This paper presents control and coordination algorithms for groups of vehicles. The focus is on autonomous vehicle networks performing distributed sensing tasks where each vehicle plays the role of a mobile tunable sensor. The paper proposes gradient descent algorithms for a class of utility functions which encode optimal coverage and sensing policies. The resulting closed-loop behavior is adaptive, distributed, asynchronous, and verifiably correct.

Coverage control for mobile sensing networks

In the past years, finite-set model predictive control (FS-MPC) has received a lot of attention in the power electronics field. Due to very simple inclusion of the control objectives and straightforward design, it has been adopted in a lot of different converter topologies. However, computational burden often imposes limitations in the control implementation if multistep predictions are deployed or/and if multilevel converters with many possible switching states are used. To remove these limitations, we propose to imitate the predictive controller. It is important to highlight that the imitator is not intended to improve the dynamic or steady-state performance of the original FS-MPC algorithm. In contrast, its key role is to keep approximately the same performance while at the same time reducing the computational burden. Our proposed imitator is an artificial neural network trained offline using data labeled by the original FS-MPC algorithm. Since the computational burden of the imitator is not correlated with the complexity of the FS-MPC algorithm it emulates, implementation of much more complex predictive controllers is made possible without prior limitations. The proposed method is validated experimentally on a stand-alone converter configuration and the results confirm a good match between the imitator and the predictive controller performance. Simulation models of both controllers are provided in the supplementary files for three different prediction horizons.

/pdf/supervised-imitation-learning-of-finite-set-model-predictive-2pmhzosw32.pdf

Supervised Imitation Learning of Finite-Set Model Predictive Control Systems for Power Electronics

Gilbert综合征(Gilbert syndrome,GS)又称先天性非溶血性黄疸,是以遗传性慢性轻度非结合型高胆红素血症,或同时伴有肝脏色素清除功能障碍,但无肝脏结构改变或肝脏其他功能障碍为特点的一类罕见肝病.2008年1月至2009年2月河北省承德县医院和河北医科大学第三医院收治2例GS患者,均经病理证实,现报道如下。

Gilbert综合征二例

Modeling, diagnostics, optimization, and control of internal combustion engines via modern machine learning techniques: A review and future directions

/pdf/observer-design-for-continuous-time-dynamical-systems-2uqaxtxv.pdf

Observer design for continuous-time dynamical systems

Optimizing Kalman optimal observer for state affine systems by input selection

https://hal.archives-ouvertes.fr/hal-01651631/document

Optimal Sensor Location and Mobile Sensor Crowd Modeling for Environmental Monitoring

This paper is devoted to a simple approach for the offline computation of closed-loop optimal control for dynamical systems with imposed terminal state arising in Model Predictive Control Scheme (MPC). The here-proposed approach simply relies on some integrations of the characteristic equations associated to the optimal control problem, together with the classical supervised learning of a one-hidden-layer neuron network, to get a closed-loop MPC completely computed offline. Some examples are provided in the paper, which demonstrate the ability of this approach to tackle some quite large problems, with state dimensions reaching 50, without encountering limitations due to the so-called curse of dimensionality.

/pdf/a-simple-machine-learning-technique-for-model-predictive-58l86f4e23.pdf

A Simple Machine Learning Technique for Model Predictive Control

This paper deals with the estimation of the wildfire ignition location by using a variational approach, which, to the best of my knowledge, has never been proposed before. Wildfires are here modeled by using two balance equations for energy and fuel, where the fuel loss due to combustion is defined by the fuel reaction rate. The physical coefficients of the model, together with the initial fuel distribution, are here supposed to be known. The use of a small number of low cost temperature sensors constituting a sensor network distributed on the field according to a low-discrepancy sequence is investigated which provides some promising results for this estimation problem considered to be very difficult in the litterature.

/pdf/a-variational-calculus-approach-to-wildfire-monitoring-using-3fb41e12tk.pdf

Didier Georges

Papers

Optimizing Kalman optimal observer for state affine systems by input selection

Optimal Sensor Location and Mobile Sensor Crowd Modeling for Environmental Monitoring

A Simple Machine Learning Technique for Model Predictive Control

A Variational Calculus Approach to Wildfire Monitoring Using a Low-Discrepancy Sequence-Based Deployment of Sensors

Simplified Non-Uniform Models for Various Flow Configurations in Open Channels