The Implementation in VISSIM REALTIME of an Active Electromagnetic Damper Controller for Lightweight Electric Vehicles

The use of linear electromagnetic active damper units in the suspension system of a lightweight electric vehicle offers many advantages over conventional passive, semi-active and active hydraulic dampers. While full active hydraulic systems have been commercially available in automobiles for many years, the linear electromagnetic active damper offers a lower weight system with a much reduced power demand. However an active system requires the use of a controller to adjust the power output to the damper unit. This unit must process signal inputs and provide an output solution within a short time period, often 5 milliseconds or less. By using VISSIM REALTIME, a controller was built that controlled a scale linear electromagnetic damper using Karnopp’s Skyhook algorithm. This had to deal with issues such as accelerometer drift and signal to noise ratio. These required simple but fast techniques to provide useful information to the damper in a useful timeframe. This controller-damper combination proved effective in reducing the vibration experienced by the sprung mass and was more effective than an ideal passive damper at all frequencies tested by at least a factor of three

Investigation into low power active electromagnetic damping for automotive applications

Automobile suspension systems carry out two important functions; road handling and passenger comfort. Hydraulic passive dampers are the most common system employed on vehicles, yet it is well known that passive suspension systems are less effective on lightweight vehicles. Modern damper technologies such as semi-active and active dampers, offer potential benefits when used in these vehicles. An active electromagnetic (e.m.) damper could offer these same benefits with lower power consumption and with less mechanical complexity than existing active suspension systems. This research investigates the effectiveness of e.m. passive and active damping on the performance of lightweight electric vehicles and develops a novel, fully integrated model of the e.m. damper in both passive and active modes. The proposed e.m. damper consisted of one or more cylindrical permanent magnets that travelled axially through one or more cylindrical solenoids. A magnet/solenoid damper system was modelled for both the passive and active modes. The magnets were modelled as a current carrying solenoid and from Maxwell's Laws the magnetic field was determined. For the passive damper, the magnetic field was used with Faraday's Law to determine the forces generated. In the case of the active damper the magnetic field and the current in the damper solenoid were used to calculate the magnetic force. Both a passive and active e.m. damper were modelled for a small, one degree of freedom experimental system. The active e.m. damper was modelled as a pure Skyhook damper. There was a good correlation between the modelled and experimental data for the magnet, the passive and the active Skyhook dampers. The passive damper model was scaled up as a two degree of freedom system using realistic values for a road legal lightweight electric vehicle and demonstrated that sufficient passive damping could be achieved for automotive uses, but at the price of excessive mass. For the scaled up active damper model, sufficient force could be achieved with a mass similar to a commercial hydraulic damper. The power consumption was less than 5 % of an equivalent active hydraulic suspension system. This demonstrated that the passive damper was currently impractical for lightweight electric vehicles, but the active electromagnetic damper was of sufficiently low weight and power consumption: had enough authority and offered sufficient passenger comfort benefits to include in future lightweight electric vehicle designs

Inverse kinematics solution for trajectory tracking using artificial neural networks for SCORBOT ER-4u

This paper presents the kinematic analysis of the SCORBOT-ER 4u robot arm using a Multi-Layered Feed-Forward (MLFF) Neural Network. The SCORBOT-ER 4u is a 5-DOF vertical articulated educational robot with revolute joints. The Denavit-Hartenberg and Geometrical methods are the forward kinematic algorithms used to generate data and train the neural network. The learning of forward-inverse mapping enables the inverse kinematic solution to be found. The algorithm is tested on hardware (SCORBOT-ER 4u) and reliable results are obtained. The modeling and simulations are done using MATLAB 8.0 software

NASA Tech Briefs, November 1994

Topics: Advanced Manufacturing; Electronic Components and Circuits; Electronic Systems; Physical Sciences; Materials; Computer Programs; Mechanics; Machinery/Automation; Manufacturing/Fabrication; Mathematics and Information Sciences; Life Sciences; Books and Reports

NASA Tech Briefs, October 1993

Topics include: Sensors; esign and Engineering; Electronic Components and Circuits; Electronic Systems; Physical Sciences; Materials; Computer Programs; Mechanics; Machinery; Fabrication technology; Mathematics and Information Sciences; Life Sciences; Books and Reports