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"这篇外文文献来源于《 ProceedingsoftheInstitutionofMechanicalEngineers,PartD:JournalofAutomobileEngineering》期刊，作者是AHossain, ARahman和AKMMohiuddin，文章标题为 'Nonlinearcontrollerofanair-cushionsystemforaswampterrainvehicle:fuzzylogicapproach'，通过Sage Publications发布，并可在线访问，其DOI为10.1177/0954407011400818。"
模糊控制是一种基于人类决策过程的控制理论，它在处理不确定性、非线性和复杂系统时表现出强大的能力。这篇文献特别探讨了在沼泽地形车辆的空气垫系统中的非线性控制器设计，采用了模糊逻辑方法。模糊逻辑是模糊控制的基础，它允许使用不精确或模糊的语言规则来描述系统的行为，而不是传统的精确数学模型。
在传统的控制系统中，如PID（比例-积分-微分）控制器，通常需要精确的数学模型，但许多实际系统，尤其是涉及到环境交互的复杂系统，其行为难以准确预测。模糊逻辑控制器则能够处理这些不确定性，通过定义模糊集、模糊化输入、推理过程和去模糊化输出来实现控制目标。
文献中提到的空气垫系统可能是指车辆底部的空气囊，这种系统可以提供额外的浮力和减震效果，帮助车辆在沼泽等软地面行驶。非线性控制器设计对于这类系统尤其重要，因为车辆与地面的接触力、空气囊的压力变化以及车辆运动状态都可能呈现出复杂的非线性关系。
模糊逻辑控制器设计通常包括以下步骤：
1. **模糊化**：将实值输入转化为模糊集合的成员度。
2. **规则库**：建立一系列模糊逻辑规则，这些规则反映了专家或经验知识。
3. **推理**：根据输入的模糊值，应用模糊逻辑规则进行推理，得出控制输出的模糊值。
4. **去模糊化**：将推理结果转换回实值，作为实际的控制器输出。
这篇研究可能会详细分析如何构建和调整模糊逻辑规则，以适应空气垫系统的变化条件，同时优化车辆在沼泽地的性能。此外，它可能还会讨论控制器的稳定性和性能，以及与传统控制策略的比较。
该文献对那些对模糊控制在工程应用，特别是非线性系统控制，以及汽车和越野车辆领域有兴趣的研究者和工程师具有很高的参考价值。通过学校图书馆获取此类专业资源，可以免费阅读并深入理解模糊控制在实际工程问题中的应用。
ground pressure and have the capability of operat-
ing over a wide range of unprepared moderate peat
terrain [5]. However, transportation efficiency is the
main requirement for the tracked vehicles when the
vehicles are travelling over low-bearing-capacity
swamp peat terrain. A moderate peat terrain bear-
ing capacity of 12 kN/m
2
is reported by Rahman
et al.[5]; swamp peat terrain with a surface
mat thickness of 70 mm and with a bearing capacity
of 7 kN/m
2
is reported by Jamaluddin and Sarawak
[6]. Systematic studies of the principles underlying
the transportation development of off-road vehicles
on low-bearing-capacity swamp peat terrain, there-
fore, have attracted considerable interest in the
search to develop an intelligent air-cushion system
for a swamp terrain vehicle and to achieve the asso-
ciated reduced fuel consumption [7].
The air-cushion–terrain interaction takes place in
an uncertain and vague environment due to soil con-
ditions, and submerged and undecomposed materi-
als such as wood, stones, stumps, and shrubs, etc.
No mathematical model can describe such a complex
mechanics system satisfactorily [8]. In efforts to con-
trol such systems, the fuzzy logic controller (FLC)
has become a popular model that offers nonlinear
control and has the advantage that the fuzzy control-
ler does not require a precise mathematical model
[9–12]. The appropriate and inexact nature of the
air-cushion system for a tracked vehicle has been
effectively captured using fuzzy logic, which is a logi-
cal system closer to human knowledge and machine
language. This paper describes the implementation
of a FLC to control an air-cushion system based on
the vehicle vertical position from the ground.
Sampling data collected from the swamp terrain are
used to validate the fuzzy models.
2 METHODOLOGY
2.1 Theoretical model of power consumption
The air-cushion tracked vehicle is partially supported
by the air-cushion (lifting) system and is driven by
the propulsion system. Therefore, the total power
consumption P of the vehicle is given by
P = P
c
+ P
d
(1)
where P
c
is the power consumed by the air-cushion
(in W) when it partially supports the weight of the
vehicle body and P
d
is the power consumed by the
driving syst em (in W) when overcoming the travel-
ling resistance and maintaining the normal driving
state.
2.1.1 Power required for the air-cushion system
The power required for the air-cushion system can
be expressed by [13]
P
c
= p
c
Q = h
c
L
c
D
c
2
r
1=2
p
c
ðÞ
3=2
(2)
where
Q = h
c
L
c
D
c
2p
c
r
1=2
In equation (2), Q is the volume flowrate (in
m
3
/s), p
c
is the cushion pressure (in N/m
2
), h
c
is
theoretical cushion clearance height (in m), L
c
is
the air-cushion perimeter (in m), D
c
is the discharge
coefficient, and r is the air density (in kg/m
3
).
2.1.2 Power required for the driving system
For the propulsion system, the power consumption
is from total motion resistance. Therefore, in
normal driving cases for the vehicle, the required
power for the driving system can be expressed by
[8, 13, 14]
P
d
= R
t
V
t
= R
c
+ R
in
+ R
drag
V
t
(3)
where
R
c
=2B
k
p
z
2
2
+
4
3D
ht
m
m
z
3
R
in
=
W p
c
A
c
1000 g
222 + 3V
t
ðÞ
R
drag
= p
c
A
c
tan u
Fig. 1 Photo of the developed vehicle [1]
722 A Hossain, A Rahman, and A K M Mohiuddin
Proc. IMechE Vol. 225 Part D: J. Automobile Engineering
at Southwest University of Science and Technology on June 14, 2011pid.sagepub.comDownloaded from
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