论文标题
在模型参数不确定性下，贝叶斯优化可靠模型预测控制
Bayesian Optimisation for Robust Model Predictive Control under Model Parameter Uncertainty
论文作者
论文摘要
我们提出了一种自适应优化方法，用于调整随机模型预测控制（MPC）超参数，同时根据性能奖励共同估算过渡模型参数的概率分布。特别是，我们使用异性噪声模型开发出一种贝叶斯优化（BO）算法，以处理MPC超参数和动力学模型参数空间的不同噪声。由于随机控制器本质上是嘈杂的，并且噪声水平受其高参数设置的影响，因此典型的同质噪声模型对于调整MPC而言是不现实的。我们在模拟控制和机器人技术任务中评估了所提出的优化算法，我们共同推断控制和动力学参数。实验结果表明，我们的方法会导致更高的累积奖励和更稳定的控制器。
We propose an adaptive optimisation approach for tuning stochastic model predictive control (MPC) hyper-parameters while jointly estimating probability distributions of the transition model parameters based on performance rewards. In particular, we develop a Bayesian optimisation (BO) algorithm with a heteroscedastic noise model to deal with varying noise across the MPC hyper-parameter and dynamics model parameter spaces. Typical homoscedastic noise models are unrealistic for tuning MPC since stochastic controllers are inherently noisy, and the level of noise is affected by their hyper-parameter settings. We evaluate the proposed optimisation algorithm in simulated control and robotics tasks where we jointly infer control and dynamics parameters. Experimental results demonstrate that our approach leads to higher cumulative rewards and more stable controllers.
