is to achieve a certain level of performance using a guaranteed-cost approach results known from LQ control theory.

Theorem 4.1. *The equilibrium of the system (3.1), controlled by the fuzzy controller (3.2), is globally asymptotically stable if there exist positive definite symmetric matrices , , , and matrices , such that with (3.29)
**
for all , , and , respectively. The set of control law gain matrices can be found directly as
*

* Proof. * Considering (3.16) and defining with respect to (2.15), (2.25), then the quadratic positive Lyapunov function is as follows
where is a positive definite symmetric matrix, then it yields
Substituting (2.15), (3.1) and (3.2) into (4.4) gives
where
and after straightforward computation it can be obtained
with
Now, exploiting (3.3), then (4.7), (4.8) can be rewritten as
where
Analogously to (3.23) then (4.10) can be written as
where, with defined in (3.14), it is
for all , and , respectively.

Since is a regular positive definite square matrix, then premultiplying left-hand side and right-hand side of (4.12) by give
Thus, using (3.29) and the notations
it yields
for all , , , respectively. It is evident that (4.15) implies (4.2), and (4.16) implies (4.2).

Theorem 4.2. *The equilibrium of the fuzzy system (2.11) controlled by the fuzzy controller (3.2) is globally asymptotically stable if there exist positive definite matrices , , , and matrices such that
**
for all , . The set of control law gain matrices is given by (4.7). *

*Proof. * Since for all , then (4.10) implies
Thus, premultiplying the both side of (4.19) by gives
and with (3.34) and the notations (4.14) then (4.20) implies (4.18).

#### 5. Enhanced Controller with Quadratic Performance

The previous section was detailed how to find the fuzzy controller with quadratic performance ensuring the global asymptotic stability of the system. To extend the affine TS model principle by introducing the slack matrix variables into the LMIs, the system matrices are now decoupled from the equivalent Lyapunov matrix.

##### 5.1. Stability Conditions

Theorem 5.1. *The equilibrium of the system (3.1) under control (3.2) is globally asymptotically stable if there exist positive definite symmetric matrices , , , , , such that
**
for all , , and , respectively, where with defined in (3.14) it is
*

*Proof. * Since (3.1) implies
using arbitrary regular symmetric square matrices it yields
Adding (5.5), and transposition of (5.5) to (4.4), and then inserting (3.2) give
Then, using the notation:
after straightforward computation it can be obtained
where
Exploiting (3.3), then (5.10) can be rewritten as
and using (5.12) it yields
where
Analogously to (3.23) and (4.11) now (5.13) can be written as
with , defined in (5.3), respectively, and
Since (5.16) implies (5.1), this concludes the proof.

Corollary 5.2. * If for all then (5.14) implies that the equilibrium of the system (2.11) under control (3.2) is globally asymptotically stable if there exist positive definite symmetric matrices , , , , , such that
**
for all , , where
*

The importance of Theorem 5.1 is that it separates from system matrices , , that is, there are no terms containing product of and any of them. This enables to derive design conditions with respect to natural affine properties of TS models.

##### 5.2. Control Parameter Design

In the next theorems, a scalar , is involved in the set of LMIs. The tuning parameter was added in the LMIs in an attempt to obtain less conservative stability conditions than Theorems 4.1 and 4.2, respectively. This procedure of adding scalar in LMIs has been widely explored in literature (see e.g., [19]).

Theorem 5.3. *The equilibrium of the system (3.1) controlled by the fuzzy controller (3.2) is globally asymptotically stable if for given , there exist positive definite symmetric matrices , , , , and matrices , such that
**
where
**
for all , , , , respectively. The set of control law gain matrices is given as in (3.30).*

* Proof. * Since , are considered to be symmetric positive definite, introducing the congruence transform matrix:
and premultiplying left-hand as well as right-hand sides of (5.16) by (5.25) gives
Thus, with , and with the notations
it yields
and considering (5.22), we have
Using Schur complement property, then (5.29) implies (5.21)–(5.24).

Theorem 5.4. * The equilibrium of the system (2.11) controlled by the fuzzy controller (3.2) is globally asymptotically stable if for given , there exist positive definite symmetric matrices , , , , and matrices , such that
**
for all . Then, the set of control law gain matrices is given as in (3.30). *

*Proof. * If for all then (5.18) implies
Premultiplying left-hand side and right-hand side of (5.32) by (5.25) gives
and with the notations (5.27), (5.32) then (5.34) implies (5.31).

Note, the forms (5.2), (5.18) are suitable to optimize a solution with respect to LMI variables in an LMI structure. Conversely, the forms (5.21), (5.31) behave LMI structure only if is a prescribed constant design parameter. In the opposite case, the design task has to be formulated as BMI problem.

#### 6. Illustrative Examples

The nonlinear dynamics of the hydrostatic transmission were taken from [22], and this MIMO model was used at first in control design and simulation.

The hydrostatic transmission dynamics is represented by a nonlinear fourth-order state-space model: where is the normalized hydraulic pump angle, is the normalized hydraulic motor angle, is the pressure difference [bar], is the hydraulic motor speed [rad/s], is the speed of hydraulic pump [rad/s], is the normalized control signal of the hydraulic pump, and is the normalized control signal of the hydraulic motor. It is supposed that the external variable as well as the second state variable are measurable. In given working points the parameters are Since the variables and are bounded on the prescribed sectors then vector of the premise variables can be chosen as follows: Thus, the set of nonlinear sector functions: implies the next set of normalized membership functions: The transformation of nonlinear differential equations of the system into a TS fuzzy system in standard form gives with the associations Thus, solving (5.30)-(5.31) for given with respect to the LMI matrix variables , , , , and , , , using Self-Dual-Minimization (SeDuMi) package for Matlab [23], the feedback gain matrix design problem was feasible with the results: which rise up a stable set of closed-loop subsystems.

Comparing with the standard approach, presented method tends to produce the same control gain matrices if for all , which radically reduce the control structure, since the result is stabilizing linear control law with quadratic performance for the nonlinear system. Moreover, such control is robust with respect to a premise variable sensor fault.

Specifying simulation conditions for unforced (autonomous) regime and as follows: