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Sliding Mode Control
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Motivation ExampleConsider the problem of stabilizing the second order system
at the origin
Choose a surface Sliding Manifold (Surface)
How can we bring the trajectory to the manifold s = 0?
How can we maintain it there?
where h(x) and g(x) are unknown nonlinear function with
=0
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Motivation ExampleTo bring s = 0, take the derivative of s as
Suppose2Rx∈∀,
Take the Lyapunov function , we have
Let
When
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Motivation Examplewhen
000
101
<=>
⎪⎩
⎪⎨
⎧=
sss
-s
,,,
)sgn(
( )
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Motivation Example
s(t) reaches zero in finite time
Once on the surface s = 0, the trajectory cannot leave it.
Why? because
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SummarySome definitions:
The motion consists of a reaching phase and sliding phase.The manifold s = 0 is called sliding manifold.The control law u = -β(x)sgn(s) is called sliding mode control
Advantage:Sliding mode is robustness with respect to h and g . We only need to know their bounds. During the sliding phase, the motion is completely independent of h and g.
What is the region of validity?
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Relay form of sling modelThe sliding model controller can be further simplified if in some domain of interest, h and g satisfy the inequality
We have
0t , |)(| )( 1 ≥∀≤⇒≤11
1 0actx
ac|x|
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Relay form of sling model
Ω is positively invariant if
and the set sketched as Noted that 21 xx =&
Estimation of the region of attraction.
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Procedure for designing a sliding mode controller
The procedure for designing a sliding mode controller can be summarized by the following steps:
.
Design the sliding manifold s=0 to control the motion of the reduced order system.
Estimate the upper bound .
Take the control u = -β(x)sgn(s) is the switching (discontinuous) control.
This procedure exhibits model order reduction because the main design task is performed on the reduced-order system.
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Chattering
Chattering results in low control accuracy,high heat losses in electrical power circuits, and high wear of moving mechanical parts.
Sliding model control leads chattering due to imperfections in switching devices anddelays.
The "zig-zag" motion (oscillation) shown inthe sketch, which is known as chattering.
It may also excite unmodeled high-frequency dynamics, which degrades the performance of the system and may even lead to instability
How can we reduce or eliminate chattering?
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Two possible methods to reduce chattering
Two possible methods to reduce chattering:
• Use knowledge of h and g, i.e. not only their bounds.
• Replace the signum function sgn(s) by some high-slope saturation functions such as sat(s /ε ). But only ultimate stability can be achieved.
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Reduce the amplitude
Reduce the amplitude of the signum function
Because ρ is an upper bound on the perturbation term, it is likely to be smaller than that an upper bound on the whole function. Consequently, the amplitude of the switching component would be smaller.
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“Continuous " Sliding Mode ControllerReplace the signum function by a high-slope saturation function
The signum nonlinearity and its approximation are shown in Figure. The slope of the linear portion of sat(s/ε) is 1/ε. A good approximation requires the use of small ε .
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Performance of the "continuous " sliding mode controllerHow can we analyze the system?
0t , |)(| )( 1 ≥∀≤⇒≤11
1 0actx
ac|x|Thus
..and the set
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Performance of the "continuous " sliding mode controller
What happen inside the boundary layer?
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Performance of the "continuous " sliding mode controller
Inside the boundary layer:
||xa
|x|xa
|x|xaxa-θxx
1121
121
2111 1
)-()-(1
)(
11
11
θεθ
εθ
−−=
+−−≤&
The trajectories reach the positively invariant set
in finite time.
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Performance of the "continuous " sliding mode controller
011 2== x|xas
What happens inside Ωε?
Find the equilibrium points
00 =)(φ
Let
=0
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Performance of the "continuous " sliding mode controller
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Performance of the "continuous " sliding mode controller
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Performance of the "continuous " sliding mode controller
011 2== x|xas
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Stabilization Sliding Mode Control
.RDt,x,ux,ut
RD
f ,B,G,ERu Rx
p
n
pn
××∞∈
⊂
∈∈
)[0, )( for )( in smooth lysufficient and in continuous piecewise
is function The origin. the contains that domain a in functions smooth
lysufficient are and input control the is state, the is where
δ
Consider the system
We assume that: • f ,B,E are known while G,δ can be uncertain.
• E(x) is nonsingular matrix, G(x) is a diagonal matrix whoseelements are positive and bounded away from zero, i.e.
.Dx∈all for
Goal: Find a feedback to stabilize (*) at the origin.
(*)
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Regular Form
Let T : be a diffeomorphism such thatnRD →
where I is the p X p identity matrix.
• Change of variables
transforms the system
This form is referred to as the regular form.
(*)
to
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Stabilization Sliding Mode Control•Design a sliding manifold such that when the motionis restricted to the manifold, the reduced-order model
has an asymptotically stable equilibrium point at the origin. The design of amounts to solving a stabilization problem for the system
with ξ viewed as the control input.
•Taking the derivative of s gives
Suppose that we can find the .We need to design u to bring s to zero.
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Stabilization Sliding Mode ControlAssume that the nominal value of G(x) is Ĝ(x) , the control is chosen as
Now we have
Assume that
(Known)
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Stabilization Sliding Mode Control
obtain wecandidate, function Lyapunov a as )( Utilizing 221 ii s/V =
=iV&
Take
where
Then,
=iV&
which guarantees that si=0 reaches in a finite time.
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Design Procedure
)).(( : system order-reduced the stablize to 0 ) ( - manifold sliding a Design
a ηφηηηφξ
,fs
===
&
1)
2) Take the control u as
3) Estimate
4) Choose
Remark•This procedure exhibits model-order reduction because the main design task is preformed on the reduced-order system
•The key future of sliding model control is its robustness to matched uncertainties
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Stabilization Sliding Mode Control
Now use
||s-kg||sxx-kxgV,||s
||sxk||sxεssxxgV
s/V
iiii
i
iii
iii
ii
)()]( )())[-(( have we region the In
])()( )( sat )()[-(
inequality the satisfies derivative its ,)( function Lyapunov the Using
00 11
21
00
0
2
βρβε
βρβ
−≤+≤
≥
++≤
=
&
&
The trajectory reaches the boundary layer |s| ≤ ε in finitetime and remains inside thereafter.
Study the behavior of η
What do we know about this system and what do we need?
) )( ,( a sf += ηφηη&
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Stabilization Sliding Mode ControlTo study the behavior of η, we assume that, together with the sliding manifold design ξ= Φ(η) (continuously differentiable) Lyapunov functionV(η) that satisfies the inequalities
functions.ofclass areandwhere),( ),( all for 31 K,,,DT γαααξη 2∈
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Stabilization Sliding Mode Control
Define a class of K function:
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Stabilization Sliding Mode Control
is positively invariant and all trajectories starting in Ω reach
in finite time
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Stabilization Sliding Mode Control
state. initial any for holds conclusion foregoing the unbounded, radially is )( and globally hold sassumption the If time. finite in set invariant
positively the reaches and 0 all for bounded is ))( ),(( trajectory the , (0)) (0),( all for Then, . over hold sassumption the all Suppose
ηΩξη
ΩξηΩ
ε Vttt ≥
∈
Theorem 1: Consider the systems:
with
ξ= Φ(η) and V(η)
Assume that :
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Stabilization Sliding Mode Control
• The theorem shows that the "continuous " sliding mode controller achieves ultimate boundedness with an ultimate bound that can be controlled by the design parameter ε. It also gives conditions for global ultimate boundedness.
• Since the uncertainty δ could be nonvanishing at x=0, ultimate boundedness is the best we can expect, in general.
• If however, δ vanishing at x=0, then as shown in the next theorem, the origin is asymptotically stable.
Remark:
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Stabilization Sliding Mode Control
stable allyasymptoticuniformly globally be willorigin the globally, hold
sassumption the If .attraction of region its of subset a is and stable llyexponentia is system loop-closed the of origin
the , 0 all for that such 0 exits there Then
stale llyexponentia is ))( ,( of origin The
, )(
. over hold sassumption the all Suppose :
0
Ω
εεε
ηφηη
ρ
Ω
∗∗ <<>
=•
==•
af
k
&
000
2 Theorem
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Unmatched Uncertainties
1δδδ
ξηδδξηξ
ξηδξηη
xT
,u,x,tuxExG,f,,f
b
a
bb
aa
∂∂
=⎥⎦
⎤⎢⎣
⎡
+++=
+=
where
)()()()()( )()(
&
&
Remark:-The sliding mode control usually applies to the system with matched uncertainties, i.e. the uncertainties enter the system at the same level (point) with the control.
-The sliding mode control cannot usually handle arbitrary unmatched uncertainties,
Suppose: The system (*) is modified as:
The system is transformed as
)()]()()()[()( xu,x,tuxExGxBxfx 1δδ +++=&
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Unmatched Uncertainties
unmatched. is term The uncertain matched the to added is term The)()()()()(
)()(
ab
bb
aa
.,u,x,tuxExG,f
,,f
δδδξηδδξηξ
ξηδξηη
+++=
+=&
&
.
,,f
a
aa
δηφ
ηφηδξηη
yuncertaint the of presence the in 0 origin the of stability asymptotic guarantee to have will of design The
))(()( to manifold sliding the on model order-reduced the changes It
=
+=&
Remark: • The difference between matched and unmatched uncertainties is that sliding model control guarantees robustness for any matcheduncertainty provided that an upper bound is known and the neededcontrol effort can be provided.
• There is no such guarantee for unmatched uncertainties.
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Example of Unmatched UncertaintiesExample Consider the second order system
The uncertainties are bounded by
With sliding model
This system is already in a regular form, there is no need to do atransformation.
.
Let
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Example of Unmatched Uncertainties
or
will stabilize the origin with sufficient small ε.
In this example we are able to design a controller to stabilize the systemwithout restrict the magnitude of the unmatched uncertainty. In general, this mat not be possible.
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Example of Unmatched Uncertainties
uxxx
xxx
++=
−+=
12222
2111 1
θ
θ
&
& )(
Example Consider the second order system
2111 1 xxx )( θ−+=&
We try to design x2 to robustly stabilize the origin x1=0.
Note that the system is not stabilizable at Hence, we must limit a to be less than one.
.θ 11=
).( taking by stabilized be can origin the Hence,]-)([)(
obtainwe Using
-a/kxxakxθkxxx
,kxx
110111
1
21
211
2111
12
>=−≤−−=
−=
&
.|x|akbxx
ssgnxkxxku
0
1
00222
21
>++=
−−+=
βββ
β
with)( where
)()()-( iscotnrollermodel sliding final The
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ExampleConsider the planar system:
Assume “nominal” system parameters:
and the bounds
cuxxbxaxx
=+=
2
21311
&
&
1.=== c, b, a 11
..c., .b., a 5150515020 <<<<<<
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Transforming to Standard FormRe-write the dynamics
)),((
)(
ux,xucx
x,xxxbxax
x
x
212
2121311
2
1
δ
δ
+=
++=
&
&
where
.)(),(
)()()(
uc
cc-ux,x
xxbbxaa-x,x
x
x
=
−+=
21
213121
2
1
δ
δ
Comparing to the general derivation,
)( )( 2 2121311 1
x,xδ,xxbxa,f,xξ,x xa δξηη =+===
)( ,)( ,)( ba 2120 x,xc,G,f xa δδξηξη ===
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41
Stabilizing the η Subsystem.xV xxφ 2
1211 2
1== define and -)( Choose α
.xabxbxxxxV
,xxx41
21
21
4111
2112
)()(
)( With
−−=−+==
−==
ααα
αφ
&&
Choose
desired. as 0)( then min max
1 ≤
≥
xV
,ba
&
α
Given the bounds on a and b, choose α=4.
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42
The Complete Control Law
)Δ( that so )(- Define 12
,v,xxvsxxs
21+==
&
φ
.xxbxaxc
ffGu
,vc
uu
ab-aeq
eq
)))((()(-
where Define
21311
1 21
1
+−=∂∂
+=
+=
αηφ
where
vccc-xxaaα
ccc-aα
xaaαccc-aα
δG,v,xx ba
+⎟⎠⎞
⎜⎝⎛ −++
⎟⎠⎞
⎜⎝⎛ −+=
∂∂
=
221
41
21
22
22
)()(-
)()(-
-)Δ( a δηφ
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43
Bounding Δ
|v|k|x|xkxk|| ++≤ 2212
411Δ
Note that
where
ccc-k
|b|b|c|c-cbαbbα
ccc-bαk
|a|a|c|c-caαaaα
ccc-aαk
≥
−+≥−+≥
−+≥−+≥
and ),()()(-
),()()(-
222
222
2
1
and where k>0 can be chosen less than one, as required. We have
.|x|xkxkx,xk|v|x,x|| 2212
4112121 +=+≤ )( where)(Δ ρρ
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44
Simulation with the Signum Function
).αxsign(x)(
Let with)()( Define
212 +−=
>+=
-kx,xv
.bbx,xx,x
1
0
21
002121
β
ρβ
Define the model and control parameters
a=1.2, b=1.3, c=0.8
α = 50, k=0.5 k1=150
k2 = 100, b0=1.
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45
Simulation with the Signum Functions
Note that: • s converges to zero in less than 0.1 second and |x2| grows relatively large
during this transient. • After s=0, the state converges asymptotically to zero.
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46
Simulation with the Signum Function
Replace the signum function with the saturation function with slope ε.
Try ε values of 10, 0.1, and 0.001.
Things to note:
• The system behaves identically for s ≥ ε.
• After s < ε the system converges quickly to a small constant values.
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47
Simulation with the Signum Functions
ε =10
ε =0.1
ε =0.001
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48
TrackingConsider a single-input and single-output relative-degree ρ system:
)()]()[()()(
xhyu,x,tuxgxxfx
=+++= δδ1&
that is
We want to design a state feedback control so that the output y asymptotically tracks a reference signal r(t), where
Goal:
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49
TrackingThere exists a diffeomorphism T(x) over D transfer the system into the normal form by change the variables
Normal form:
Let
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50
TrackingThe change of variables yields
Our objective now is to design state feedback control to ensure thate(t) is bounded and converges to zero as t tends to infinity. Boundednessof e(t) will ensure boundedness of ξ, since R(t), and its derivatives are
bounded. We need also to ensure boundednessof η. This will follow from a minimum phase assumption.
is input-to-state stable.
In particular, we assume that the system
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51
Tracking
We view as the control input. A controller is proposed so that
Then, the sliding manifold is
=0
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52
TrackingWith
We can proceed by design u=v as a pure switching component
to cancel the known terms on the right-hand side.
We have
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53
Tracking
Suppose
with a “continuous” controller
where 0.
It can be shown that with a “continuous” sliding model controller, thereexists a finite time T1, possibly dependent on ε and the initial states, andpositive constant k, independent of ε and the initial state, such that
.Ttkε|trty| 1≥≤− allfor)()(
What properties can we prove for this control?