We present a framework to design nonlinear robust output feedback model predictive control (MPC) schemes that ensure constraint satisfaction under noisy output measurements and disturbances. We provide novel estimation methods to bound the magnitude of the estimation error based on: stability properties of the observer; detectability; set-membership estimation; moving horizon estimation (MHE). Robust constraint satisfaction is guaranteed by suitably incorporating these online validated bounds on the estimation error in a homothetic tube based MPC formulation. In addition, we show how the performance can be further improved by combining MHE and MPC in a single optimization problem. The framework is applicable to a general class of detectable and (incrementally) stabilizable nonlinear systems. While standard output feedback MPC schemes use offline computed worst-case bounds on the estimation error, the proposed framework utilizes online validated bounds, thus reducing conservatism and improving performance. We demonstrate the reduced conservatism of the proposed framework using a nonlinear 10-state quadrotor example.

中文翻译：

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使用在线估计范围的鲁棒输出反馈模型预测控制

我们提出了一个框架来设计非线性鲁棒输出反馈模型预测控制（MPC）方案，以确保在嘈杂的输出测量和干扰下满足约束条件。我们提供了新颖的估计方法，以基于以下条件来限制估计误差的幅度：观察者的稳定性；可检测性；集合成员估计；移动视界估计（MHE）。通过在基于合成管的MPC公式中适当地将这些在线验证的边界合并到估计误差中，可以确保强大的约束满足。此外，我们展示了如何通过在单个优化问题中结合使用MHE和MPC来进一步提高性能。该框架适用于一般类别的可检测和（渐增）可稳定的非线性系统。虽然标准输出反馈MPC方案对估计误差使用离线计算的最坏情况界限，但所提出的框架利用了在线验证界限，从而减少了保守性并提高了性能。我们使用一个非线性的10态四旋翼飞机实例证明了所提出框架的保守性降低。