Learning Event-triggered Control from Data through Joint Optimization

We present a framework for model-free learning of event-triggered control strategies. Event-triggered methods aim to achieve high control performance while only closing the feedback loop when needed. This enables resource savings, e.g., network bandwidth if control commands are sent via communication networks, as in networked control systems. Event-triggered controllers consist of a communication policy, determining when to communicate, and a control policy, deciding what to communicate. It is essential to jointly optimize the two policies since individual optimization does not necessarily yield the overall optimal solution. To address this need for joint optimization, we propose a novel algorithm based on hierarchical reinforcement learning. The resulting algorithm is shown to accomplish high-performance control in line with resource savings and scales seamlessly to nonlinear and high-dimensional systems. The method's applicability to real-world scenarios is demonstrated through experiments on a six degrees of freedom real-time controlled manipulator. Further, we propose an approach towards evaluating the stability of the learned neural network policies

Pilot evaluation of the Index Based Flood Insurance in Bihar, India: Lessons of experiences

The Government of Bihar (GOB) with the help of Government of India (GOI) introduced and implemented various crop insurance programs, to provide protection against losses caused by fluctuations in the output of a crop from one year to another or from one crop season to another. Traditional agricultural insurances are designed to make compensation to client farmers affected by various disasters and natural calamities based on individual yield losses or damage to crops and livestock (Ahmed, 2013; Swain and Patnaik, 2016). For developing countries like India, with large numbers of smallholder farmers, measuring such individual losses would incur enormous costs for insurance companies. The index-based insurance offers an alternative in which individual assessment is not necessary. Advances in satellite technology and data analysis were integrated to develop index insurance products, which were piloted in different countries throughout the world such as India, Ethiopia, Senegal, and United States. The index insurance products help minimize the high transaction costs and have the potential to expand the reach of insurance policies to rural areas that were previously considered uninsurable (Swain and Patnaik, 2016; Smith and Watts, 2019). The International Water Management Institute (IWMI) has developed an Index-Based Flood Insurance (IBFI) product integrating hi-tech modeling and satellite imagery (Amarnath and Sikka, 2018; Matheswaran et al. 2019). The product was pilot tested among 200 farmers in six villages of the Gaighat Block of Muzaffarpur District, Bihar during the Khariff season, 2017. This report presents the findings of the IBFI ex-post evaluation undertaken in the pilot areas in Muzaffarpur. The findings of this study provide lessons on how index-based insurance schemes can be made more inclusive, and inform any development of a scheme for future upscaling by IWMI. The findings are based on the qualitative assessment made in April 2018 and a household survey conducted in July 2018

Self-Triggered and Event-Triggered Set-Valued Observers

This paper addresses the problem of reducing the required network load and computational power for the implementation of Set-Valued Observers (SVOs) in Networked Control System (NCS). Event- and self-triggered strategies for NCS, modeled as discrete-time Linear Parameter-Varying (LPV) systems, are studied by showing how the triggering condition can be selected. The methodology provided can be applied to determine when it is required to perform a full (``classical'') computation of the SVOs, while providing low-complexity state overbounds for the remaining time, at the expenses of temporarily reducing the estimation accuracy. As part of the procedure, an algorithm is provided to compute a suitable centrally symmetric polytope that allows to find hyper-parallelepiped and ellipsoidal overbounds to the exact set-valued state estimates calculated by the SVOs. By construction, the proposed triggering techniques do not influence the convergence of the SVOs, as at some subsequent time instants, set-valued estimates are computed using the \emph{conventional} SVOs. Results are provided for the triggering frequency of the self-triggered strategy and two interesting cases: distributed systems when the dynamics of all nodes are equal up to a reordering of the matrix; and when the probability distribution of the parameters influencing the dynamics is known. The performance of the proposed algorithm is demonstrated in simulation by using a time-sensitive example