/PRNewswire/ NSITEXE, Inc., a wholly owned subsidiary of Denso Corporation that develops and sells high efficiency processor IP for embedded systems, and.
A tyre blow-out can greatly affect vehicle stability and cause serious accidents. In the literature, however, studies on comprehensive three-dimensional vehicle dynamics modelling and stability control strategies in the event of a sudden tyre blow-out are seriously lacking. In this study, a comprehensive 14 degrees-of-freedom (DOF) vehicle dynamics model is first proposed to describe the vehicle yaw-plane and roll-plane dynamics performance after a tyre blow-out. Then, based on the proposed 14 DOF dynamics model, an integrated control framework for a combined yaw plane and roll-plane stability control is presented. This integrated control framework consists of a vehicle state predictor, an upper-level control mode supervisor and a lower-level 14 DOF model predictive controller (MPC). The state predictor is designed to predict the vehicle’s future states, and the upper-level control mode supervisor can use these future states to determine a suitable control mode. After that, based on
Devices in Internet of Things (IoT) networks are required to execute tasks such as sensing, computation and communication. These devices, however, have energy limitation, which in turn bounds the number of tasks they can execute and their tasks execution time. To this end, this paper considers energy delivery, tasks assignment and execution in a Radio Frequency (RF) IoT network with a Hybrid Access Point (HAP) and RF-powered devices. We outline a novel Mixed-Integer Linear Program (MILP) to assign tasks to devices, and also to optimize the HAP’s charging duration. We also propose a heuristic algorithm called Energy Saving Task Assignment (ESTA), and two Model Predictive Control (MPC) approaches called MPC-MILP and MPC-ESTA; both of which use channel estimates over a given window or time horizon. Our results show that MPC-MILP and MPC-ESTA respectively consume up to 74.27% and 63.71% less energy as compared to competing approaches. Moreover, MPC-MILP with a small window has better per
Automotive steer-by-wire (SbW) system is a safety-critical system where the safety and reliability issues must be addressed during the operation. This paper proposes a fault detection and isolation-based actuator fault tolerant control for SbW system based on model predictive control. The fault detection and isolation module uses a two-stage Kalman filtering algorithm to provide simultaneous control parameter and state estimation to detect the occurrence of actuator failure. Additionally, a model predictive controller is designed to maintain the original functionality of the SbW systems using the output of fault detection and isolation to update the fault information. We evaluate the performance of the proposed approach by numerical simulations and experiments on our SbW platform. The simulation and experimental results illustrate that our approach achieves a better steering performance than a conventional model predictive controller without fault detection and isolation and stabilise
Findings on Machine Learning Discussed by Investigators at RWTH Aachen University (Application of Data-driven Methods for Energy System Modelling Demonstrated On an Adaptive Cooling Supply System) energycentral.com - get the latest breaking news, showbiz & celebrity photos, sport news & rumours, viral videos and top stories from energycentral.com Daily Mail and Mail on Sunday newspapers.