Nonlinear receding horizon model predictive control is a powerful approach to controlling nonlinear dynamical systems. However, typical approaches that use the Jacobian, adjoint, and forward-backward passes may lose fidelity and efficacy for highly nonlinear problems. Here, we develop an Ensemble Model Predictive Control (EMPC) approach wherein the forward model remains fully nonlinear, and an ensemble-represented Gaussian process performs the backward calculations to determine optimal gains for the initial time. EMPC admits black box, possible non-differentiable models, simulations are executable in parallel over long horizons, and control is uncertainty quantifying and applicable to stochastic settings. We construct the EMPC for terminal control and regulation problems and apply it to the control of a quadrotor in a simulated, identical-twin study. Results suggest that the easily implemented approach is promising and amenable to controlling autonomous robotic systems with added state/parameter estimation and parallel computing.

翻译：非线性后退时域模型预测控制是控制非线性动力系统的有效方法。然而，对于高度非线性问题，使用雅可比矩阵、伴随矩阵和前向-反向传递的典型方法可能降低保真度和有效性。本文提出了一种集成模型预测控制方法，其中前向模型保持完全非线性，而通过集成表示的高斯过程执行反向计算以确定初始时刻的最优增益。集成模型预测控制允许使用黑箱模型（可能不可微），可在长时间范围内并行执行仿真，且控制具有不确定性量化能力并适用于随机场景。我们针对终端控制和调节问题构建了集成模型预测控制，并将其应用于模拟的同型双机四旋翼飞行器控制。结果表明，这种易于实现的方法具有前景，且适用于通过增加状态/参数估计与并行计算来控制的自主机器人系统。