We present a Reinforcement Learning-based Robust Nonlinear Model Predictive Control (RL-RNMPC) framework for controlling nonlinear systems in the presence of disturbances and uncertainties. An approximate Robust Nonlinear Model Predictive Control (RNMPC) of low computational complexity is used in which the state trajectory uncertainty is modelled via ellipsoids. Reinforcement Learning is then used in order to handle the ellipsoidal approximation and improve the closed-loop performance of the scheme by adjusting the MPC parameters generating the ellipsoids. The approach is tested on a simulated Wheeled Mobile Robot (WMR) tracking a desired trajectory while avoiding static obstacles.

翻译：我们提出了一个基于强化学习的强力非线性模型预测控制框架(RL-RNMPC),用于在出现扰动和不确定性的情况下控制非线性系统,并使用一个计算复杂性低的粗略非线性模型预测控制框架(RNMPC),在这种框架中,国家轨迹不确定性是通过椭球形模型模型模拟的。然后,利用强化学习处理双向近似值,并通过调整生成单球体的MPC参数来改进该计划的闭环性能。该方法在模拟的轮式移动机器人(WMR)跟踪所希望的轨迹时进行测试,同时避免静态障碍。