With increase in the frequency of natural disasters such as hurricanes that disrupt the supply from the grid, there is a greater need for resiliency in electric supply. Rooftop solar photovoltaic (PV) panels along with batteries can provide resiliency to a house in a blackout due to a natural disaster. Our previous work showed that intelligence can reduce the size of a PV+battery system for the same level of post-blackout service compared to a conventional system that does not employ intelligent control. The intelligent controller proposed is based on model predictive control (MPC), which has two main challenges. One, it requires simple yet accurate models as it involves real-time optimization. Two, the discrete actuation for residential loads (on/off) makes the underlying optimization problem a mixed-integer program (MIP) which is challenging to solve. An attractive alternative to MPC is reinforcement learning (RL) as the real-time control computation is both model-free and simple. These points of interest accompany certain trade-offs; RL requires computationally expensive offline learning, and its performance is sensitive to various design choices. In this work, we propose an RL-based controller. We compare its performance with the MPC controller proposed in our prior work and a non-intelligent baseline controller. Themore »
A Continuous Optimization Approach to Drift Counteraction Optimal Control
Drift counteraction optimal control (DCOC) aims at optimizing control to maximize the time (or a yield) until the system trajectory exits a prescribed set, which may represent safety constraints, operating limits, and/or efﬁciency requirements. To DCOC problems formulated in discrete time, conventional approaches were based on dynamic programming (DP) or mixed-integer programming (MIP), which could become computationally prohibitive for higher-order systems. In this paper, we propose a novel approach to discrete-time DCOC problems based on a nonlinear programming formulation with purely continuous variables. We show that this new continuous optimization-based approach leads to the same exit time as the conventional MIP-based approach, while being computationally more efﬁcient than the latter. This is also illustrated by numerical examples representing the drift counteraction control for an indoor airship.
- Award ID(s):
- Publication Date:
- NSF-PAR ID:
- Journal Name:
- Proceedings of 2021 American Control Conference
- Page Range or eLocation-ID:
- 3824 to 3829
- Sponsoring Org:
- National Science Foundation
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