Robustness of a new nonlinear positive controller for BIS tracking

In this paper, the study of the robustness of the positive control law introduced in [1] in the presence of parameter uncertainties is made. This controller was developed to track a desired reference level for the BIS of a patient by means of the simultaneous administration of propofol and of remifentanil. Here, it is proven that in the presence of uncertainties in the BIS model, the controller still has a good performance and the BIS of the patient converges to clinically acceptable values. These results are illustrated by simulations

Force Estimation for Teleoperating Industrial Robots

As the energy on the particle accelerators or heavy ion accelerators such as CERN or GSI, fusion reactors such as JET or ITER, or other scientific experiments is increased, it is becoming increasingly necessary to use remote handling techniques in order to interact with the remote and radioactive environment

A Fuzzy-Based Brokering Service for Cloud Plan Selection

The current cloud market features a multitude of cloud services that differ from one another in terms of functionality or of security/performance guarantees. Users wishing to use a cloud service for storing, processing, or sharing their data must be able to select the service that best matches their desiderata. In this paper, we propose a novel, user centric, brokering service for supporting users in the specification of requirements and enabling their evaluation against available cloud plans, assessing how much the different plans can satisfy the user\u2019s desiderata. Our brokering service allows users to specify their desiderata in an easy and intuitive way by using natural language expressions and high-level concepts. Fuzzy logic and fuzzy inference systems are adopted to quantitatively assess the compliance of cloud services with the users\u2019 desiderata, and hence to help users in the cloud service selection process

A Generalized Algorithm for Multi-Objective Reinforcement Learning and Policy Adaptation

We introduce a new algorithm for multi-objective reinforcement learning (MORL) with linear preferences, with the goal of enabling few-shot adaptation to new tasks. In MORL, the aim is to learn policies over multiple competing objectives whose relative importance (preferences) is unknown to the agent. While this alleviates dependence on scalar reward design, the expected return of a policy can change significantly with varying preferences, making it challenging to learn a single model to produce optimal policies under different preference conditions. We propose a generalized version of the Bellman equation to learn a single parametric representation for optimal policies over the space of all possible preferences. After an initial learning phase, our agent can execute the optimal policy under any given preference, or automatically infer an underlying preference with very few samples. Experiments across four different domains demonstrate the effectiveness of our approach.Comment: Accepted in NeurIPS 201