Abstract
This paper presents the first results for the stability analysis of Model Predictive Control schemes applied to the Fokker–Planck equation for tracking probability density functions. The analysis is carried out for linear dynamics and Gaussian distributions, where the distance to the desired reference is measured in the L2-norm. We present results for general such systems with and without control penalization. Refined results are given for the special case of the Ornstein–Uhlenbeck process. Some of the results establish stability for the shortest possible (discrete time) optimization horizon N = 2.
Notes
The only exception are the results in our conference paper [10], which form a preliminary version of the results in this paper for a much more restricted class of systems.
Note also that D has full rank.
To see this in the equation for \(\dot {\Sigma }_{Y}(t),\) it is helpful to use (18), which holds due to Assumption 1(b).
We remind that Σ(k) corresponds to the evaluation of Σ(t) at t = tk.
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Acknowledgements
The authors wish to thank Tobias Damm for very helpful suggestions and the referees for their valuable comments that helped to improve the manuscript.
Funding
This work was supported by the German Research Foundation (DFG) (grant number GR 1569/15-1).
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Dedicated to Prof. Hans Georg Bock on occasion of his 70th birthday.
Appendix
Appendix
Proof of Proposition 2
Due to Proposition 1, we can assume that μ̈ is arbitrarily close to \(\bar {\mu }= 0\). For |μ̈| sufficiently small, we argue below that the exponential controllability condition (24) with respect to stage cost (26) holds with C = 1 for the control candidate \((\bar {K},\bar {c})\). Then we apply Theorem 1 to conclude the assertion.
First, due to \(\bar {\mu }= 0\), we have that \(\bar {c}= 0\). Then, due to \(\bar {\Sigma }= 1\), we see from (13), (15), and (16) that applying \((\bar {K},\bar {c})\) results in
We define
Then the stage cost (26) can be written as
(cf. Lemma 1.)
Our aim is to show \(\tilde {f}(t) \leq e^{-\kappa t}\tilde {f}(0)\) for some κ > 0 (for sufficiently small μ̈2). Then (24) holds with overshoot bound C = 1 and decay rate \(\delta =e^{-\kappa T_{s}}\). We claim that \(\tilde {f}(t) \leq e^{-\kappa t}\tilde {f}(0)\) with
To this end, we prove \(\tilde {f}^{\prime }(t) + \kappa \tilde {f}(t) \leq 0\). First, to shorten the notation, we introduce
Then we can express \(\frac {\tilde {f}^{\prime }(t) + \kappa \tilde {f}(t)}{\bar {\theta }}\) by \(-\frac {h(\tau )}{{(a\tau + 2)}^{3/2}(a + 2)}\), where
which means we have to show that h(τ) ≥ 0. We consider the two cases Σ̈ > 1 respective a > 0 and Σ̈ < 1 respective a < 0. The case Σ̈ = 1 is trivial.
First, let us assume a > 0. In this case, we set μ̈2 = εa for some ε ≥ 0. Then
with
due to a + 2 ≥ aτ + 2. If a1 ≥ 0, which we prove below, then
i.e., it is left to show that \(a_{1}(a\tau + 1)-a_{2}\sqrt {a\tau + 2} \geq 0\). Furthermore, if a3 ≥ 0, then
reducing the problem further to
Since a1 ≥ 0 follows from (55), we only need to prove (55) and a3 ≥ 0. Regarding the latter, with \(\tilde {a}:=a\tau \in [0,\infty )\) and for \(\varepsilon \in [0,\frac {1}{2}]\), we have
where the first inequality follows since a3 is monotonically decreasing in ε for \(\varepsilon \in [0,\frac {1}{2}]\):
Now, we can turn our attention to (55), which we claim holds for \(\varepsilon \in [0,\frac {1}{4}]\). With \(\tilde {a}=a\tau \) as above, we get
which unfortunately is not monotone with respect to ε. We know, however, that
where the limit is positive for \(\varepsilon \in [0,\frac {1}{4}]\). Moreover, in the special case ε = 0, we see that
which, together with (56) proves that h(τ) ≥ 0 for ε = 0. In general, we have
A similar but more involved argument can be made to show that the derivative has at most one root for \(\tilde {a}>0\) and arbitrary but fixed \(\varepsilon \in [0,\frac {1}{4}]\). Then from (56) and (58) follows that h(τ) ≥ 0 for \(\varepsilon \in [0,\frac {1}{4}]\) and a > 0.
For a ∈ (− 1, 0), we cannot choose μ̈2 = εa. Instead, we set μ̈2 = ε ∈ [0, 1] and note that aτ ∈ (− 1, 0). Then
with
due to a < 1. If a1, a4 ≤ 0, then due to aτ ∈ (− 1, 0), we have
Note that \((a\tau + 2)^{3/2} \geq 2\sqrt {2}(a\tau + 1)\) only holds for aτ ∈ (− 1, 0). We only show \(a_{1}-\sqrt {2}a_{4}\geq 0\) and a1 ≤ 0, since a4 ≤ 0 then follows. Regarding the latter, with μ̈2 = ε, we have
In the last step, we prove \(a_{1}-\sqrt {2}a_{4}\geq 0\):
One can set ε = −a ∈ (0, 1) and use \(\tilde {a}=a\tau \) to obtain a function depending only on one variable and prove the assertion directly. An alternative approach is to show that \(a_{1} - \sqrt {2}a_{4}\) is monotonously decreasing in ε for ε ∈ (0, 1), which is easy to show. Recall that this property did not hold in case of a > 1. Consequently, it suffices to consider ε = 0, for which
In particular, we can use (57). Since the derivative is negative for \(\tilde {a}<0\) and the first equation in (56) holds, we have h(τ) ≥ 0 for a < 0. □
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Fleig, A., Grüne, L. L2-Tracking of Gaussian Distributions via Model Predictive Control for the Fokker–Planck Equation. Vietnam J. Math. 46, 915–948 (2018). https://doi.org/10.1007/s10013-018-0309-8
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DOI: https://doi.org/10.1007/s10013-018-0309-8
Keywords
- Model Predictive Control
- Fokker–Planck equation
- Probability density function
- Stochastic optimal control