Signals derived from the rat motor cortex can be used for controlling one-dimensional movements of a robot arm. It remains unknown, however, whether real-time processing of cortical signals can be employed to reproduce, in a robotic device, the kind of complex arm movements used by primates to reach objects in space. Here we recorded the simultaneous activity of large populations of neurons, distributed in the premotor, primary motor and posterior parietal cortical areas, as non-human primates performed two distinct motor tasks. Accurate real-time predictions of one- and three-dimensional arm movement trajectories were obtained by applying both linear and nonlinear algorithms to cortical neuronal ensemble activity recorded from each animal. In addition, cortically derived signals were successfully used for real-time control of robotic devices, both locally and through the Internet. These results suggest that long-term control of complex prosthetic robot arm movements can be achieved by simple real-time transformations of neuronal population signals derived from multiple cortical areas in primates.

Electroactive Polymer Artificial Muscles (EPAM™) based on dielectric elastomers have the bandwidth and the energy density required to make haptic displays that are both responsive and compact. Recent work at Artificial Muscle Inc. has been directed toward the development of thin, high-fidelity haptic modules for mobile handsets. The modules provide the brief tactile "click" that confirms key press, and the steady state "bass" effects that enhance gaming and music. To design for these capabilities we developed a model of the physical system comprised of the actuator, handset, and user. Output of the physical system was passed through a transfer function to covert vibration into an estimate of the intensity of the user's haptic sensation. A model of fingertip impedance versus button press force is calibrated to data, as is impedance of the palm holding a handset. An energy-based model of actuator performance is derived and calibrated, and the actuator geometry is tuned for good haptic performance.

Current demonstrations of brain-machine interfaces (BMIs) have shown the potential for controlling neuroprostheses under pure motion control. For interaction with objects, however, pure motion control lacks the information required for versatile manipulation. This paper investigates the idea of applying impedance control in a BMI system. An extraction algorithm incorporating a musculoskeletal arm model was developed for this purpose. The new algorithm, called the muscle activation method (MAM), was tested on cortical recordings from a behaving monkey. The MAM was found to predict motion parameters with as much accuracy as a linear filter. Furthermore, it successfully predicted limb interactions with novel force fields, which is a new and significant capability lacking in other algorithms.

The aim of this article is to assess the influence of the tyre/road contact interface on driveline vibrations. The mode shapes of a vehicle driveline are obtained and analysed, initially using three tyre models: a simple torsional spring, linear slip, and relaxation length-based models. Additionally, a fully transient load-and slip-dependent non-linear relaxation length model is incorporated into the driveline to determine the dynamic response on different surfaces. Simulations of pull-away manoeuvres on various surfaces are carried out. The halfshaft torque in each case is analysed and conclusions drawn on the effect of tyre dynamics on the frequency and intensity of driveline vibrations. In order to investigate the influence of higher-frequency tyre dynamics, a model incorporating tyre belt inertia is simulated for the same cases. It is found that the higher-order dynamics introduced by the tyre belt result in additional frequencies in the response, as well as differences in response amplitude. Using the non-linear relaxation length and belt inertia models it is observed that low-µ surfaces promote driveline vibrations at higher and more numerous frequencies compared with the typical shuffle response observed on a high-µ surface. It is shown that this frequency migration can be physically explained by considering the effect of decoupling between driveline and vehicle on low-µ surfaces. It is also shown that the observed frequencies can be predicted by appropriately modified linear models.

Vehicle driveability is increasingly used as a key measure in media evaluations, and is refined aggressively to differentiate and position the product within its market segment. This is a highly complex system level issue, and encompasses the non-linear interactions between the driveline, suspension and powerunit mounting hardware. The driveability character of the vehicle has typically been tuned through calibration in the later stages of development. Through the use of physical prototypes, such activities have typically been performed on the basis of subjective assessments, to achieve a balanced compromise with other vehicle attributes such as ride, handling and refinement. This paper introduces a model-based approach to facilitate design and detailed analysis early in the product development process, thereby reducing reliance on physical prototypes and the need to implement late design changes. A detailed non-linear mathematical model has therefore been developed in order to characterise the low frequency, longitudinal behaviour of a prototype, four-wheel drive vehicle both in the time and frequency domains. In conjunction with full vehicle test measurements, the analytical model has been validated and then used to investigate a low frequency, fore-aft vehicle oscillation issue that was identified specifically during part throttle pullaway events in cold climate testing.