Analytical Actuator Model Insights

• Analytical actuator models are physics-based frameworks that capture multi-domain actuator behavior via explicit equations derived from first principles or experimental calibration.
• They enable simulation, control, and design optimization by preserving internal variables and offering mechanistic reasoning across electromechanical, fluidic, and soft robotic systems.
• Applications include performance analysis, model-based control, and design trade-offs, achieving high accuracy and computational efficiency in practical actuator implementations.

An analytical actuator model is a mathematical framework that captures the physical, electrical, or fluidic principles underpinning actuator behavior using explicit, structure-preserving equations. Such models are derived from first principles or experimentally-validated parameterizations that map input signals (voltages, pressures, forces) to actuator outputs (position, torque, velocity, deformation) without reliance on black-box fitting or primarily data-driven approaches. Analytical actuator models are employed across electromechanical, fluidic, chemical, and soft robotic domains, enabling inference of dynamics, design optimization, model-based control, and rigorous analysis of performance, coupling, and scaling laws.

1. Fundamental Structure and Scope

Analytical actuator models are grounded in explicit physical laws and system topology. For electromechanical systems, they formalize the coupling between electrical and mechanical domains (e.g., PMSM drive equations, transmission gearing, load coupling) as a set of ordinary differential or algebraic equations with well-defined parameters such as resistance, inductance, inertia, torque and back-EMF constants, and friction coefficients (Bahari et al., 19 Sep 2025, Mohammadi et al., 2023). In fluidic or hydraulic actuators, dominant phenomena such as valve flow, volume dynamics, and pressure–force mapping are abstracted as algebraic or low-order integral–differential equations under simplifying assumptions (e.g., quasistatic limits, incompressibility, or lossless transmission) (Kikuuwe et al., 2021, Lee et al., 16 Jan 2026).

A defining characteristic is the retention of internal variables and intermediary states (e.g., voltages, angular velocities, chamber pressures, charge densities), which enables mechanistic reasoning, state estimation, and direct physical interpretability. Explicit parameterization facilitates calibration, uncertainty quantification, and sensitivity analysis.

2. Kinematic and Dynamic Formulation

Actuator analytical models incorporate the kinematic constraints dictated by mechanical architecture and multi-domain energy flows. In multicoupled systems such as the Parallel Force/Velocity Actuator (PFVA), kinematic relations are derived from gear train topology (e.g., Dual-Input-Single-Output epicyclic gears), yielding linear mappings between input (motor) and output (joint) states (Rabindran et al., 2014): $$\dot{\theta}_o = R_v \dot{\theta}_v + R_f \dot{\theta}_f$$ where $R_v$, $R_f$ are gear ratios, and $\dot{\theta}_v$, $\dot{\theta}_f$ are input velocities.

The input-reflected inertia matrix is systematically constructed via: $I_{\phi\phi}^* = G^T I_{\theta} G + I_M$ revealing both direct and dynamic coupling terms (off-diagonal $\mu$). For multi-path actuators, key parameters such as the relative scale factor (RSF) $\rho = R_f/R_v$ fundamentally govern coupling and decoupling regimes (Rabindran et al., 2014).

For actuators in soft robotics or fluidics, similar reconstructive logic applies. In fibre-reinforced fluidic actuators, all nonlinearities due to hyperelasticity, geometry, and screw kinematics are preserved in their full three-dimensional form, with pressure–curvature–moment relationships established through quasi-static equilibrium and explicit integration over actuator geometry (Cacucciolo et al., 2016).

In heavy-duty manipulators with linearly actuated parallel mechanisms, analytical models are constructed in the dual Lie algebra $se(3)$, supporting recursive inertia and wrench propagation across both serial and parallel-closed kinematic chains. This enables closed-form expressions for total actuator force, actuator acceleration, and the complete base-frame wrench assembly via adjoint transformations and articulated-body algorithms (Alvaro et al., 2024).

3. Key Analytical Model Types and Key Equations

Electromechanical Linear Actuator (EMLA)

• Electrical subsystem (PMSM in $dq$-frame):

$$\frac{di_d}{dt} = \frac{1}{L_d}v_d - \frac{R_s}{L_d}i_d + \frac{L_q}{L_d}P\omega_m i_q$$

$$\frac{di_q}{dt} = \frac{1}{L_q}v_q - \frac{R_s}{L_q}i_q - \frac{L_d}{L_q}P\omega_m i_d - \frac{P\psi_f}{L_q}\omega_m$$

$$\tau_e = \frac{3}{2}P [\psi_f i_q + (L_d-L_q)i_d i_q]$$

• Mechanical subsystem (including direction-dependent transmission efficiency and friction):

$$\ddot{x}_L = \begin{cases} \displaystyle\frac{nN_g[\tau_e - \tau_c - \kappa_f (f_v \dot{x}_L + F_L)]}{M_t + nN_g \kappa_f J_m}, & \ddot x_L \ge 0 \ \displaystyle\frac{\kappa_b(\tau_e - \tau_c - nN_g f_v \dot{x}_L) - F_L}{M_t + nN_g\kappa_b J_m}, & \ddot x_L < 0 \end{cases}$$

Hydraulic and Fluidic Actuators

• Discrete-time torque update for hydraulic joints (Lee et al., 16 Jan 2026):

$$\tau_{\mathrm{next}} = k_1 R^2 (q_{\mathrm{des}} - q) + (1 - k_2) \tau - k_3 R^2 \dot{q} + k_4 R (q_{\mathrm{des}} - q) \max(-\tau \operatorname{sgn}(q_{\mathrm{des}} - q), 0)$$

• Nonsmooth quasistatic force–velocity map for 4-valve actuators (Kikuuwe et al., 2021):

$f \in \Gamma(v) = \Gamma_h(v) - \Gamma_r(v)$

with $\Gamma_h, \Gamma_r$ set-valued via branchwise algebraic laws with orifice-flow and relief constraints.

Soft and Electrochemical Actuators

• 3D ion-transport and curvature mapping in IPMC (Annabestani et al., 2019):

$$\frac{\partial \phi}{\partial t} - d\nabla^2\phi + K\phi = 0$$

with curvature enforced by a large-deflection nonlinear beam ODE and arc-length-constrained tip displacement:

$$\frac{w_{zz}}{(1 + w_z^2)^{3/2}} = \frac{M_{zy}(z, t)}{Y I}$$

Parallel-Coupled/Redundant Actuators

• Dynamic coupling in PFVA (Rabindran et al., 2014):

$\mu(\rho) = \frac{I_\theta \rho}{(1 + \rho)^2}$

demonstrating how scaling the gear ratios ($\rho$) affects inertial coupling between force and velocity input paths.

4. Practical Applications and Performance Analysis

Analytical actuator models are central to real-time control, optimal design, and sim-to-real transfer. Their closed-form structure allows embeddings into optimization loops and predictive controllers, e.g., NSGA-II-based actuator selection (Bahari et al., 19 Sep 2025), model-predictive control (Alvaro et al., 2024), and reinforcement learning workflows for hydraulic legged robots (Lee et al., 16 Jan 2026). The models’ parameter dependencies enable mechanism-aware design tradeoffs:

• For PFVA designs, increasing the relative scale factor $\rho$ rapidly reduces dynamic coupling, permitting nearly independent operation of force and velocity actuators and guiding epicyclic gear train selection (Rabindran et al., 2014).
• In heavy electro-mechanical actuators, direction-sensitive transmission equations inform efficiency-optimal sizing, sensorless estimation, and robust control sequencing (Bahari et al., 19 Sep 2025).
• Explicit models of soft, origami- or fibre-reinforced pneumatic actuators clarify pathway from geometry, pressure input, and fabric patterning to predicted curvature, deformation, and force (Cacucciolo et al., 2016, Li et al., 2021, Ota et al., 27 Oct 2025).

Empirical validation typically shows sub-5% RMS error for torque or displacement, with analytical models substantially outperforming neural network models both in accuracy (lower RMSE and MAPE) and computational efficiency, especially in extrapolation to dynamics or rare events (Lee et al., 16 Jan 2026, Annabestani et al., 2022).

5. Limitations, Assumptions, and Extensions

Analytical actuator models, while interpretable and fast, incorporate assumptions for tractability:

• Neglect of elastic/damping in gear trains or actuated paths (often modeled as rigid and lossless, or with coarse viscous/frictional approximations).
• Simplified treatment of actuator nonlinearities; e.g., linearized pressure–force or force–decay relations in hydraulics, or geometric scale factor approximations in parallel actuators (Rabindran et al., 2014, Lee et al., 16 Jan 2026).
• Idealizations of flow as incompressible and steady; exclusion of higher-order or unmodeled coupled dynamics, hysteresis, or magnetics saturation.
• Parametric fitting sufficient for the modeled regime, but potentially inaccurate outside calibration data or in extreme environments (e.g., coupling elastic deformation with fast, high-impact events).

A frequent direction is the hybridization of analytical models with data-driven residual surrogates or neural networks to capture unmodeled dynamics and hysteresis, especially in complex ionic or soft materials (Annabestani et al., 2022). For high-end applications involving complex mechanical topology or real-time control, recursive formulations in $se(3)$ or physics-informed Kriging surrogates have shown to maintain both scalability and accuracy (Alvaro et al., 2024, Bahari et al., 19 Sep 2025).

6. Representative Analytical Actuator Models in the Literature

Actuator Domain	Core Formulation	Referenced Paper
PFVA (parallel gear)	Epicyclic kinematics, input inertia	(Rabindran et al., 2014)
Hydraulic (robot leg)	Discrete-time explicit torque update	(Lee et al., 16 Jan 2026)
Heavy-duty EMLA	PMSM, direction-dependent transmission	(Bahari et al., 19 Sep 2025)
Soft/fibre fluidic	Neo-Hookean, 3D geometry, equilibrium	(Cacucciolo et al., 2016Ota et al., 27 Oct 2025)
IPMC ionics	3D NP-Poisson, beam ODE	(Annabestani et al., 2019)
Quasistatic hydraulic	Nonsmooth force–velocity mapping	(Kikuuwe et al., 2021)

Each model delivers closed-form or nearly closed-form equations mapping physical input parameters to output actuator behavior, supporting simulation, estimation, and synthesis for model-based design and control.

7. Conclusions and Future Perspectives

Analytical actuator models provide a rigorous, physically interpretable basis for actuator characterization in robotics, automation, fluidics, and soft systems. Continued extension of these models to account for elasticity, damping, saturated nonlinearities, and multi-domain coupling is necessary for high-fidelity real-world emulation and optimization. Hybrid analytical–ML approaches are increasingly valuable for bridging the gap between tractable modeling and high accuracy in unmodeled or highly nonlinear regimes. Design guidance arising from analytical structures, such as the scaling laws for dynamic coupling in gear trains (Rabindran et al., 2014), demonstrates the enduring utility of analytical modeling frameworks in advanced actuator and mechanism research.