Vehicle Active Steering Control Research Based on Two-DOF Robust Internal Model Control

Because of vehicle＇s external disturbances and model uncertainties,robust control algorithms have obtained popularity in vehicle stability control.The robust control usually gives up performance in order to guarantee the robustness of the control algorithm,therefore an improved robust internal model control（IMC） algorithm blending model tracking and internal model control is put forward for active steering system in order to reach high performance of yaw rate tracking with certain robustness.The proposed algorithm inherits the good model tracking ability of the IMC control and guarantees robustness to model uncertainties.In order to separate the design process of model tracking from the robustness design process,the improved 2 degree of freedom（DOF） robust internal model controller structure is given from the standard Youla parameterization.Simulations of double lane change maneuver and those of crosswind disturbances are conducted for evaluating the robust control algorithm,on the basis of a nonlinear vehicle simulation model with a magic tyre model.Results show that the established 2-DOF robust IMC method has better model tracking ability and a guaranteed level of robustness and robust performance,which can enhance the vehicle stability and handling,regardless of variations of the vehicle model parameters and the external crosswind interferences.Contradiction between performance and robustness of active steering control algorithm is solved and higher control performance with certain robustness to model uncertainties is obtained.

A tracking filter method of active sonar subject to unknown target maneuver

A tracking filter algorithm based on the maneuvering detection delay is presented in order to solve the fuzzy problem of target maneuver decision introduced by the measure？ment errors of active sonar. When the maneuvering detection is unclear, two target moving hypotheses, the uniform and the maneuver, derived from the method of multiple hypothesis tracking, are generated to delay the final decision time. Then the hypothesis test statistics is constructed by using the residual sequence. The active sonar？s tracking ability of unknown prior information targets is improved due to the modified sequential probability ratio test and the integration of the advantages of strong tracking filter and the Kalman filter. Simulation results show that the algorithm is able to not only track the uniform targets accurately, but also track the maneuvering targets steadily. The effectiveness of the algorithm for real underwater acoustic targets is further verified by the sea trial data processing results.

Smart Care: Energy-Efficient Long-Term Physical Activity Tracking Using Smartphones

Lack of physical activity is becoming a killer of our healthy life. As a solution for this negative impact, we propose SmartCare to help users to set up a healthy physical activity habit. SmartCare can monitor a user＇s activities over a long time, and then provide activity quality assessment and suggestion. SmartCare consists of three parts, activity recognition, energy saving, and health feedback. Activity recognition can recognize nine kinds of daily activities. A hybrid classifier that uses less power and memory with satisfactory accuracy was designed and implemented by utilizing the periodicity of target activity. In addition, a learning-based energy saver was introduced to reduce energy consumption by adjusting sampling rates and the set of features adaptively. Based on the type and duration of the activity recorded, health feedback in terms of the calorie burned was given. The system could provide quantitative activity quality assessment and recommend future physical activity plans. Through extensive real-life testing, the system is shown to achieve an average recognition accuracy of 98.0% with a minimized energy expenditure.

Wearable sensors for activity monitoring and motion control:A review

Wearable sensors for activity monitoring currently are being designed and developed,driven by an increasing demand in health care for noninvasive patient monitoring and rehabilitation training.This article reviews state-of-the-art wearable sensors for activity monitoring and motion control.Different technologies,including electromechanical,bioelectrical,and biomechanical sensors,are reviewed,along with their broad applications.Moreover,an overview of existing commercial wearable products and the computation methods for motion analysis are provided.Future research issues are identified and discussed.

Activity-based simulation using DEVS: increasing performance by an activity model in parallel DEVS simulation

Improving simulation performance using activity tracking has attracted attention in the modeling field in recent years.The reference to activity has been successfully used to predict and promote the simulation performance.Tracking activity,however,uses only the inherent performance information contained in the models.To extend activity prediction in modeling,we propose the activity enhanced modeling with an activity meta-model at the meta-level.The meta-model provides a set of interfaces to model activity in a specific domain.The activity model transformation in subsequence is devised to deal with the simulation difference due to the heterogeneous activity model.Finally,the resource-aware simulation framework is implemented to integrate the activity models in activity-based simulation.The case study shows the improvement brought on by activity-based simulation using discrete event system specification(DEVS).