Riemannian Formulation of Pontryagin’s Maximum Principle for the Optimal Control of Robotic Manipulators

In this work, we consider robotic systems for which the mass tensor is identified to be the metric in a Riemannian manifold. Cost functional invariance is achieved by constructing it with the identified metric. Optimal control evolution is revealed in the form of a covariant second-order ordinary di...

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Bibliographic Details
Main Authors: Dubois, F. (Author), Ramírez-De-ávila, H.C (Author), Rojas-Quintero, J.A (Author)
Format: Article
Language:English
Published: MDPI 2022
Subjects:
Online Access:View Fulltext in Publisher
LEADER 02174nam a2200229Ia 4500
001 10.3390-math10071117
008 220425s2022 CNT 000 0 und d
020 |a 22277390 (ISSN) 
245 1 0 |a Riemannian Formulation of Pontryagin’s Maximum Principle for the Optimal Control of Robotic Manipulators 
260 0 |b MDPI  |c 2022 
856 |z View Fulltext in Publisher  |u https://doi.org/10.3390/math10071117 
520 3 |a In this work, we consider robotic systems for which the mass tensor is identified to be the metric in a Riemannian manifold. Cost functional invariance is achieved by constructing it with the identified metric. Optimal control evolution is revealed in the form of a covariant second-order ordinary differential equation featuring the Riemann curvature tensor that constrains the control variable. In Pontryagin’s framework of the maximum principle, the cost functional has a direct impact on the system Hamiltonian. It is regarded as the performance index, and optimal control variables are affected by this fundamental choice. In the present context of cost functional invariance, we show that the adjoint variables are the first-order representation of the second-order control variable evolution equation. It is also shown that adding supplementary invariant terms to the cost functional does not modify the basic structure of the optimal control covariant evolution equation. Numerical trials show that the proposed invariant cost functionals, as compared to their non-invariant versions, lead to lower joint power consumption and narrower joint angular amplitudes during motion. With our formulation, the differential equations solver gains stability and operates dramatically faster when compared to examples where cost functional invariance is not considered. © 2022 by the authors. Licensee MDPI, Basel, Switzerland. 
650 0 4 |a invariance 
650 0 4 |a multibody dynamics 
650 0 4 |a optimal control 
650 0 4 |a Riemann curvature tensor 
650 0 4 |a Riemannian geometry 
650 0 4 |a robotics 
700 1 |a Dubois, F.  |e author 
700 1 |a Ramírez-De-ávila, H.C.  |e author 
700 1 |a Rojas-Quintero, J.A.  |e author 
773 |t Mathematics