Designing predictive controllers towards optimal closed-loop performance while maintaining safety and stability is challenging. This work explores closed-loop learning for predictive control parameters under imperfect information while considering closed-loop stability. We employ constrained Bayesian optimization to learn a model predictive controller's (MPC) cost function parametrized as a feedforward neural network, optimizing closed-loop behavior as well as minimizing model-plant mismatch. Doing so offers a high degree of freedom and, thus, the opportunity for efficient and global optimization towards the desired and optimal closed-loop behavior. We extend this framework by stability constraints on the learned controller parameters, exploiting the optimal value function of the underlying MPC as a Lyapunov candidate. The effectiveness of the proposed approach is underlined in simulations, highlighting its performance and safety capabilities.

翻译：在保持安全性和稳定性的同时设计具有最优闭环性能的预测控制器颇具挑战性。本文研究在信息不完备条件下考虑闭环稳定性的预测控制参数闭环学习方法。我们采用带约束的贝叶斯优化来学习模型预测控制器（MPC）的代价函数（该函数参数化为前馈神经网络），同时优化闭环行为并最小化模型与真实系统间的失配。这种方法提供了高度自由度，从而为高效全局优化以实现期望的最优闭环行为创造了机会。我们通过将稳定性约束施加于学习得到的控制器参数来扩展该框架，利用底层MPC的最优值函数作为李雅普诺夫候选函数。仿真结果验证了所提方法的有效性，突显了其性能与安全保障能力。