Abstract-In recent years, waste heat recovery (WHR) systems based on Rankine cycles have been the focus of intensive research for transport applications, as they seem to offer considerable potential for fuel consumption reduction. Because of the highly transient conditions they are subject to, control plays a fundamental role to enable viability and efficiency of those systems.The system considered here is an Organic Rankine Cycle (ORC) for recovering waste heat from a heavyduty diesel engine. For this system, a hierarchical and modular control structure has been designed, implemented and validated experimentally on an engine testbed cell.The paper focuses more particularly on improving the baseline control strategy using a model-based approach. The improvements come from an extensive system identification campaign allowing model-based tuning of PID controllers and, more particularly, from a dynamic feedforward term computed from a nonlinear reduced model of the high-pressure part of the system.Experimental results illustrate the enhanced performance in terms of disturbance rejection.

This work addresses output feedback stabilization via event triggered output feedback. In the first part of the paper, linear systems are considered, whereas the second part shows that a dynamic event triggered output feedback control law can achieve feedback stabilization of the origin for a class of nonlinear systems by employing dynamic high-gain techniques. arXiv:1605.07425v1 [math.DS]

International audienceThis paper addresses the problem of maximizing the power produced by an organic rankine cycle (ORC) waste heat recovery system on board a diesel-electric railcar. A simplified model of the system allows the formulation of an optimal control problem that can be solved via dynamic programming (DP). To increase the smoothness and the accuracy of the solution obtained offline using the implementation of DP known as level-set DP, an improved version is developed, making use of adaptive grids for discretization. The analysis of the resulting optimal trajectory for the ORC control problem provides useful insight for both control design and system design. Based on these results, the optimal control problem is reformulated allowing online implementation via dynamic real-time optimization. The proposed approach is validated on a realistic simulator, showing significant benefits in the amount of recovered energy when compared with the classical, quasi-static approach found in ORC literature