The Port-Hamiltonian Structure of Vehicle Manipulator Systems
Authors: Ramy Rashad
Organizations: Control and Instrumentation Engineering department and Interdisciplinary Research Center for Smart Mobility and Logistics, King Fahd University of Petroleum and Minerals, Dhahran, 34464, Saudi Arabia
This paper presents a port-Hamiltonian formulation of vehicle-manipulator systems (VMS), a broad class of robotic systems including aerial manipulators, underwater manipulators, space robots, and omnidirectional mobile manipulators. Unlike existing Lagrangian formulations that obscure the underlying energetic structure, the proposed port-Hamiltonian formulation explicitly reveals the energy flow and conservation properties of these complex mechanical systems. We derive the port-Hamiltonian dynamics from first principles using Hamiltonian reduction theory. Two complementary formulations are presented: a standard form that directly exposes the energy structure, and an inertially-decoupled form that leverages the principal bundle structure of the VMS configuration space and is particularly suitable for control design and numerical simulation. The coordinate-free geometric approach we follow avoids singularities associated with local parameterizations of the base orientation. We rigorously establish the mathematical equivalence between our port-Hamiltonian formulations and existing reduced Euler-Lagrange and Boltzmann-Hamel equations found in the robotics and geometric mechanics literature.
Figures & tables
Figure 1: Illustration of vehicle-manipulator systems. Left figure shows a floating-base manipulator h∈Gb=SE(3) while the right figure shows a ground mobile manipulator h∈Gb=S1×R2 .
Map/Variable
Coordinate-free
Coordinate-based
(v,q˙)
gb×TqQm
Rb×Rn
(w,τ)
gb∗×Tq∗Qm
Rb×Rn
Jii,s(q)
gb×TqQm→gis
R6×(b+n)
Jii,b(q)
TqQm→gib
R6×n
Mb(q)
gb→gb∗
Rb×b
Mbm(q)
TqQm→gb∗
Rb×n
Table 1: Coordinate-free and coordinate-based representations of maps and variables in ( 21 )
Figure 2: Port-Hamiltonian formulation of fixed-base manipulator dynamics.
Figure 3: Port-Hamiltonian formulation of moving-base dynamics.
Map/Variable
Coordinate-free
Coordinate-based
(p˙,∂p∂Hkin)
gb∗×gb
Rb×Rb
(q˙,∂q∂Hkin)
TqQm×Tq∗Qm
Rn×Rn
(π˙,∂π∂Hkin)
Tq∗Qm×TqQm
Rn×Rn
adp∼
gb→gb∗
Rb×b
J(p)
Tx∗X→TxX
R(b+2n)×(b+2n)
G
gb∗×Tq∗Qm→TxX
R(b+2n)×(b+n)
Table 2: Coordinate-free and coordinate-based representations of maps and variables in ( 47 )
Figure 4: Principal bundle structure of the VMS configuration space Q=Gb×Qm .
Map
Coordinate-free
Coordinate-based
A(q)
TqQm→gb
Rb×n
Φ(p,q)
TxX→Tx^X
R(b+2n)×(b+2n)
L(p,q)
TqQm→Tq∗Qm
Rn×n
Nb(p^,q^)
Tq^Qm→gb
Rb×n
N^m(q^,π^)
Tq^Qm→Tq^∗Qm
Rn×n
B(p^,q^)
Tq^Qm→Tq^∗Qm
Rn×n
Table 3: Coordinate-free and coordinate-based representations of maps in ( 59 )
Figure 5: The proposed port-Hamiltonian formulations of VMS dynamics and their corresponding Dirac structures. Left figure shows the port-Hamiltonian formulation ( 47 ) while the right figure shows the inertially-decoupled port-Hamiltonian formulation ( 59 ). The intermediate figure illustrates the change of coordinates ϕ(q) .
Map
Coordinate-free
Coordinate-based
Pb(q,q˙)
gb→gb
Rb×b
Eb(v^,q)
TqQm→gb
Rb×n
C^m(q,q˙)
TqQm→Tq∗Qm
Rn×n
B^(v^,q^)
Tq^Qm→Tq^∗Qm
Rn×n
M^,C^1,C^2
gb×TqQm→gb∗×Tq∗Qm
R(b+n)×(b+n)
Table 4: Coordinate-free and coordinate-based representations of maps in ( 85 )
This paper investigates the problem of data-driven modeling of port-Hamiltonian systems while preserving their intrinsic Hamiltonian structure and stability properties. We propose a novel neural-network-based port-Hamiltonian modeling technique that relaxes the convexity constraint commonly imposed by neural network-based Hamiltonian approximations, thereby improving the expressiveness and generalization capability of the model. By removing this restriction, the proposed approach enables the use of more general non-convex Hamiltonian representations to enhance modeling flexibility and accuracy. Furthermore, the proposed method incorporates information about stable equilibria into the learning process, allowing the learned model to preserve the stability of multiple isolated equilibria rather than being restricted to a single equilibrium as in conventional methods. Two numerical experiments are conducted to validate the effectiveness of the proposed approach and demonstrate its ability to achieve more accurate structure- and stability-preserving learning of port-Hamiltonian systems compared with a baseline method.
Binh Nguyen, Nam T. Nguyen, Truong X. Nghiem
Department of Electrical and Computer Engineering, College of Engineering and Computer Science, University of Central Florida, Orlando, FL 32816, USA
Finite-dimensional Koopman models enable efficient linear prediction and control of nonlinear robotic systems. However, models learned purely from trajectory data may violate the energetic structure of the underlying mechanics, producing predictions that exhibit artificial energy growth and diverge under recursive propagation. This work presents a structure-preserving Koopman framework for Euler-Lagrange systems built on generalized-momentum coordinates. The momentum transformation exposes the mechanical actuation as a known, state-independent port, which is preserved explicitly in the lifted dynamics. A structure-constrained neural architecture is developed to jointly learn the lifting functions and a port-Hamiltonian Koopman generator, rendering the learned dynamics passive by construction rather than through penalty terms or post-hoc projection. A Cayley-midpoint discretization further preserves the corresponding storage-dissipation balance exactly in discrete time. These properties are established analytically by deriving the discrete storage balance and associated stability guarantees of the learned predictor. Simulation and experimental studies demonstrate improved prediction accuracy, data efficiency, and closed-loop tracking over Koopman baselines, with increasing gains for higher-dimensional systems.
Rajpal Singh, Aditya Singh, Jishnu Keshavan
Department of Mechanical Engineering, Indian Institute of Science, Bangalore, Karnataka 560012, India
We develop a physics-informed learning framework for energy-shaping control of port-Hamiltonian (pH) systems from trajectory data. The proposed approach co-learns a pH system model and an optimal energy-balancing passivity-based controller (EB-PBC) through alternating optimization with policy-aware data collection. At each iteration, the system model is refined using trajectory data collected under the current control policy, and the controller is re-optimized on the updated model. Both components are parameterized by neural networks that embed the pH dynamics and EB-PBC structure, ensuring interpretability in terms of energy interactions. The learned controller renders the closed-loop system inherently passive and provably stable, and exploits passive plant dynamics without canceling the natural potential. A dissipation regularization enforces strict energy decay during training, thereby enhancing robustness to sim-to-real gaps. The proposed framework is validated on state-regulation and swing-up tasks for planar and torsional pendulum systems.
Ankur Kamboj, Biswadip Dey, Vaibhav Srivastava
Electrical and Computer Engineering, Michigan State University, East Lansing, MI 48824 · Meta Reality Labs, Redmond, WA 98052