Introduction to IEEE STANDARDS and its different types.pptx
1801069 minor project
1. PROJECT
ON
PRESSURE CONTROL OF ELECTRO-HYDRAULIC BRAKE SYSTEM BY SLIDING MODE CONTROL
BY
SUDHANSHU SHEKHAR (1801069)
7TH SEMESTER, MECHANICAL ENGINEERING,
NATIONAL INSTITUTE OF TECHNOLOGY, PATNA
UNDER GUIDANCE OF
Dr. NIMAI PADA MANDAL,
ASSISTANT PROFESSOR, MECHANICAL ENGINEERING DEPARTMENT,
NATIONAL INSTITUTE OF TECHNOLOGY, PATNA
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LIST OF FIGURES
Figure 1: Valve control EHB physical model………..13
Figure 2: MATLAB/Simulink model………………….....20
Figure 3: Control logic diagram……………………….....21
Figure 4: Tracking simulation result of the pressure signal in the sine
wave…………………………………………………………………..21
Figure 5: Tracking simulation error of the pressure signal in the sine
wave…………………………………………………………………..21
Figure 6: Tracking simulation result of the pressure signal in the square
wave…………………………………………………………………..22
Figure 7: Tracking simulation error of the pressure signal in the square
wave…....................................................................22
Figure 8: Tracking simulation result of the pressure signal in the square wave
with superimposed sine disturbance…………………..23
Figure 9: Tracking simulation error of the pressure signal in the square wave
with superimposed sine disturbance……………………23
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LIST OF TABLE
Table 1: Comparison between Sliding Mode Control
Control……………………………………………………………………………………………9
& PID
Table 2: Comparison between Electro-hydraulic brake booster vs Vacuum
Booster…………………………………………………………………………………………..9
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NOMENCLATURE
m- total mass of rod and piston in wheel-cylinder
L- total piston travel
u- input variable in nonlinear state
𝐾𝑎𝑥𝑣 – gain of the servo valve
𝑝𝑒 – expected pressure in brake wheel-cylinder
𝒦 – equivalent contacting stiffness between the brake disc and brake slipper
𝜔 – area gradient at sliding valve port
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ABBREVIATIONS
SMC- Sliding Mode Control
EHB- Electro-Hydraulic Brake
PID- Proportional-Integral-Derivative
ECU- Electronic Control Unit
TSM- Time Series Model
LSTM- Long Short-Term Memory
RNN- Recurrent Neural Networks
NMV- Non-Motor Vehicle
ACC- Adaptive Cruise Control
ABS- Anti-lock Braking System
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ABSTRACT
India, today is one of the main ten automotive markets on the planet and given
its enormous working class popular with purchasing potential and the consistent
financial growth. Reducing the number of traffic accidents is a declared target
of most governments. Since dependence on driver reaction is the main cause of
road accidents, it would be advisable to replace the human factor in some
driving-related tasks with automated solutions. The possibility to enhance the
stability and robustness of electro-hydraulic brake (EHB) systems is considered
a subject of great importance in the automotive field. In such a context, the
present study focuses on an actuator with a four-way sliding valve and a
hydraulic cylinder. A 4-order nonlinear mathematical model is introduced
accordingly. Through the linearization of the feedback law of the high order EHB
model, a sliding mode control method is proposed for the hydraulic pressure.
The hydraulic pressure tracking controls are simulated and analysed by
MATLAB/Simulink soft considering separately different conditions, i.e., a sine
wave, a square wave and a square wave with superimposed sine disturbance.
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1.INTRODUCTION
In spite of the electro - hydraulic systems are widely used in many
applications such as in robotics, factories of large objects, actuators in
aircrafts, and many kinds of mechanizations, because of their high power-
to-weight ratios, fast and smooth response characteristics, low cost in
comparison to other types of drives [1].
They have some problems in controlling of the position, force or pressure
because of the nonlinearities of some parameters such as friction, fluid
compressibility and leakages, in order to improve the performance of the
hydraulic actuator, a suitable controller is required [1].
The real running condition of automotive is very adverse for hydraulic
pressure control, and the strong real-time transience would result in
distortion to real-time hydraulic control at variable levels and the
universal nonlinear factors would result in the physical model hard to
construct and meanwhile the control strategy hard to take effect.
Therefore, with the order that EHB would work reliably and stably, the
feasibility and validity research about resolving target pressure and
controlling real-time pressure has to be implemented [2].
The sliding mode controller (SMC) is a discontinuous robust controller and
it is used with systems in presence of the disturbances and the variation
in their parameters [3].
Many advanced solutions for conventional automotive parts and relevant
technics researches emerged, and the EHB is one of them. The electro-
hydraulic brake system, with EHB for short, is a kind of brake system,
which can replace the vacuum booster absolutely and improve the control
effect to the brake request better. Apart from that, the EHB can also
generate the expected brake force within the range required by
regulations, decrease the response time and be easy to match with brake
energy recycling function [4].
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Table 1: Comparison between Sliding Mode Control & PID Control
No. SMC PID
1 Due to the difference in
mathematical and actual
situations there is a discrepancy
and disturbances, SMC can be
used even in very complex
situations.
Can’t be very flexible for higher
level control situations.
2 It is able to completely remove
the disturbances.
It is trying to reduce or remove
disturbances but operates even if
disturbances are present in
system.
3 Sliding Mode Control can deal
with bounded modelling
uncertainties and bounded
disturbances both in linear and
nonlinear systems. It can be said
as hybrid controlling feedback
control.
Very strong and stable but for
lower level controlling as it is not
able to control non-linear higher
level disturbances.
Table 2: Comparison between Electro-hydraulic brake booster vs Vacuum Booster
No. EHB Vacuum Booster
1. Better Control Comparatively lesser control
2. It can scaled up for more braking
power easily.
Relatively lesser
3. Suitable for all conditions with
the help of different controllers it
is able to sustain and handle.
Being used in especially diesel
cars as we can’t use vacuum
booster as due to high pressure
operation also used for heavy
vehicles and other vehicles.
Most gasoline engine vehicles use
the vacuum boost design, as
vacuum is readily available at the
intake manifold
Under which conditions is a
vacuum brake booster vacuum
suspended?
Vacuum booster may also be
called atmospheric suspended.
When the brakes are released and
the engine is running, there is a
balanced atmospheric pressure on
both sides of the diaphragm and
no boost pressure can be created.
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2.LITERATURE REVIEW
Designing of electrohydraulic brake system for autonomous
vehicles in which implementation and braking system and
testing of brake system done using system like AUTOPIA control
system [1].
Using 4-order mathematical model sliding mode control on
electrohydraulic brake system implemented according to
linearization [2].
Using signal fusion, pressure estimation of EHB has been done
[3].
It deals with the problem of designing a robust controller for the
electro-hydraulic position servo system (EHPSS). The sliding
mode control design methodology is utilized here to design a
robust controller with respect to system parameters uncertainty
[4].
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3.OBJECTIVES
To design sliding mode controller using matlab so by the help of
that pressure control of electro-hydraulic brake system happens
and by that disturbances could be controlled by achieving finite
time convergence.
4.THEORETICAL BACKGROUND
Before proceeding with the design, it was necessary to
determine the maximum braking pressure in order to avoid
excessive system stress. This datum was determined
experimentally by means of a manometer. A wheel was
removed, and a manometer was connected in lieu of the brake
shoe. A pressure of 160 bars was measured when the brake
pedal was completely pressed down [1].
The research for methods or theories of hydraulic pressure
control to the electro-hydraulic brake system is one of the
urgencies in the process of developing the electric vehicle. Based
on the requirements of the stability and robustness of the
electro-hydraulic brake (EHB) system, according to the feedback
linearization theory, the 4-order nonlinear mathematical model
of an actuator with a four-way sliding valve and a cylinder is
established. Then, based on the model, a sliding mode control
method is proposed and designed. After several simulations, the
results show that the proposed strategy can track the target
within 0.25 s, and the mean observed error is less than 1.2 bar.
Moreover, with such a strategy, faster response and less
overshoot are possible [2].
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5.RESEARCH WORKING
Based on the analysis of relevant researches home and abroad,
a 4-order nonlinear mathematical model is established
according to the feedback linearization theory. Then, a sliding
mode control method based on the model is designed, aiming at
the nonlinear higher-order EHB model proposed [2].
6.BRAKING SYSTEM DESIGNED
The AUTOPIA C3 Pluriel was already automated, but with only
the steering wheel and accelerator controlled. Speed control
was good as long as sudden changes in speed were not required.
Thus, the necessary next step was to design and implement a
braking system. This system would have to be capable of
operating as a minimum at the AUTOPIA control sampling rate,
which was set by the GPS at 5 Hz [1].
The main prerequisite was to obtain a brake by SMC in
coexistence with the original braking system. The solution
decided on was to design a hydraulic system equipped with
electronic components to permit handling by computer
generated signals through an input/output device [1].
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7.MATHEMATICAL MODEL
1. Actuator Design and Model Establishment
At the view of the structure, the EHB discards the vacuum
booster, which depends on the engine to work and consists of
electromechanical and hydraulic subsystems. Of them, the
electromechanical subsystem consists of ECU, motor part,
sensor part, and mechanics powertrain part; the hydraulic
subsystem consists of a master cylinder, pipeline, electromagnet
valves, and wheel cylinder. Due to the necessity to research, the
actuator in EHB assembled by a four-way sliding valve and a
wheel-cylinder is used to the research object [2].
Because of the advantages of small space to occupy, simple
structure, low cost, and better loading ability of the asymmetric
cylinder [14], so the wheel-cylinder is chosen to be the hydraulic
actuator. Besides, it is used to transform the hydraulic energy in
wheel-cylinder into mechanical energy to generate brake
torque. The hydraulic subsystem controlled by the sliding valve
its structure is shown in Fig [2].
Figure 1: Valve control EHB physical model [1]
As shown in Fig, the adopted asymmetric cylinder is an ideal-
zero-opened four-way sliding valve, which means that its four
orifices are symmetrical and matched. The flow in its throttle is
turbulent, and the fluid compressibility in it is ignored, the spool
of it can move and fluid can flow in instance, the pressure in each
one chamber is equal anywhere, and the supply pressure ps is
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constant while the return pressure p0 is zero; the pipelines are
all short and thick; the influence of fluid mass and the dynamic
effect of pipeline are ignored; the fluid temperature and volume
modulus are considered as constant in the simulation duration;
the external leakage in wheel-cylinder is laminar flow; the
direction that the piston push toward is regarded as the forward
kinematics [2].
The flow equation of the sliding valve is shown [3] in Eqs
1 𝑑 𝑣 √
𝑄 = 𝐾 𝑥 [
(1+sgn(𝑥 ))𝑝 (−1+sgn(𝑥 ))𝑝
𝑣 𝑠 𝑣 0
2 2 𝑣 1
+ − sgn(𝑥 )𝑝 ]
2 𝑑 𝑣 √
𝑄 = 𝐾 𝑥 [ +
(1−sgn(𝑥 ))𝑝 (−1−sgn(𝑥 ))𝑝
𝑣 𝑠 𝑣 0
2 2 𝑣 2
+ sgn(𝑥 )𝑝 ]
2
where Kd is presented as 𝐾𝑑 = 𝐶𝑑𝜔√𝜌
,
And the flow continuity equations of the cylinder are shown in
Eqs
𝑄1 = 𝐴1𝑥̇𝑝 + 𝐶𝑖𝑐(𝑝1 − 𝑝2) + 𝐶𝑒𝑐𝑝1 +
𝑉 + 𝐴 𝐿 + 𝐴 𝑥
𝑔1 1 0 1 𝑝
𝛽𝑒
𝑝̇1
𝑄2 = 𝐴2𝑥̇𝑝 + 𝐶𝑖𝑐(𝑝1 − 𝑝2) − 𝐶𝑒𝑐𝑝2 −
𝑉𝑔2+ 𝐴2(𝐿 − 𝐿0) − 𝐴2𝑥𝑝
𝛽𝑒
𝑝̇2
14
where, x is the area gradient at sliding valve port when the
sliding valve port with variable flowing resistance is full
circumference, Cic is the internal flow leakage coefficient, L is the
total piston travel, L0 is the initial piston position, xp is the piston
motion displacement, be is the fluid volume modulus, Vg1 is the
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inlet pipeline volume from sliding valve to wheel-cylinder [2], Vg2 is
the outlet pipeline volume from wheel-cylinder to sliding valve. The
equation of force is shown in Eq.
𝐴1𝑝1 − 𝐴2𝑝2 = 𝑚𝑥̈𝑝 + 𝐵𝑐𝑥̇𝑝 + 𝑘𝑥𝑝 + 𝐹
𝐴1 = 1
𝜋𝑑2
4
𝐴2 = 1 2
𝜋(𝑑2 − 𝑑2)
4
where, m is the total mass of rod and piston in wheel-cylinder; Bc is
the equivalent fluid damping coefficient; k is the equivalent contacting
stiffness between the brake disc and brake slipper. And as the sliding
valve is also a twin flapper-nozzle electro-hydraulic servo sliding valve,
so its dynamic model can be presented as a proportional component
as shown in eqn.
𝑥𝑣 = 𝐾𝑟 𝐾𝑎𝑥𝑣 𝑓𝑒
In Equation,
𝑓𝑒= 𝑝𝑒𝐴1 − 𝑝2𝐴2
where, Kaxv is the gain of the servo valve; pe is the expected pressure
in brake wheel-cylinder; Kr is the gain of the hydraulic pressure sensor
[2].
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2. Feedback Linearization to Nonlinear Model
According to the established nonlinear mathematical model, the
rod displacement xp, rod velocity xp, hydraulic pressure p1 in the
chamber without the rod of wheel-cylinder [2], and hydraulic
pressure p2 in the chamber with the rod of wheel-cylinder are
chosen to be the system state variable, which is shown in Eq.
𝑥 = [𝑥1 𝑥2 𝑥3 𝑥4]𝑇 = [𝑥𝑝 𝑥̇𝑝 𝑝1 𝑝2]𝑇
{
The fe is the input variable and presented in Eq. and the
nonlinear state equation is presented in Eq.
𝑢 = 𝑓𝑒
𝑥̇ = 𝑓(𝑥) + 𝑔(𝑥)𝑢
𝑦 = ℎ(𝑥) = 𝐴1𝑥3 − 𝐴2𝑥4
The state variables x1, x2, x3, and x4 are shown in Eqs.
𝑥̇1 = 𝑥̇𝑝 = 𝑥2
𝑥̇2 = 𝑥̈𝑝 =
−𝑘𝑥1 − 𝐵𝑐𝑥2 + 𝐴1𝑥3 − 𝐴2𝑥4 − 𝐹
𝑚
3 1
𝑥̇ = 𝑝̇ =
𝛽 [𝑄 −𝐴 𝑥 −(𝐶 +𝐶 )𝑥 +𝐶 𝑥
𝑒 1 1 2 𝑖𝑐 𝑒𝑐 3 𝑖𝑐 4 ]
𝑉𝑔1+𝐴1(𝐿0+𝑥𝑝)
4 2
𝑥̇ = 𝑝̇ = −
𝛽 [𝑄 −𝐴 𝑥 −𝐶 𝑥 +(𝐶 +𝐶 )𝑥
𝑒 2 2 2 𝑖 𝑐 3 𝑖 𝑐 𝑒 𝑐 4]
𝑉𝑔2+𝐴2(𝐿−𝐿0−𝑥𝑝)
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17. 11/12/2021
The
[2]
argumentation above can be concluded
𝑓(𝑥) = [𝑓1
𝑔(𝑥) = [0
𝑓1= 𝑥2
𝑓2 𝑓3 𝑓4]𝑇
0 𝑔3 𝑔4]𝑇
𝑓2 = −𝑘𝑥1−𝐵𝑐𝑥2+𝐴1𝑥3−𝐴2𝑥4−𝐹
𝑚
𝑓3 = 𝛽𝑒[−𝐴1𝑥2−(𝐶𝑖𝑐+𝐶𝑒𝑐)𝑥3+𝐶𝑖𝑐𝑥4]
𝑉𝑔1+𝐴1(𝐿0+𝑥𝑝)
4
𝑓 = −
𝛽 [−𝐴 𝑥 −𝐶 𝑥 +(𝐶 +𝐶 )𝑥 ]
𝑒 2 2 𝑖𝑐 3 𝑖𝑐 𝑒𝑐 4
𝑉𝑔2+𝐴2(𝐿−𝐿0−𝑥𝑝)
𝑉𝑔1+𝐴1𝐿0+𝐴1𝑥1
3 𝑠
𝑔 = 𝛽𝑒𝐾𝑟𝐾𝑎𝑥𝑣𝐾𝑑
√[(1 + sgn(𝑢))𝑝 +
(−1+sgn(𝑢))𝑝0
2 1
− sgn(𝑢)𝑝 ]
4
𝑔 = −
𝑔2 2
𝑉 +𝐴 (𝐿−𝐿 )−𝐴 𝑥
0 2 1
2
√ [
2 (1−sgn(𝑢))𝑝𝑠
𝜌 2
+
(−1−sgn(𝑢))𝑝0
2 2
+ sgn(𝑢)𝑝 ]
According to the definition of Lie Derivative, the results shown
below can be calculated.
17
𝑓
𝐿0ℎ(𝑥) = 𝐴1𝑥3 − 𝐴2𝑥4
𝑓
𝐿𝑔 𝐿0ℎ(𝑥) = 𝐴1𝑔3 − 𝐴2𝑔4
𝐿𝑓ℎ(𝑥) = 𝐴1𝑓3 − 𝐴2𝑓4
According to the definition of relative order, the relative order
of this system is 1. And due to the system is a four-order system,
an internal dynamic subsystem exists. In accordance with the
theory of feedback linearization in the nonlinear system, a
transformation should be implemented on that subsystem, and
then a linear state equation is obtained, presented in Eq [2]
{
𝑧̇ = 𝑣
{
𝑦 = 𝑧
In Eq, the z is a one order vector, and the transformation relation
of state variable can be presented in Eq.
𝑧 = ℎ(𝑥) = 𝐴1𝑥3 − 𝐴2𝑥4
𝑦 = 𝑧
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In Eq, the transformation relation of the input variable v can be
presented in Eq.
𝑣 = 𝛼(𝑥) + 𝛽(𝑥)𝑢
The a and b are presented in Eqs.
𝛼(𝑥) = 𝐿𝑓ℎ(𝑥)
𝑓
𝛽(𝑥) = 𝐿𝑔 𝐿0ℎ(𝑥)
𝑢 =
In Eq, the input variable u in nonlinear state space can be
calculated from the input variable v in linear state space by
inverse transformation [2].
𝑣 − 𝛼(𝑥)
𝛽(𝑥)
3. Sliding Mode Controller Design
Due to the limited condition to observe and measure, the precise
nonlinear mathematical model is hard to obtain when designing
the feedback linearization control law [3].
As to the servo wheel-cylinder controlled by the sliding valve
adopted in EHB in this paper, the variation of the load, the fluid
viscosity, the supply pressure, and the wear condition on some
contact area would affect the tracking control of hydraulic
pressure [2].
So, it is necessary to introduce a robust control algorithm to
ensure the robustness to the parameter variation and external
disturbance for the system transformed by feedback
linearization [2]. First, the tracking pressure error is defined as
Eq.
𝑒 = 𝑧𝑒 − 𝑧
In the Eq, the ze is the expected pressure in wheel-cylinder of
the system transformed by feedback linearization [2]. Due to the
relative order of the system transformed by feedback
linearization which is indicated to be 1. So, the sliding mode
surface can be designed as Eq.
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s=e
Then, the equivalent control veq is settled as Eq.
𝑠̇ = 𝑒̇ = 𝑧𝑒̇ − 𝑧̇
𝑣𝑠𝑤 = −𝜅 ⋅ sgn(𝑠)
In the Eq, the j is the gain of the switching control. Then, the output of
the designed controller can be stated as Eq.
𝑣 = 𝑣𝑒𝑞 + 𝑣𝑠𝑤 = 𝑧𝑒̇ − 𝜅 ⋅ sgn(𝑠)
In order to verify the stability of the control algorithm, the Lyapunov
function is settled in Eq.
1
𝑉 = 𝑠2
2
Additionally, the relative process can be calculated according to Eqs.
𝑉̇ = 𝑠𝑠̇
𝑉̇ = 𝑠(𝑧̇𝑒 − 𝑧̇)
𝑉̇ = 𝑠[𝑧̇𝑒 − 𝜅 ⋅ sgn(𝑠) − 𝑧𝑒̇ ]
𝑉̇ = 𝑠[−𝜅 ⋅ sgn(𝑠)]
𝑉̇ = −𝜅 ⋅ |𝑠| ≤ 0
Therefore, it is indicated that the control system in the linear space
after transformation is stable. But, due to the chattering with which
the sign function would bring to the system, so, it is replaced by the
boundary layer function to weaken the chattering. The boundary layer
function is demonstrated as [2]
𝑠
Φ
Φ
sgn ( )
𝑠 𝑠
(| | > 1)
sat ( ) = { 𝑠 𝑠
Φ
𝑠
Φ Φ Φ
(| | < 1) (| | < 1)
19
According to Eq, the sliding mode control law u can be calculated and
shown as
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𝑠
𝑧̇𝑒 − 𝜅 ⋅ sat (Φ) − 𝐿𝑓 ℎ(𝑥)
𝑢 =
𝑓
𝐿𝑔 𝐿0ℎ(𝑥)
8.SIMULATION
According to the feedback linearization theory, and the
linearization to the model of cylinder controlled by servo sliding
valve in the EHB, and the sliding mode control algorithm
designed in this paper, a simulation model is established in
MATLAB/Simulink. The model is shown in Fig. 2 below. As shown
in Fig. 3 below, it illustrates the control logic designed in this
paper [2].
Then, the tracking simulation verifications to the pressure signal
in the sine wave, square wave, and the square wave with sine
wave disturbance mixed, are implemented [2]. The parameters
used in the simulation are listed in Tab. 3.
1. Simulation and Verification under Sine Wave Signal
First, the pressure signal in the sine wave with mean value 5
MPa, amplitude 10 MPa, and frequency 0.25 Hz is adopted to
the tracking simulation for 4 s [2].
Figure 2: MATLAB/Simulink model [2]
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Figure 3: Control logic diagram [3]
Figure 4: Tracking simulation result of the pressure signal in the sine wave [2]
Figure 5: Tracking simulation error of the pressure signal in the sine wave [2]
2. Simulation and Verification under Square Wave Signal
Second, the pressure signal in the square wave with amplitude 10
MPa, and frequency 0.25 Hz is adopted to the tracking simulation for
4 s [2].
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Figure 6: Tracking simulation result of the pressure signal in the square wave [2]
8.3 Simulation and Verification under Superimposed Sine-
Square Wave Signal
Last, to validate the stability of the robust control algorithm designed
in this paper, the pressure signal in the square wave with
superimposed sine disturbance is adopted to the tracking simulation
for 4 s. This pressure signal contains the square wave with amplitude
10 MPa, and frequency 0.25 Hz and the sine disturbance with mean
value 0 MPa, amplitude 0.5 MPa, and frequency 0.25 Hz [2].
Figure 7: Tracking simulation error of the pressure signal in the square wave [2]
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Figure 8: Tracking simulation result of the pressure signal in the square wave with superimposed
sine disturbance [2]
Figure 9: Tracking simulation error of the pressure signal in the square wave with
superimposed sine disturbance [2]
9.RESULTS AND DISCUSSION
It can be inferred from Figs. 4 and 5, that the mean tracking
error to the pressure signal in the sine wave is -0.03931 MPa,
and phase delay is acceptable under the sliding mode control
algorithm. In the duration of the simulation, the pressure
responded to the target pressure signal starts keeping stable
tracking error at the time of 0.25 s.
It can be inferred from Figs. 6 and 7, that the mean tracking
error to the pressure signal in the square wave is -0.0801 MPa
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under the sliding mode control algorithm. In the duration of
the simulation, the pressure responded to the target pressure
signal starts keeping stable tracking error at the time of 0.1 s.
It can be inferred from Figs. 8 and 9, that the mean tracking
error to the pressure signal in the square wave with
superimposed sine disturbance is -0.1186 MPa under the
sliding mode control algorithm. In the duration of the
simulation, the pressure responses to the target pressure
signal and starts keeping stable tracking error at the time of
0.1 s, and the tracking error is more stable in the high target
pressure signal phase than in the low target pressure signal
phase.
1O. CONCLUSION
By applying the feedback linearization theory, the
nonlinearity of the proposed valve control hydraulic wheel-
cylinder is transformed into approximate linearity when
designing the SMC controller, which proves correct and
characterizes the novelty of the work in this paper.
The response characteristic, tracking effect and the phase
delay are well limited in an acceptable section for the tracking
simulations, under the pressure signal in the sine wave, in the
square wave, and in the square wave with sine wave
disturbance mixed. And they indicate that the strategy of
sliding mode control of hydraulic pressure based feedback
linearization of high order EHB model proposed
In this project, we can track the target within 0.25 s, and the
mean observed error is less than 1.2 bar. Therefore, this
research shows that the strategy designed has the advantages
of faster response and less overshoot, which can be
considered as a theory reference for further hydraulic
pressure control research of EHB.
25. 25
11/12/2021
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SudhanshuShekhar
(SIGNATURE)
DATE: -11/12/2021
