06 mpc jbr stanford
TRANSCRIPT
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Robustness of Suboptimal Model Predictive Control
James B. Rawlings
Department of Chemical and Biological EngineeringUniversity of Wisconsin–Madison
Insitut für Systemtheorie und RegelungstechnikUniversität StuttgartStuttgart, Germany
June 24, 2011
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Outline
1 Inherent robustness of suboptimal MPCExponential stability for difference inclusionsNominal exponential stability of suboptimal MPCRobust exponential stability of suboptimal MPCFeasibility issueMain resultsRobust stability in absence of state constraints
2 Proposal for nonlinear, distributed MPCModelObjective FunctionTerminal ControllerRemoving the terminal constraintCooperative control algorithmStability of distributed nonlinear cooperative controlExample
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Why study robustness of suboptimal MPC?
Cooperative , distributed MPC is a special case of suboptimal MPC.Anything we establish about suboptimal MPC can be applied tocooperative, distributed MPC (and optimal MPC!)
Suboptimal MPC has an interesting feature: a nonunique,point-to-set control law u ∈ κN (x ).
Optimal solution of nonconvex
PN (x ) : minu∈U N
V N (x , u)
cannot be computed online for any nonlinear model. Practitionersimplement only suboptimal MPC.
We should know something about its inherent robustness properties.1
1Pannocchia, Rawlings, and Wright (2011)Stuttgart – June 2011 Distributed MPC 3 / 50
For suboptimal MPC; again, the basic MPC setup
The system modelx + = f (x , u ) (1)
State and input constraints
x (k ) ∈ X , u (k ) ∈ U for all k ∈ I≥0
Terminal constraint (and penalty)
φ(N ; x , u) ∈ Xf ⊆ X
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Feasible sets
The set of feasible initial states and associated control sequences
ZN = {(x , u) | u (k ) ∈ U, φ(k ; x , u) ∈ X for all k ∈ I0:N −1,
and φ(N ; x , u) ∈ Xf }
and Xf ⊆ X is the feasible terminal set.
The set of feasible initial states is
X N = {x ∈ Rn | ∃u ∈ UN such that (x , u) ∈ ZN } (2)
For each x ∈ X N , the corresponding set of feasible input sequences is
U N (x ) = {u | (x , u) ∈ ZN }
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Cost function and control problem
For any state x ∈ Rn and input sequence u ∈ UN , we define
V N (x , u) =N −1k =0
(φ(k ; x , u), u (k )) + V f (φ(N ; x , u))
(x , u ) is the stage cost; V f (x (N )) is the terminal costConsider the finite horizon optimal control problem
PN (x ) : minu∈U N
V N (x , u)
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Suboptimal MPC
Rather then solving PN (x ) exactly , we consider using any(unspecified) suboptimal algorithm having the following properties.
Let u ∈ U N (x ) denote the (suboptimal) control sequence for theinitial state x , and let ũ denote a warm start for the successor initialstate x + = f (x , u (0; x )), obtained from (x , u) by
ũ := {u (1; x ), u (2; x ), . . . , u (N − 1; x ), u +} (3)
u + ∈ U is any input that satisfies the invariance conditions of Assumption 3 for x = φ(N ; x , u) ∈ Xf , i.e., u + ∈ κf (φ(N ; x , u)).
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Suboptimal MPC
The warm start satisfies ũ ∈ U N (x +).
The suboptimal input sequence for any given x + ∈ X N is defined asany u+ ∈ UN that satisfies:
u+ ∈ U N (x
+) (4a)
V N (x +, u+) ≤ V N (x
+, ũ) (4b)
V N (x +
, u+
) ≤ V f (x +
) when x +
∈ r B (4c)
in which r is a positive scalar sufficiently small that r B ⊆ Xf .
Notice that constraint (4c) is required to hold only if x + ∈ r B, and itimplies that |u+| → 0 as |x +| → 0.
Condition (4b) ensures that the computed suboptimal cost is nolarger than that of the warm start.
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Basic MPC assumptions
Assumption 1 (Continuity of system and cost)
The functions f : Rn × Rm → Rn, : Rn × Rm → R≥0 and
V f : Rn → R≥0 are continuous, f (0, 0) = 0, (0, 0) = 0, and V f (0) = 0.
Assumption 2 (Properties of constraint sets)
1 The set U is compact and contains the origin. The sets X and Xf areclosed and contain the origin in their interiors, Xf ⊆ X.
2 The set U is compact and contains the origin. The sets X = Rn and
Xf = levαV f = {x ∈ Rn | V f (x ) ≤ α}, with α > 0.
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Basic MPC assumptions
Assumption 3 (Basic stability assumption)
For any x ∈ Xf there exists u := κf (x ) ∈ U such that f (x , u ) ∈ Xf andV f (f (x , u )) + (x , u ) ≤ V f (x ).
Assumption 4
There exist positive constants a, a1, a2, af and r̄ , such that the costfunction satisfies the inequalities
(x , u ) ≥ a1|(x , u )|a for all (x , u ) ∈ X × U
V N (x , u) ≤ a2|(x , u)|
a for all (x , u) ∈ r̄ B
V f (x ) ≤ af |x |a for all x ∈ X
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Definition 5 (Exponential stability)
The origin of the difference inclusion z + ∈ H (z ) is exponentially stable (ES) on Z , 0 ∈ Z , if there exist scalars b > 0 and 0 < λ
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Nominal exponential stability
Theorem 7 (ES)Under Assumptions 1, 2.1, 3, and 4, the origin of the closed-loop system
x + ∈ F (x ) = {f (x , u ) | u ∈ κN (x )}
is ES on (arbitrarily large) compact subsets of X N .
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Robust exponential stability of suboptimal MPC
Definition 8 (RES)
The origin of the closed-loop system (6) is robustly exponentially stable (RES) on int(X N ) if there exist scalars b > 0 and 0 < λ 0, there exists δ > 0 such that for all sequences {d (k )} and{e (k )} with x (0) = x ∈ C satisfying:
maxk ≥0
|d (k )| ≤ δ, maxk ≥0
|e (k )| ≤ δ
x m(k ) = x (k ) + e (k ) ∈ X N , x (k ) ∈ X N , for all k ∈ I≥0,
it follows that
|φed (k ; x )| ≤ b λk |x | + , for all k ∈ I≥0. (8)
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Robust exponential stability of suboptimal MPC
Definition 9 (SRES)
The origin of the closed-loop system (6) is strongly robustly exponentially
stable (SRES) on a compact set C ⊂ X N , 0 ∈ int(C ), if there exist scalarsb > 0 and 0 < λ 0, there exists δ > 0 such that for all sequences {d (k )} and {e (k )}satisfying
|d (k )| ≤ δ and |e (k )| ≤ δ for all k ∈ I≥0,
and all x ∈ C , we have that
x m(k ) = x (k ) + e (k ) ∈ X N , x (k ) ∈ X N , for all k ∈ I≥0, (9a)
|φed (k ; x )| ≤ b λk
|x | + , for all k ∈ I≥0. (9b)
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Feasibility issue
The warm start ũ is feasible for the predicted successor state
x̃ + = f (x m, u (0; x m))
i.e., (x̃ +, ũ) ∈ ZN ,
But it may not be feasible for the measured successor state
x +m = f (x , u (0; x m)) + d + e +
(The true successor state, which is unknown, isx + = f (x , u (0; x m)) + d .)
So we have to resolve this infeasibility online. We make the followingassumption
Assumption 10
For any x , x ∈ X N and u ∈ U N (x ), there exists u ∈ U N (x
) such that|u − u| ≤ σ(|x − x |) for some K-function σ(·).
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Feasibility issue
Among various options for finding p, we consider the following feasibility problem (notice that x̃ + is known):
Find p s.t. ũ + p ∈ U N (x +m ) and |p| ≤ σ(|x
+m − x̃
+|), (10)
Proposition 11
Under Assumption 10 , for any (x̃ +, ũ) ∈ ZN and x +m ∈ X N , the set of
solutions to (10) is nonempty.
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Main results
Theorem 12 (SRES of suboptimal MPC)
Under Assumptions 1, 2.1, 3, 4, and 10 , the origin of the perturbed closed-loop system
x + ∈ F ed (x )
is SRES on C ρ.
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Main results
u
U N
X N
S ρS
u0(x )
C ρ
ρ
ZN
x
Figure: Sketch of the main sets involved in SRES.
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Robust stability in absence of state constraints
To this aim, we replace Assumption 2.1 with 2.2.
Assumption 10 will not be necessary, whereas Assumptions 1, 3 and 4(with V N (·) replaced by V
βN (·) later defined) are required.
We modify the cost function as follows:
V βN (x , u) =N −1k =0
(φ(k ; x , u), u (k )) + β V f (φ(N ; x , u))
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Modified suboptimal MPC algorithm in absence of stateconstraints
u+
∈ UN
(11a)V βN (x
+, u+) ≤ V βN (x +, ũ) (11b)
V βN (x +, u+) ≤ β V f (x
+) when x + ∈ r B (11c)
Z̄ r = {(x , u) ∈ Rn × UN | V βN (x , u) ≤
V̄ ,
and V βN (x , u) ≤ β V f (x ) if x ∈ r B}
X0 = {x ∈ Rn
|∃u ∈ UN
such that (x , u) ∈ Z̄ r } (12)
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Nominal exponential stability
We have the following nominal stability result.
Theorem 13 (ES without state constraints)
Under Assumptions 1, 2.2 , 3, and 4, the origin of the closed-loop system
x + ∈ {f (x , u ) | u ∈ κN (x )}
is ES on X0.
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Robust stability in absence of state constraints
G ed (z ) = {u+ | u+ ∈ UN , V βN (x
+m , u
+) ≤ V βN (x +m , ũ),
V βN (x +m , u
+) ≤ β V f (x +m ) if x
+m ∈ r B}
V̄ ρN (z m) = maxe ∈ρB
V βN (z ) s.t. z = z m − (e , 0) (13a)
S̄ ρ = {z m ∈ Z̄ r | V̄ ρN (z m) ≤
V̄ } (13b)
C̄ ρ = {x ∈ Rn | x = x m − e , e ∈ ρB, ∃u s.t. (x m, u) ∈ S̄ ρ} (14)
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Robust stability in absence of state constraints
Theorem 14 (SRES without state constraints)
Under Assumptions 1, 2.2 , 3 and 4, the origin of the closed-loop system
x +
∈ F ed (x )
is SRES on C̄ ρ.
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The robust region of attraction
The ideal, but unachievable, result would be that the robust region of attraction under nonzero disturbances is the entire nominal feasible
set, X N .
This ideal result is approached reasonably closely, however, whenexcluding state constraints!
When |d |, |e | → 0, C̄ ρ → X0 and SRES holds over a set approachingthe admissible set of initial conditions.
These results for robustness of suboptimal nonlinear MPC all apply tocooperative, distributed MPC .
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Requirements for distributed, nonlinear control
Now let’s return to the setting of distributed, nonlinear MPC, anddeal with the nonconvexity issue.
Two criteria in design:1 the optimizers should not rely on a central coordinator2 the exchange of information between the subsystems and the iteration
of the subsystem optimizations should be able to terminate beforeconvergence without compromising closed-loop properties.
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Distributed nonconvex optimization
Consider the optimization
minu V (u ) s.t. u ∈U
We require approximate solutions to the following suboptimizations atiterate p ≥ 0 for all i ∈ I1:M
u p i = arg minu i ∈Ui
V (u i , u p −i )
in which u −i = (u 1, . . . , u i −1, u i +1, . . . , u M ).
Define the step υp
i
= u p
i
− u p
i
.
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Algorithm
To choose the stepsize αp i , each suboptimizer initializes the stepsize2
with αi
V (u p ) − V (u p i + αp i υ
p i , u
p −i ) ≥ −σα
p i ∇i V (u
p )υp i
in which σ ∈ (0, 1).
After all suboptimizers finish the backtracking process, they exchangesteps. Each suboptimizer forms a candidate step
u p +1i = u p i + w i α
p i υ
p i ∀i ∈ I1:M
2Armijo rule: (Bertsekas, 1999, p.230)Stuttgart – June 2011 Distributed MPC 30 / 50
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Algorithm
Check the following inequality, which tests if V (u p ) is convex-like
V (u p +1) ≤i ∈I
1:M
w i V (u p i + α
p i υ
p i , u
p −i ) (15)
in which
i ∈I1:M w i = 1 and w i > 0 for all i ∈ I1:M .
If the condition above is not satisfied, then we find the direction withthe worst cost improvement
i max = arg maxi
{V (u p i + αp i υ
p i , u
p −i )}
and eliminate this direction by setting w i max to zero and repartitioning
the remaining w i so that they sum to 1.At worst, condition (15) is satisfied with one direction only.
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Distributed nonconvex optimization — Properties
Lemma 15 (Feasibility)
Given a feasible initial condition, the iterates u p are feasible for all p ≥ 0.
Lemma 16 (Objective decrease)
The objective function decreases at every iterate, that is,V (u p +1) ≤ V (u p ).
Lemma 17 (Convergence)
Every accumulation point of the sequence {u p } is stationary.
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Distributed nonconvex optimization
0
1
2
3
4
0 1 2 3 4
u 2
u 1
Figure: Nonconvex function optimized with Distributed nonconvex optimizationalgorithm
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Distributed nonlinear cooperative control
We have the following local models
x +1 = f 1(x 1, x 2, u 1, u 2) x +2 = f 2(x 1, x 2, u 1, u 2) (16)
Collect these models to form the plantwide model
x + = f (x 1, x 2, u 1, u 2) = f (x , u )
in which
x =
x 1x 2
u =
u 1u 2
f (x , u ) =
f 1(x 1, x 2, u 1, u 2)f 2(x 1, x 2, u 1, u 2)
for which x ∈ Rn, u ∈ Rm, and f : Rn × Rm → Rn.
At each time step k , we require the inputs to satisfy
u 1(k ) ∈ U1 u 2(k ) ∈ U2 k ∈ I0:N −1
in which each Ui ∈ Rmi is compact, convex, and contains the origin
in its interior.
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Distributed nonlinear cooperative control
The objective function for each subsystem i ∈ I1:2 is defined
V i
x (0), u1, u2
=
N −1
k =0
i
x i (k ), u i (k )
+ V if
x (N )
We define plantwide objective
V
x 1(0), x 2(0), u1, u2
= ρ1V 1
x (0), u1, u2
+ ρ2V 2
x (0), u1, u2
To simplify notation we use V (x , u) for the plantwide objective.
Assumption 18
For each i ∈I
1:2, there exists a K ∞ function αi (·) such that
i (x i , u i ) ≥ αi (|x i |) ∀(x i , u i ) ∈ Rni × Ui (17)
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Distributed nonlinear cooperative control
Denote the plantwide terminal penalty V f (x ) = ρ1V 1f (x ) + ρ2V 2f (x ).
We define the terminal region Xf to be a sublevel set of V f . Fora > 0, define
Xf = {x | V f (x ) ≤ a}
Assumption 19
The plantwide terminal penalty V f (·) satisfies
αf (|x |) ≤ V f (x ) ≤ γ f (|x |) ∀x ∈ Xf
in which αf (·) and γ f (·) are K ∞ functions.
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Distributed nonlinear cooperative control
Defining (x , u ) = ρ11(x 1, u 1) + ρ22(x 2, u 2), we require the followingstability assumption.
Assumption 20
The terminal cost V f (·) satisfies
min(u 1,u 2)∈U1×U2
V f
f (x , u 1, u 2)
+ (x , u )
s.t. f (x , u 1, u 2) ∈ Xf
≤ V f (x ) ∀x ∈ Xf
This assumption implies that there exists a κif (x ) ∈ Ui for all i ∈ I1:2such that
V f
f (x , κ1f (x ), κ2f (x ))
+
x , κ1f (x ), κ2f (x )
≤ V f (x ) (18)
s.t. f (x , κ1f (x ), κ2f (x )) ∈ Xf
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Removing the terminal constraint
To avoid coupled input constraints, must remove the terminal stateconstraint.
For some β ≥ 1, define the modified objective function
V β(x , u) =
N −1k =0
(x (k ), u (k )) + β V f (x (N )) (19)
Define the set of admissible initial (x , u) pairs as
Z0 = {(x , u) ∈ X × UN | V β(x , u) ≤ V , φ(N ; x , u) ∈ Xf } (20)
The set of initial states X0 is the projection of Z0 onto X
X0 = {x ∈ X | ∃u such that (x , u) ∈ Z0}
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Distributed nonlinear cooperative control
Proposition 21 (Terminal constraint satisfaction)
Let {(x (k ), u(k )) | k ∈ I≥0} denote the set of states and control sequences generated by the suboptimal system. There exists a β > 1 suchthat for all β ≥ β , if (x (0), u(0)) ∈ Z0, then (x (k ), u(k )) ∈ Z0 withφ(N ; x (k ), u(k )) ∈ Xf for all k ∈ I≥0.
For the remainder of the talk, we replace the plantwide objective with themodified objective V (·) ← V β(·) and hence the terminal constraint issatisfied.
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Cooperative control algorithm
Let ũ ∈ U be the initial condition for the cooperative MPC algorithmsuch that φ(N ; x (0), ũ) ∈ Xf .
At each iterate p , an approximate solution of the followingoptimization problem is found
minu
V x 1(0), x 2(0), u1, u2 (21a)s.t. x +1 = f 1(x 1, x 2, u 1, u 2) (21b)
x +2 = f 2(x 1, x 2, u 1, u 2) (21c)
ui ∈ UN i ∀i ∈ I1:2 (21d)
|ui | ≤ δ i (|x i (0)|) if x (0) ∈ B r ∀i ∈ I1:2 (21e)
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Cooperative control algorithm
Let the input sequence returned by distributed nonconvexoptimization algorithm be up̄ (x , ũ).
The first input of this sequence κ¯p
(x (0)) = u ̄p
(0; x (0), ũ) is injectedinto the plant and the state is moved forward.
To reinitialize the algorithm at the next sampling time, we define thewarm start
ũ+1 = {u 1(1), u 1(2), . . . , u 1(N − 1), κ1f
x (N )
}
ũ+2 = {u 2(1), u 2(2), . . . , u 2(N − 1), κ2f
x (N )
}
in which x (N ) = φ(N ; x (0), u1, u2).
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Nominal stability results
Theorem 22 (Asymptotic stability)
Let Assumptions 18 -20 hold and let V (·) ← V β(·) by Proposition 21.Then for every x (0) ∈ XN , the origin is asymptotically stable for the closed-loop system x + = f (x , κp̄ (x )).
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A nonlinear example
Consider the unstable nonlinear system
x +1 = x 21 + x 2 + u
31 + u 2
x +2 = x 1 + x 22 + u 1 + u
32
with initial condition (x 1, x 2) = (3, −3).
For this example, we use the stage cost
1(x 1, u 1) =1
2(x 1Q 1x 1 + u
1R 1u 1)
2(x 2, u 2) =1
2(x 2Q 2x 2 + u
2R 2u 2)
For the simulation we choose the parameters
Q = I R = I N = 2 p = 3 Ui = [−2.5, 2.5] ∀i ∈ I1:2
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Distributed nonlinear cooperative control
-4
-3
-2
-1
0
1
2
3
4
0 2 4 6 8 10
x
k
x 1
x 2
Figure: State trajectory (p = 3)
-4
-3
-2
-1
0
1
2
3
4
0 2 4 6 8 10
x
k
x 1
x 2
Figure: Centralized state trajectory(p = 10)
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Distributed nonlinear cooperative control
-3
-2
-1
0
1
2
3
0 2 4 6 8 10
u
k
u 1
u 2
Figure: Input trajectory (p = 3)
-3
-2
-1
0
1
2
3
0 2 4 6 8 10
u
k
u 1
u 2
Figure: Centralized input trajectory(p = 10)
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Distributed nonlinear cooperative control
10−10
10−810−6
10−4
10−2
100
102
104
0 2 4 6 8 10
V (x (k ), ū (k ))
k
p̄ = 3p̄ = 10
Figure: Open-loop cost to go versus time on the closed-loop trajectory fordifferent numbers of iterations.
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Further Reading I
D. P. Bertsekas. Nonlinear Programming . Athena Scientific, Belmont,MA, second edition, 1999.
G. Pannocchia, J. B. Rawlings, and S. J. Wright. Conditions under whichsuboptimal nonlinear MPC is inherently robust. Sys. Cont. Let., 2011.Accepted for publication.
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Acknowledgments
The author is indebted to Luo Ji of the University of Wisconsin andGanzhou Wang of the Universität Stuttgart for their help in organizing thematerial and preparing the overheads.