Model Predictive Path Integral Control (MPPI)

Updated 10 November 2025

MPPI is a sampling-based model predictive control method that computes optimal input sequences for nonlinear systems using stochastic trajectory sampling.
It integrates path-integral control theory with importance sampling to handle arbitrary dynamics, non-differentiable costs, and stringent constraints.
The SMPPI variant adds input-lifting and a quadratic action-variation cost to suppress actuator chattering, ensuring smoother control in real-world applications.

Model Predictive Path Integral Control (MPPI) is a sampling-based model predictive control (MPC) methodology designed for nonlinear systems and non-convex optimization problems. At its core, MPPI leverages stochastic trajectory sampling and path-integral control theory to compute optimal input sequences, accommodating arbitrary dynamics, non-differentiable costs, stringent constraints, and complex interaction models. MPPI's theoretical foundation is rooted in the Feynman–Kac path-integral representation of stochastic optimal control, enabling forward Monte Carlo integration as a substitute for traditional backward dynamic programming approaches.

1. Mathematical Foundations and Standard Algorithm

MPPI considers discrete-time system dynamics of the form: $x_{t+1} = f(x_t, u_t)$ where $u_t$ is a mean control signal, and actual inputs are perturbed by zero-mean Gaussian noise $\epsilon_t \sim \mathcal{N}(0, \Sigma)$ so that $u_t' = u_t + \epsilon_t$ . Over a finite horizon $T$ , MPPI computes trajectory rollouts, each incurring a per-trajectory cost: $S(V) = \varphi(x_T) + \sum_{t=0}^{T-1} c(x_t)$ and evaluates the path-integral cost functional using importance sampling: $q^*(V) = \frac{1}{\eta} \exp\Bigl(-\frac{S(V)}{\lambda}\Bigr) p(V)$ where $p(V)$ is the uncontrolled trajectory density, $\lambda$ is the temperature parameter (exploitation–exploration trade-off), and $\eta$ normalizes the distribution.

The MPPI update rule for control sequence $U = \{u_0, \dots, u_{T-1}\}$ is: $u_t^{i+1} = u_t^i + \sum_{k=0}^{K-1} w_k \epsilon_t^k$ with weights $w_k \propto \exp\left(-\frac{C(V^k) - \beta}{\lambda}\right)$ , where $C(V^k)$ denotes the trajectory cost including control-noise corrections, and $\beta = \min_k C(V^k)$ enhances numerical stability.

2. Smoothness, Input-Lifting, and Chattering Suppression

The stochasticity of MPPI rollouts often introduces actuator chattering, especially in fast-changing environments. To resolve this, the method known as "Smooth Model Predictive Path Integral Control without Smoothing" (SMPPI) (Kim et al., 2021) integrates the following innovations:

Input-Lifting: The derivative control sequence $U$ (input rates) is decoupled from the action sequence $A$ (actual commands) by integration: $a_t = a_{t-1} + u_t \Delta t$ . Sampling is conducted in $U$ , naturally enforcing actuator rate bounds.
Quadratic Action-Variation Cost:

$\Omega(A) = \sum_{t=1}^{T-1} (a_t - a_{t-1})^\top \omega (a_t - a_{t-1})$

with diagonal $\omega \succeq 0$ penalizes large time-axis variations in $A$ , directly in the MPPI cost structure.

This intrinsic smoothing replaces post-hoc filters and preserves the information-theoretic derivation for non-affine dynamics. The update law remains unchanged in functional form; thus, SMPPI maintains the original KL-free-energy interpretation and theoretical convergence guarantees.

3. Implementation: Pseudocode and Real-World Deployment

The SMPPI algorithm according to (Kim et al., 2021) proceeds as follows:

initialize U^0, A^0
for i in range(I):
    x0 = current_state
    for k in range(K):
        x, a_prev = x0, A^i_{-1}
        C_k = 0
        ε_k = sample_noise_vector_array(K, T, Σ)
        for t in range(T):
            u_t^k = U^i_t + ε_t^k
            a_t^k = a_prev + u_t^k * Δt
            a_prev = a_t^k
            x = f(x, a_t^k)
            C_k += c(x) + λ * u_t^T * Σ^{-1} * ε_t^k
        C_k += ϕ(x) + Ω(a_0^k, ..., a_{T-1}^k)
    β = min_k C_k
    w_k = exp(-(C_k - β) / λ)
    U^{i+1} = U^i + sum_k( w_k * ε^k )
    A^{i+1} = A^i + U^{i+1} * Δt
    apply_action(A^{i+1}_0)
    shift_sequences()

Computational Requirements: The integration of input-lifting and action cost increases overhead slightly (integration and evaluation of $\Omega(A)$ ), yet is negligible relative to parallelized sampling.
Tuning: The choice of $\omega$ balances smoothness (higher values) versus responsiveness; simulated actuators require appropriate $\Sigma$ for stochastic exploration. Accurate models are required; learned neural network dynamics are supported.

4. Comparative Empirical Results and Performance Metrics

Swing-Up Pendulum Task

Neural dynamics model (online learning).
Cost: $c([\theta, \dot\theta]) = \theta^2 + 0.1\dot\theta^2$ , $T=20$ .
SMPPI achieved upright convergence from all initial angular velocities; baselines with external smoothing or naive action costs failed due to theoretical violations or improper tuning.

Autonomous Driving Task

CarMaker + Volvo XC90, variable friction, neural dynamics.
Cost: track penalty, speed error $(v_x - v_{ref})^2$ , slip penalty $\sigma^2$ , hard slip constraint $|\sigma| > 0.2$ rad.
Controllers: baseline MPPI (no smoothing), variants with Savitzky–Golay filtering, SMPPI with and without $\Omega$ .
SMPPI with $\Omega$ completed all sharp corners, delivered highest minimum speeds and constrained slip angles (≤11°), with the fastest lap times; it rapidly adapted to changing friction without chattering.

5. Theoretical Implications and Implementation Trade-offs

Action-Variation Cost Integration: By embedding smoothness costs ( $\Omega(A)$ ) within trajectory evaluation rather than external filtering, SMPPI avoids violating input bounds and circumvents phase delays introduced by causal filtering.
Dual-Axis Smoothing: SMPPI enables two-fold smoothing—iteration axis ("i-axis") via control-variance restriction, and time axis ("t-axis") via action-variation costs.
Limitations:
- Tuning $\omega$ and $\Sigma$ is scenario-dependent.
- SMPPI requires sufficiently accurate system identification to realize agility; poor models undermine benefit.
- Compared to vanilla MPPI, increased computation is modest but should be evaluated for resource-constrained systems.

6. Connections to Broader MPPI Research Directions

Various strategies have been proposed for smoothing and sample efficiency in MPPI:
- Spline interpolation and SVGD updates (Miura et al., 2024, Aldrich et al., 3 Nov 2025).
- Low-pass noise filtering (frequency-domain control) (Kicki, 13 Mar 2025).
- Post-hoc smoothers (e.g., Savitzky–Golay) are inferior for guaranteeing bounded derivatives (Andrejev et al., 15 Apr 2025).
SMPPI's input-lifting approach maintains all theoretical properties of path-integral control under non-affine dynamics, distinguishing it from naive cost augmentations and post-sampling filters.

7. Practical Takeaways and Guidelines

SMPPI is most effective for systems where actuator chattering jeopardizes real-world control (e.g., robots with hard rate limits, neural-network-driven controllers, autonomous cars on varying surfaces).
No external filtering is necessary—smoothness is controlled directly in the sampling-based optimization.
SMPPI enables aggressive, agile maneuvers while retaining stability and smooth actuator profiles, outperforming externally-smoothed MPPI in both classical and neural-network-driven nonlinear benchmarks.

In summary, Model Predictive Path Integral Control and its smooth (SMPPI) variant formalize the sampling-based solution to real-time, robust nonlinear control, integrating smoothness directly into optimization rather than via filtering. This approach substantiates chattering-free control in complex tasks, with theoretical and empirical validation for neural and classical dynamic models, and is rapidly extensible to sophisticated modern robotics and autonomous driving domains.