Of all driving functions, one of the critical maneuvers is the lane merge. A cooperative Nonlinear Model Predictive Control (NMPC)-based optimization method for implementing a highway lane merge of two connected autonomous vehicles is presented using solutions obtained by the direct multiple shooting method. A performance criteria cost function, which is a function of the states and inputs of the system, was optimized subject to nonlinear model and maneuver constraints. An optimal formulation was developed and then solved on a receding horizon using direct multiple shooting solutions; this is implemented using an open-source ACADO code. Numerical simulation results were performed in a real-case scenario. The results indicate that the implementation of such a controller is possible in real time, in different highway merge situations.

Accurate power system monitoring plays a vital role in detection of supply problems within the supply grid. Of all parameters being monitored, rms based measurements are used to detect various voltage based events within the system. IEEE and IEC based standards utilize full cycle instantaneous samples repeated every half a cycle for the calculation of rms. These measurements are not without limitations especially for short duration variations. This paper proposes 4-instantaneous sample points sampled at 90 degrees apart (quadrature based rms) to estimate rms and further detect voltage events in real-time. The proposed method is experimentally tested and applied for voltage events monitoring using Labview FPGA CompactRio platform. The results demonstrate the effectiveness of the proposed method for estimating the signal rms value with less number of samples.

This study proposes a new averaged model for steady‐state and dynamic response analysis of the electric spring (ES). The accuracy of steady‐state and dynamic behaviour of the ES is demonstrated. Further, the dynamic behaviour of the proposed model is described as a function of the modulating signal. The developed model is used to design a controller for bus voltage regulation in a distribution system with an intermittent renewable energy source. The ES controller design problem is formulated as an optimization problem with a time domain‐based objective function to reduce the time‐weighted voltage deviations using particle swarm optimization method. Different disturbances have been applied to verify the developed ES model accuracy including sudden changes in renewable source current and load variations. An experimental analysis is also performed with the detailed non‐linear switching model of the ES to verify its behaviour with that of the proposed averaged model. The characteristics and performance of ES are thoroughly investigated. The effectiveness of the developed model and the proposed controller design approach is demonstrated in terms of a significant improvement in the dynamic performance of the distribution system