Double Wishbone Suspension Configuration

• Double wishbone suspension is a configuration that employs two control arms per wheel arranged in a parallelogram to maintain accurate wheel kinematics.
• It utilizes multibody dynamics modeling to simulate forces and displacements, enabling precise predictions of chassis acceleration and ride comfort.
• The design integrates advanced optimization methodologies and robust testing to enhance handling, transmission efficiency, and environmental resilience.

A double wishbone suspension configuration, also known as dual A-arm suspension, is a multibody mechanical assembly wherein each wheel is mounted via two control arms (upper and lower) arranged in a parallelogram geometry. This architecture provides independent control of wheel kinematics, improved handling, and adaptable terrain performance, and is foundational in both terrestrial automotive engineering and advanced planetary robotics.

1. Double Wishbone Architecture and Kinematic Foundations

A double wishbone suspension employs two rigid arms per wheel: the upper and lower control arms (the “wishbones”), each connected to the chassis at two points and to the wheel carrier at a single point. The geometric arrangement (parallelogram structure) maintains the wheel’s vertical motion with minimal camber change, ensuring precise wheel alignment under suspension travel. This feature is critical for maximizing road contact, handling stability, and minimizing tire wear.

A representative schematic (from (Luna et al., 7 Oct 2025)) can be produced in LaTeX using the following structure:

\documentclass{standalone} \usepackage{tikz} \begin{document} \begin{tikzpicture}[line cap=round, line join=round, >=triangle 45] % Upper and lower wishbones \coordinate (B) at (2,1); % chassis, upper \coordinate (C) at (2,-1); % chassis, lower \coordinate (D) at (4,0.5); % wheel carrier, upper \coordinate (E) at (4,-0.5); % wheel carrier, lower % Drawing arms \draw[thick] (B) -- (D); \draw[thick] (C) -- (E); % Connect to wheel \draw[thick, dashed] (D) -- (5, 0); \draw[thick, dashed] (E) -- (5, 0); % Wheel \draw (5,0) circle (0.8); \end{tikzpicture} \end{document}

The kinematic benefit of this topology is that wheel motion in the vertical direction (z) is controlled by the effective lever arms, minimizing undesirable lateral or rotational movement.

2. Multibody Dynamics Modeling of Double Wishbone Systems

Advanced simulation and optimization of double wishbone suspension necessitate a multibody dynamics (MBD) approach, enabling explicit modeling of the degrees of freedom and constraint enforcement for all suspension elements. According to (Cheong et al., 11 Sep 2024), the dynamics are computed by coupling the mass matrix $\mathbf{M}$, generalized displacement vector $\mathbf{q}$, constraint Jacobian $\mathbf{C}_q$, and Lagrange multipliers $\boldsymbol{\lambda}$ as follows:

$$\begin{bmatrix} \mathbf{M} & \mathbf{C}_q^T \ \mathbf{C}_q & \mathbf{0} \end{bmatrix} \begin{bmatrix} \ddot{\mathbf{q}} \ \boldsymbol{\lambda} \end{bmatrix} = \begin{bmatrix} \mathbf{F}_e + \mathbf{F}_v \ \mathbf{F}_c \end{bmatrix}$$

Here, $\mathbf{q}$ typically expands to include the wheel, upper and lower arms, and linkages. Suspension forces and displacements (e.g., $q^z_{U,i}$ for upper arm vertical displacement) yield realistic predictions of chassis acceleration ($\ddot{z}_C$, $\ddot{\theta}_C$) and ride comfort.

3. Optimization Methodologies for Suspension Configuration

Recent advances demonstrate mixed-fidelity multidisciplinary optimization, notably the integration of a high-fidelity suspension module into a low-fidelity whole-vehicle model (Cheong et al., 11 Sep 2024). The Modular Analysis and Unified Derivatives (MAUD) approach combines analytic derivatives with finite-difference sensitivities from black-box simulators to produce system gradients for constrained optimization (e.g., using IPOPT).

Collocation methods are employed to discretize state and design variables over time segments, enforcing defect constraints $\eta_j(\bar{u}, \dot{\bar{u}}) = 0$ and yielding a unified optimization over both state evolution and design parameters such as spring constants $k_{S,i}$ and damping coefficients $c_{S,i}$.

4. Configuration Design and Discrete Optimization

Configuration design of double wishbone systems can be formalized as a black-box, discrete, constrained optimization problem (Cheong et al., 14 Mar 2025). The assembly is modeled as an undirected graph $G = \langle Y, Z \rangle$, where joints (vertices) and components (edges) represent potential design variables. Optimization seeks to minimize a performance cost function, aggregating terms such as vertical acceleration, frame displacements, and component count:

$$f(X) = \sum_{t}^{T} \left[a_{z,t}^{(q_1)} + w_1 (d_{t}^{(q_2)} + d_{t}^{(q_3)}) \right] + w_2 |\{ y_i : y_i \ne 0\}|$$

Bivariate Marginal Distribution Algorithm (BMDA), with adaptive chi-square testing and Gibbs sampling, is used to model variable dependencies and generate candidates efficiently. Constraint programming repair operators ensure physical feasibility, enforcing connectivity and compatibility constraints and repairing infeasible samples via propagation and backtracking (see Equations (18), (24) in (Cheong et al., 14 Mar 2025)).

5. Integration with Transmission and Steering Systems

The double wishbone configuration is frequently integrated with cardan joints and capstan drive steering, as highlighted in planetary robotics (Luna et al., 7 Oct 2025). Cardan joints, arranged in series, allow for angular deflection as the suspension moves. The parallelogram geometry ensures angular deflections are equal and opposite ($\theta_1 + \theta_2 \approx 0$), minimizing mechanical loss.

Capstan drive steering utilizes tensioned steel cables routed through protective channels to actuate a drum, achieving steering efficiencies up to 91%. This design maintains steering reference as the suspension articulates and allows independent suspension, traction, and steering management while centralizing actuation within a thermally controlled “warm box.”

6. Trade-Off Analysis, Environmental Robustness, and Validation

Systematic trade-off studies compare double wishbone configurations against alternatives such as rocker-bogie mechanisms in terms of modularity, transmission efficiency, mass distribution, reliability, and maintainability (Luna et al., 7 Oct 2025). The double wishbone (Model 1) was favored due to the following:

• Transmission efficiency (wheel drive: 43%–99%; steering: ~91%)
• Lower unsprung mass and mechanical simplicity
• Modular architecture facilitating incremental testing and replacement

Centralizing actuators in thermally protected environments provides resilience against thermal cycling ($-180^\circ$C to $+120^\circ$C) and dust contamination, ensuring performance meets long-distance requirements ($>50$ km) with minimal degradation.

Validation protocols comprise breadboard manufacturing (1:3 scale) with dust isolation, thermal vacuum testing, regolith exposure, and torque/efficiency characterization (wheel drive torque: $\sim$30.01–86.11 Nm; steering torque: $\sim$26.01–32.09 Nm), with full integration testing scheduled for Q1 2026.

7. Impact on Vehicle Dynamics, Ride Comfort, and Future Perspectives

Embedding high-fidelity double wishbone modules within vehicle dynamics optimization frameworks enables nuanced modeling of suspension forces, chassis accelerations, and tire-road interactions. Improved prediction and control of $\ddot{z}_C$ and $\ddot{\theta}_C$ directly enhance ride comfort and driving performance (Cheong et al., 11 Sep 2024). Mixed-fidelity and BMDA-GS-CP methods minimize costly evaluations and accelerate iterative design, proving superior to genetic algorithms in convergence speed.

The platform for designer-informed statistical analysis of joint/component dependencies provides actionable insight for further manual or automated improvements. Future directions include joint optimization of discrete configuration and continuous suspension parameters, extending applicability to diverse planetary and terrestrial vehicles.

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In summary, the double wishbone suspension configuration delivers superior adaptability, efficient transmission integration, and robust optimization strategies, making it a preferred solution for high-performance and robotics applications. Its evolution is driven by advances in multibody modeling, optimization algorithms, and environmental resilience measures, with empirical validation underpinning its continued adoption in complex mobility systems.