No abstract

No abstract

Determining the optimal cost function parameters of Model Predictive Control (MPC) to optimize multiple control objectives is a challenging and time-consuming task. Multiobjective Bayesian Optimization (BO) techniques solve this problem by determining a Pareto optimal parameter set for an MPC with static weights. However, a single parameter set may not deliver the most optimal closed-loop control performance when the context of the MPC operating conditions changes during its operation, urging the need to adapt the cost function weights at runtime. Deep Reinforcement Learning (RL) algorithms can automatically learn context-dependent optimal parameter sets and dynamically adapt for a Weightsvarying MPC (WMPC). However, learning cost function weights from scratch in a continuous action space may lead to unsafe operating states. To solve this, we propose a novel approach limiting the RL actions within a safe learning space representing a catalog of pre-optimized BO Pareto-optimal weight sets. We conceive a RL agent not to learn in a continuous space but to proactively anticipate upcoming control tasks and to choose the most optimal discrete actions, each corresponding to a single set of Pareto optimal weights, context-dependent. Hence, even an untrained RL agent guarantees a safe and optimal performance. Experimental results demonstrate that an untrained RL-WMPC shows Pareto-optimal closed-loop behavior and training the RL-WMPC helps exhibit a performance beyond the Pareto-front.

No abstract

Die Regelung von Radialverdichtern ist ein anspruchsvolles Problem, das entscheidend durch die Nichtlinearität der Regelstrecke, häufige Arbeitspunktwechsel und zu berücksichtigende Begrenzungen geprägt wird. Der Beitrag stellt ein modellbasiertes Regelungskonzept vor, bei dem die üblicherweise eingesetzte, konventionelle Verdichterregelung um eine nichtlineare Vorsteuerung erweitert wird. Hierdurch lässt sich das Führungs- und Störverhalten der Verdichterregelung verbessern. Die Erprobung an einem Versuchsstand demonstriert die Leistungsfähigkeit des Konzepts.