Fuzzydynamiccharacteristicmodelingandadaptivecontrolofnonlinearsystemsanditsapplicationtohypersonicvehicles资源-CSDN文库

79 浏览量 2021-02-20 19:55:14 上传评论收藏 441KB PDF 举报

资源推荐

资源详情

资源评论

RESEARCH PAPERS

Special Focus

SCIENCE CHINA

Information Sciences

March 2011 Vol. 54 No. 3: 460–468

doi: 10.1007/s11432-011-4188-9

 Science China Press and Springer-Verlag Berlin Heidelberg 2011 info.scichina.com www.springerlink.com

Fuzzy dynamic characteristic modeling and adaptive

control of nonlinear systems and its application to

hypersonic vehicles

LI HongBo

∗

, SUN ZengQi, MIN HaiBo & DENG JianQiu

Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China

Received May 6, 2010; accepted December 14, 2010

Abstract In this paper, a fuzzy dynamic characteristic modeling and adaptive control method is proposed for

a class of nonlinear systems. By employing fuzzy dynamic characteristic model, the controlled plant is described

as a slowly time-varying fuzzy system, wherein the parameters are estimated online by using recursive Least-

Squares al gorithm. Under this fr amework, a fuzzy adaptive controller is constructed, and the stability condition

of the closed-loop system is also derived. The main advantage of the proposed method lies in no requirement

for the prior knowledge of system model and less parameters to tune, which allows engineers to operate it in

a simple, straightforward manner. The proposed method is applied to the control of hypersonic vehicle, and

simulation results are given to demonstrate the eﬀectiveness of the obtained results.

Keywords fuzzy control, dynamic characteristic model, adaptive control, hypersonic vehicle

Citation Li H B, Sun Z Q, Min H B, et al. Fuzzy dynamic characteristic modeling and adaptive control of non -

linear systems and its application to hype r sonic vehicles. Sci China Inf Sci, 2011, 54: 460–468, doi: 10.1007/s11432-

011-4188-9

1 Introduction

In the past decades, fuzzy control techniques have received increasing attention from research commu-

nities, and have been widely and successfully used in modeling and control of nonlinear sys tems [1–10 ].

Especially, Takagi-Sugeno (T-S) fuzzy model has bee n well recognized as an eﬀective method in approx-

imating complex nonlinear system [6–10]. In T-S fuzzy model, local dynamics in diﬀerent state space

regions are represented by diﬀerent linear models, and the overall model of the system is achieved by

fuzzy “blending” of these fuzzy models. In r ecent years, many researchers have shown their interest in

T-S fuzzy models, and a gre at number of res ults have been reported in the literature, see [6–11] and the

reference therein. It is worth noting that, in most o f the aforementioned re sults on T- S fuzzy systems,

the T-S fuzzy models and the corresponding c ontrollers are usually obtained oﬀ-line. However, in ma ny

practical applications, it is more desirable to develop o nline controller design methods to accommodate

the changes of the process or the behavior in a dynamic environment. Therefore, some adaptation mech-

anisms have been introduced into T-S fuzzy control to improve the robustness and control performance

∗

Corresponding author (email: lihongbo.jason@gmail.com)

Li H B, et al. Sci China Inf Sci M arch 2011 Vol. 54 No. 3 461

of the closed-loop system, a nd some pro mis ing methods a re repor ted in the literature. For more details

on this subject, please refer to [12] and the reference therein.

On the other hand, characteristic model is an eﬀective modeling method based on both plant dynamic

characteristics and co ntrol performance requirements, rather than based on only accurate plant dynamic

analysis [13–17]. By using online parameter e stimation algorithm, characteristic model approximates the

controlled plant with a s lowly time-varying low-order diﬀerence equation, and therefore can enable us to

design low-order intelligent controller for various complex plants with nonlinearities and uncertainties.

More importantly, characteristic-model has less parameters to estimate and the corresponding adaptive

controller is simple to use, convenient to adjust and test [16]. Up to now, characteristic-model-based

adaptive control has been succe ssfully applied to more than 400 diﬀerent systems in the ﬁeld of industry

and spacecraft control [16, 17], and has been shown promising results. Although this technique has

good r obustness and shows promising performance, pure characteristic-model based control method also

presents drawbacks that, for complex system with strong nonlinear coupling and parametric vibration,

characteristic model may not provide the neces sary modeling precision required for engineering design

tasks. Therefore, it is desirable to introduce some mechanism into characteristic model to improve the

modeling precisio n, but without increasing model complexity. However, to the best of the authors’

knowledge, the problem has not received much r e search attention and is still open, which motivates the

present study.

In view of the advantages and disadvantages of T-S fuzzy model and characteristic model, it is a natural

idea to combine them together by replacing linear models in T-S fuzzy model with characteristic models ,

which gives rise to the so-called fuzzy dynamic characteristic model. In our earlier work [18], neuro-fuzzy

dynamic characteristic modeling and adaptive control was investigated and some preliminary results were

unveiled. It is worth noting that our previous work in [18] leaves much room for improvement since, on

one hand, the parameter estimation algorithm ther e in bears no clear physical meaning and is somewhat

complicated for on-line application, while on the other hand, the stability analysis therein is preliminary

and the stability c ondition of the closed-loop system is still needed. It is therefore the intentio n of this

paper to present more eﬀective parameter estimation algorithm and stability co nditions for this method,

and apply the obtained res ults to the control of hypersonic vehicle. The main advantage of the proposed

method lies in no requirement to the prior knowledge of system model and less parameters to tune, which

allows engineers to operate it in a simple, str aightforward manner. To make our idea more lucid, we only

consider single-input single-output (SISO) nonlinear system in this study. However, the idea behind this

paper can be easily extended to multi-input multi-output (MIMO) nonlinear systems.

The rest of this paper is organized as follows. The problem formulation and preliminaries are given in

section 2. A fuzzy dynamic characteristic modeling and adaptive control method for nonlinear system

is pr oposed in section 3. Numeric al results are provided in section 4. Finally, conclusions are drawn in

section 5.

Notation. Throughout this paper, R

and R

n×m

denote the n dimensional Euclidean space and the

set of all n × m real matrices respectively. The superscript “T” denotes matrix tra nsposition; and I is

the identity matrix with appropriate dimensions. For symmetric matrices X a nd Y , the nota tion X > Y

means that X −Y is positive deﬁnite. Finally, in symmetric block matrices, we use “∗” as an ellipsis for

the terms introduced by symmetry.

2 Problem formulation and preliminaries

Consider the following nonlinear system:

x(t) = f (x) + g(x)u,

y = h(x), (1)

where x ∈ R

is the sy stem state, u ∈ R

is the control input and y ∈ R

is the measured output. To

simplify our notation, we foc us on SISO nonlinear systems in this study, but the extension of these results

剩余8页未读，继续阅读

评论收藏

内容反馈

weixin_38555616

粉丝: 2
资源: 886

Fuzzy dynamic characteristic modeling and adaptive control of no...

最新资源

Fuzzy dynamic characteristic modeling and adaptive control of no...

Fuzzy systems based adaptive fuzzy –tolerant dynamic surface control for a class of high nonlinear systems with actuator fault

Stable Adaptive Fuzzy Control of Nonlinear Systems

Adaptive fuzzy control of a class of SISO nonaffine nonlinear systems

Fuzzy Control Systems

adaptive control

Fuzzy dynamic characteristic model based attitude control of hypersonic vehicle in gliding phase

Identification and Control of Nonlinear Systems Using Fuzzy Hammerstein Models

Type-2 Fuzzy Logic Uncertain Systems Modeling and Control.pdf

Intelligent Adaptive Control: Industrial Applications

LMI Approach to Analysis and Control of Takagi-Sugeno Fuzzy Systems with Time Delay

Robot Motion and Control Recent Developments(PDF)

Adaptive output feedback dynamic surface control of nonlinear systems with unmodeled dynamics and unknown high-frequency gain sign

FUZZY CONTROL SYSTEMS DESIGN AND ANALYSIS

Modeling and control of DEDS

Fuzzy Linguistic Hybrid Geometric Aggregation Operator and Its Application to Group Decision Making

Adaptive Control for Nonlinear Systems by Fuzzy Systems with Vector Rules

a course in fuzzy systems and control

Fuzzy Modeling and Control Theory and Applications

Information and Computer Technology, Modeling and Control

Adaptive Fuzzy Control for Pure-Feedback Nonlinear Systems with Non-Affine Functions being Semi-Bounded and In-differentiable

An_approach_to_adaptive_control_of_fuzzy_dynamic_systems

Adaptive fuzzy containment control of nonlinear systems with unmeasurable states

Distributed adaptive fuzzy event-triggered containment control of nonlinear strict-feedback systems

Dynamic system modeling and control.pdf

Fuzzy and Neuro-Fuzzy Systems in Medicine

directfuzzyadaptive.rar_Adaptive nonlinear_adaptive fuzzy _fuzzy

最新资源