Reinforcement Learning (RL) controllers have generated excitement within the control community. The primary advantage of RL controllers relative to existing methods is their ability to optimize uncertain systems independently of explicit assumption of process uncertainty. Recent focus on engineering applications has been directed towards the development of safe RL controllers. Previous works have proposed approaches to account for constraint satisfaction through constraint tightening from the domain of stochastic model predictive control. Here, we extend these approaches to account for plant-model mismatch. Specifically, we propose a data-driven approach that utilizes Gaussian processes for the offline simulation model and use the associated posterior uncertainty prediction to account for joint chance constraints and plant-model mismatch. The method is benchmarked against nonlinear model predictive control via case studies. The results demonstrate the ability of the methodology to account for process uncertainty, enabling satisfaction of joint chance constraints even in the presence of plant-model mismatch.

强化学习 (RL) 控制器在控制社区中引起了轰动。RL 控制器相对于现有方法的主要优势是它们能够独立于过程不确定性的显式假设来优化不确定系统。最近对工程应用的关注已转向安全 RL 控制器的开发。以前的工作提出了通过来自随机模型预测控制领域的约束收紧来解释约束满足的方法。在这里，我们扩展了这些方法以解决工厂模型不匹配的问题。具体来说，我们提出了一种数据驱动的方法，该方法将高斯过程用于离线模拟模型，并使用相关的后验不确定性预测来解释联合机会约束和工厂模型不匹配。该方法通过案例研究以非线性模型预测控制为基准。结果证明了该方法能够解决过程不确定性，即使在存在工厂模型不匹配的情况下也能满足联合机会约束。