Existing reinforcement learning (RL) methods have limited applicability to real-world industrial control problems because of their various constraints. To overcome this challenge, in this article, we devise a novel RL method to enable the optimization of a policy while strictly satisfying the system constraints. By leveraging a value-based RL approach, our proposed method is not limited by the challenges faced when searching a constrained policy. Our method has two main features. First, we devise two distance-based Q-value update schemes, incentive and penalty updates, which enable the agent to decide on controls in the feasible region by replacing an infeasible control with the nearest feasible continuous control. The proposed update schemes can adjust the values of both continuous and original infeasible controls. Second, we define the penalty cost as a shadow price-weighted penalty to achieve efficient, constrained policy learning. We apply our method to the microgrid control, and the case study demonstrates its superiority.

The Connected Vehicle enabled Freeway Merge Assistance system is developed by the University of Virginia Center for Transportation Studies, with the aim of reducing conflicts between merging vehicles in freeway ramp area. Initial simulation evaluation results showed that the merge assistance system has significant potential to increase capacity of freeway merge areas and reduce accidents by minimizing the number of conflicts between vehicles. As a next step of evaluation, a field test is conducted at a Connected Vehicle test bed to investigate drivers' response to the personalized advisories relayed by this system. This paper provides an overview of the field test methodology, system architecture, stated preference survey and presents preliminary results for this prototype freeway merge assistance system developed for the Connected Vehicle Environment. The revealed and stated preference data gathered will be used to develop an advisory response model that will incorporate drivers' response variability in the simulation evaluation framework of the freeway merge assistance system.

We propose novel methods to generate a sequence of shapes that represents the pattern of morphological development or transformation of a Bézier curve. The methods utilize the intrinsic geometric structures of a Bézier curve that are derived from rib and fan decomposition (RFD) [Lee and Park 2005].