Model Predictive Path Integral (MPPI)是一种基于路径积分的预测模型控制方法,被广泛应用于机器人运动规划、飞行器轨迹控制和自动驾驶车辆路径生成等领域。该方法通过将控制问题转化为优化问题,并使用路径积分来评估不同控制策略的性能,从而找到最佳的控制策略来实现所需任务。 2.2 原理解析 MPPI的原理是基于随机采样和近...
本文采用Model Predictive Path Integral (MPPI)的方法，用多元高斯分布\left( \mu ^0,\sigma ^0 \right) _{t:t+H}独立表示动作空间为m的H个动作，根据a_t\sim \mathcal{N}( \mu _{t}^{j-1},( \sigma _{t}^{j-1} ) ^2I )采样N条轨迹，根据\pi_\theta,d_\theta采样N_\pi条轨迹，...
The model predictive path integral (MPPI) controller is a novel approach for autonomous vehicle control based on stochastic sampling of trajectories. The method is derivative free, and can handle complex non-linear dynamics and cost functions, which makes it ideal for performing aggressive maneuvers w...
In this paper, a model predictive path integral control algorithm based on a generalized importance sampling scheme is developed and parallel optimization via sampling is performed using a graphics processing unit. The proposed generalized importance sampling scheme allows for changes in the drift and di...
[4] Devasia S. Output-Sampled Model Predictive Path Integral Control (o-MPPI) for Increased ...
3. Model Predictive Path Integral Control: From Theory to Parallel Computation [J] . Grady Williams, Andrew Aldrich, Evangelos A. Theodorou Journal of guidance, control, and dynamics . 2017,第2期 机译:模型预测路径积分控制:从理论到并行计算 4. Uncertainty averse pushing with model predictive...
This paper proposes a control strategy for nonlinear constrained continuous-time uncertain systems which combines robust model predictive control (MPC) with sliding mode control (SMC). In particular, the so-called Integral SMC approach is used to produce a control action aimed to reduce the differenc...
MPPI-Generic is a C++/CUDA header-only library implementation of Model Predictive Path Integral Control (MPPI) by Williams et al. Requirements MPPI-Generic relies on the following: An NVIDIA GPU GCC/G++ CUDA 10 or newer (CUDA 11.7+ is recommended but our library is compatible back to CUDA ...
a proportional-integral (PI) control structure based on four-point temperature control loops is proposed for EDWC by considering temperature difference as the discriminant criterion. Subsequently, to improve the control performance of EDWC, two model predictive control (MPC) structures based on auto-...
Lessons from AlphaZero for Optimal, Model Predictive, and Adaptive Control 链接：http://web.mit....