Abstract
Numerical methods that preserve geometric invariants of the system, such as energy, momentum or the symplectic form, are called geometric integrators. In this paper we present a method to construct symplectic-momentum integrators for higher-order Lagrangian systems. Given a regular higher-order Lagrangian \(L:T^{(k)}Q\rightarrow {\mathbb {R}}\) with \(k\ge 1\), the resulting discrete equations define a generally implicit numerical integrator algorithm on \(T^{(k-1)}Q\times T^{(k-1)}Q\) that approximates the flow of the higher-order Euler–Lagrange equations for L. The algorithm equations are called higher-order discrete Euler–Lagrange equations and constitute a variational integrator for higher-order mechanical systems. The general idea for those variational integrators is to directly discretize Hamilton’s principle rather than the equations of motion in a way that preserves the invariants of the original system, notably the symplectic form and, via a discrete version of Noether’s theorem, the momentum map. We construct an exact discrete Lagrangian \(L_d^e\) using the locally unique solution of the higher-order Euler–Lagrange equations for L with boundary conditions. By taking the discrete Lagrangian as an approximation of \(L_d^e\), we obtain variational integrators for higher-order mechanical systems. We apply our techniques to optimal control problems since, given a cost function, the optimal control problem is understood as a second-order variational problem.
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Notes
For \(k=1\), recall writing \(\delta \dot{q}=\dot{(\delta q)}\) when deriving the Euler–Lagrange equations, assuming that q is \(C^2\).
By this we mean, from now on, that there exists \(h_0>0\) such that for all \(h\in (0,h_0)\) the definition or proof holds.
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Acknowledgments
This work has been supported by MICINN (Spain) Grant MTM 2013-42870-P, ICMAT Severo Ochoa Project SEV-2011-0087 and IRSES-project “Geomech-246981.” The research of S. Ferraro has been supported by CONICET Argentina (PIP 2013-2015 GI 11220120100532CO), ANPCyT Argentina (PICT 2013-1302) and SGCyT UNS. We would like to thank the referee for the helpful comments.
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Communicated by Anthony Bloch.
Appendix: A Technical Result for Sect. 2
Appendix: A Technical Result for Sect. 2
Let E be the kernel of g, where \(g=(g_0,\ldots ,g_{k-1}):C^{l}([0,1],{\mathbb {R}}^{n})\rightarrow ({\mathbb {R}}^{n})^{k}\) and \(g_{j}[\cdot ]=\langle \langle b_{j}^{[k]},\cdot \rangle \rangle \). In the context of Sect. 2.5, E is the tangent space of the constraint set defined using the linear constraints \(g_{j}\), and l is either 0 or k.
In this Appendix we show that the orthogonal complement of E is the space F of \({\mathbb {R}}^{n}\)-valued polynomials of degree at most \(k-1\),
where \(b_j^{[k]}\), \(j=0,\ldots ,k-1\), is a basis of the space of real-valued polynomials of degree at most \(k-1\) consisting of orthonormal polynomials on [0, 1].
Lemma 6.1
\(F=E^{\perp }\), where the orthogonal complement is taken with respect to the inner product \(\llbracket ,\rrbracket \) in \(C^{l}([0,1],{\mathbb {R}}^{n})\).
Proof
We will prove that E and F are orthogonal (with zero intersection) and that their sum is the whole space \(C^{l}([0,1],{\mathbb {R}}^{n})\).
Let \(e\in E\) and \(c^{j}b_{j}^{[k]}\in F\).
since \(e\in E={\text {Ker}}g\).
The fact that \(E\cap F=\{0\}\) can be obtained either by using that the inner product is nondegenerate or directly as follows. Take \(e\in E\cap F\), so \(e=c^{j}b_{j}^{[k]}\). For all \(j'\), we have \(0=g_{j'}[e]=\langle \langle b_{j'}^{[k]},c^{j}b_{j}^{[k]}\rangle \rangle =c^{j'}\), which means that \(e=0\).
Finally, take \(e\in C^{l}([0,1],{\mathbb {R}}^{n})\). Write
The third term is in F. The remaining part of the right-hand side is in E since for all \(j'\),
Therefore, \(C^{l}([0,1],{\mathbb {R}}^{n})=E+F\). From the first part of the proof, we obtain that there is an orthogonal decomposition \(C^{l}([0,1],{\mathbb {R}}^{n})=E\oplus F\). \(\square \)
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Colombo, L., Ferraro, S. & Martín de Diego, D. Geometric Integrators for Higher-Order Variational Systems and Their Application to Optimal Control. J Nonlinear Sci 26, 1615–1650 (2016). https://doi.org/10.1007/s00332-016-9314-9
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DOI: https://doi.org/10.1007/s00332-016-9314-9