Showing papers by "M Maarten Steinbuch published in 2018"
TL;DR: The structure of the feedforward controller follows from modeling, but to achieve robust performance the tuning of the controller parameters will be based on measurement data, and the controller poles and zeros are obtained from online self-tuning using measured sampled-data systems.
19 citations
TL;DR: This paper introduces a three-dimensional volumetric representation for safe navigation based on the OctoMap representation framework that probabilistically fuses sensor measurements to represent the occupancy probability of volumes and shows that by relating this probability to a safe velocity limit a robot in a real domestic environment can move close to a certain maximum velocity but decides to attain a slower safe velocitylimit when it must.
Abstract: This paper introduces a three-dimensional volumetric representation for safe navigation. It is based on the OctoMap representation framework that probabilistically fuses sensor measurements to represent the occupancy probability of volumes. To achieve safe navigation in a domestic environment this representation is extended with a model of the occupancy probability if no sensor measurements are received, and a proactive approach to deal with unpredictably moving obstacles that can arise from behind occlusions by always expecting obstacles to appear on the robot’s path. By combining the occupancy probability of volumes with the position uncertainty of the robot, a probability of collision is obtained. It is shown that by relating this probability to a safe velocity limit a robot in a real domestic environment can move close to a certain maximum velocity but decides to attain a slower safe velocity limit when it must, analogous to slowing down in traffic when approaching an occluded intersection.
17 citations
18 Oct 2018
TL;DR: A stochastic model based driving cycle synthesis is introduced that can generate the driving cycle with the desired length to compress the original driving cycle and can be tested for the fuel economic in the powertrain simulation.
Abstract: As an important input for the simulation and design process of powertrains, a driving cycle needs to be representative of real-world driving behavior. For the purpose of reducing the time consumption in the simulation, a novel modeling method is required to get a representative short driving cycle from the driving datasets. In this paper, a stochastic model based driving cycle synthesis is introduced. The Markov Chain process is combined with a transition probability extracted from the input driving data to determine the next possible state of the vehicle. Specifically, the velocity and slope are generated simultaneously using a three-dimensional Markov Chain model. After the generation process, the result is validated by selected criteria. Furthermore, this synthesis can generate the driving cycle with the desired length to compress the original driving cycle. The results show that the successful compression of the driving cycle can be tested for the fuel economic in the powertrain simulation. At last, the standard deviation of acceleration is found that has a positive correlation of the compression capability of the driving cycle.
11 citations
01 Jan 2018
TL;DR: The regenerative braking capability of the motor is affected by the SoC of battery and motors torque limitation in real time, and the dynamical variation of the regenerative brake efficiency is considered in this study to obtain optimal gear ratios of a two-speed transmission system.
Abstract: In this paper, gear ratios of a two-speed transmission system are optimized for an electric passenger car. Quasi static system models, including the vehicle model, the motor, the battery, the transmission system, and drive cycles are established in MATLAB/Simulink at first. Specifically, since the regenerative braking capability of the motor is affected by the SoC of battery and motors torque limitation in real time, the dynamical variation of the regenerative brake efficiency is considered in this study. To obtain the optimal gear ratios, iterations are carried out through Nelder-Mead algorithm under constraints in MATLAB/Simulink. During the optimization process, the motor efficiency is observed along with the drive cycle, and the gear shift strategy is determined based on the vehicle velocity and acceleration demand. Simulation results show that the electric motor works in a relative high efficiency range during the whole drive cycle. The energy economy of the case-study vehicle with the optimized a two-speed transmission is also compared to that of the baseline vehicle with fixed-ratio reduction gear.
5 citations
09 Aug 2018
TL;DR: This work proposes a Linear Time-Varying (LTV) feedforward control scheme, which is based on the feasible and stable inversion of a minimum-phase fourth-order LTV approximation of the plant, which takes into account resonant dynamics and provides improved phase tracking of the Linear Parameter-VARYing (LPV) system.
Abstract: In the control synthesis of distributed parameter flexible systems taking into account flexible dynamics plays an increasingly important role. This work proposes a Linear Time-Varying (LTV) feedforward control scheme, which is based on the feasible and stable inversion of a minimum-phase fourth-order LTV approximation of the plant. This approximation takes into account resonant dynamics and (as a result) provides improved phase tracking of the Linear Parameter-Varying (LPV) system. The results are validated through measurement results obtained from a rotational two-mass-spring-damper system with time-varying output.
2 citations