Bradley Glenn - Academia.edu (original) (raw)

Papers by Bradley Glenn

Dynamic Systems and Control, 1999

Currently Hybrid Electric Vehicles (HEV) are being considered as an alternative to conventional a... more Currently Hybrid Electric Vehicles (HEV) are being considered as an alternative to conventional automobiles in order to improve efficiency and reduce emissions. To demonstrate the potential of an advanced control strategy for HEV’s, a fuzzy logic control strategy has been developed and implemented in simulation in the National Renewable Energy Laboratory’s simulator Advisor (version 2.0.2). The Fuzzy Logic Controller (FLC) utilizes the electric motor in a parallel hybrid electric vehicle (HEV) to force the ICE (66KW Volkswagen TDI) to operate at or near its peak point of efficiency or at or near its best fuel economy. Results with advisor show that the vehicle with the Fuzzy Logic Controller can achieve (56) mpg in the city, while maintaining a state of charge of .68 for the battery pack, compared to (43) mpg for a conventional vehicle. This scheme has also brought to light various rules of thumb for the design and operation of HEV’s.

Ieee Transactions on Control Systems Technology, Jul 1, 2010

Abstract In this paper, we investigate the control design problem associated with the use of an e... more Abstract In this paper, we investigate the control design problem associated with the use of an electrically assisted turbo-charger (TC) for a modern Diesel engine plant. It is shown that the proposed system has the potential of improving the control bandwidth of air charge ...

Smart Structures and Materials 2005: Modeling, Signal Processing, and Control, 2005

Millions of people worldwide suffer from diseases that lead to paralysis through disruption of si... more Millions of people worldwide suffer from diseases that lead to paralysis through disruption of signal pathways between the brain and the muscles. Neuroprosthetic devices are designed to restore lost function and could be used to form an electronic 'neural bypass' to circumvent disconnected pathways in the nervous system. It has previously been shown that intracortically recorded signals can be decoded to extract information related to motion, allowing non-human primates and paralysed humans to control computers and robotic arms through imagined movements 1–11. In non-human primates, these types of signal have also been used to drive activation of chemically paralysed arm muscles 12,13. Here we show that intracortically recorded signals can be linked in real-time to muscle activation to restore movement in a paralysed human. We used a chronically implanted intracortical microelectrode array to record multiunit activity from the motor cortex in a study participant with quadriplegia from cervical spinal cord injury. We applied machine-learning algorithms to decode the neuronal activity and control activation of the participant's forearm muscles through a custom-built high-resolution neuromuscular electrical stimulation system. The system provided isolated finger movements and the participant achieved continuous cortical control of six different wrist and hand motions. Furthermore, he was able to use the system to complete functional tasks relevant to daily living. Clinical assessment showed that, when using the system, his motor impairment improved from the fifth to the sixth cervical (C5–C6) to the seventh cervical to first thoracic (C7–T1) level unilaterally, conferring on him the critical abilities to grasp, manipulate, and release objects. This is the first demonstration to our knowledge of successful control of muscle activation using intracortically recorded signals in a paralysed human. These results have significant implications in advancing neuroprosthetic technology for people worldwide living with the effects of paralysis. The study participant was a 24-year-old male with stable, non-spastic C5/C6 quadriplegia from cervical spinal cord injury (SCI) sustained in a diving accident 4 years previously. He underwent implantation of a Utah microelectrode array (Blackrock Microsystems) in his left primary motor cortex. As shown in Fig. 1a, the hand area of the primary motor cortex was identified preopera-tively by performing functional magnetic resonance imaging (fMRI) while the participant attempted to mirror videos of hand movements. The final array implantation location was chosen during surgery, targeting the hand area while avoiding sulci and injury to large cor-tical vessels. The implant location was confirmed by co-registration of postoperative computed tomography imaging with preoperative fMRI (Fig. 1a) and is consistent with the 'knob' region of the primary motor cortex 5,14. The participant attended up to three sessions weekly for 15 months after implantation to use the neural bypass system (NBS). In each session, he was trained to utilize his motor cortical neuronal activity to control a custom-built high-resolution neuromuscular electrical stim-ulator (NMES). The NMES delivered electrical stimulation to his para-lysed right forearm muscles using an array of 130 electrodes embedded in a custom-made flexible sleeve wrapped around the arm (Fig. 1b). The participant was positioned in front of a computer monitor, and a stereo camera was placed overhead to track and record hand movements (Fig. 1c). During the study, up to 50 single units could be isolated in a given session. Near the end of the study, 33 units could be isolated with a mean signal-to-noise ratio of 3.05 ± 0.81 (mean ± s.d.) including units that responded to imagined or performed wrist movements (Fig. 1d). (See Extended Data Fig. 1 for additional unit activity.) Wavelet decomposition of the multiunit activity recorded from 96 microelec-trodes was used to produce mean wavelet power (MWP) features for decoding (Fig. 1e) (see Methods). To assess the ability of the NBS to restore individual movements, we focused on six wrist and hand movements that were all impaired by the participant's injury and reactivated by stimulation of forearm muscles (see Supplementary Video 1 showing the participant attempting the six movements without the use of the NBS). Each session began with recalibration of the NMES to map electrode stimulation patterns to evoked movements (see Methods). Cortical activity was continuously decoded as the participant attempted the six selected movements inter-leaved with rest periods, as cued by an animated virtual hand on the computer monitor. Changes in the MWP patterns for each movement were captured during the test. These patterns were then processed by multiple simultaneous neural decoders, one for each trained movement , using a nonlinear kernel method with a non-smooth support vector machine 15. The decoders were trained in successive blocks and, once trained, their outputs were continuously compared using the highest decoder output to control the corresponding NMES movement stimulation pattern (see Methods). During movement, a large portion of the stimulation artefact that occurred during a stimulation pulse was removed, but stimulation effects still remained (see Methods). To test the system's performance, test blocks were performed consisting of five trials of each of the six trained movements presented in random order. At the beginning of each trial, the participant was visually cued by the virtual hand demonstrating a target movement. Representative data, including modulation of MWP (before and after stimulation begins), decoder outputs, and corresponding movement state are shown in Fig. 2. MWP increases by a factor of 2–8 after stimulation begins because of residual stimulation artefact (see Methods and Extended Data Fig. 2). However, since the neural decoders were trained with MWP from before and during stimulation, they were able

Neuroprosthetic technology has been used to restore cortical control of discrete (non-rhythmic) h... more Neuroprosthetic technology has been used to restore cortical control of discrete (non-rhythmic) hand movements in a paralyzed person. However, cortical control of rhythmic movements which originate in the brain but are coordinated by Central Pattern Generator (CPG) neural networks in the spinal cord has not been demonstrated previously. Here we show a demonstration of an artificial neural bypass technology that decodes cortical activity and emulates spinal cord CPG function allowing volitional rhythmic hand movement. The technology uses a combination of signals recorded from the brain, machine-learning algorithms to decode the signals, a numerical model of CPG network, and a neuromuscular electrical stimulation system to evoke rhythmic movements. Using the neural bypass, a quadriplegic participant was able to initiate, sustain, and switch between rhythmic and discrete finger movements, using his thoughts alone. These results have implications in advancing neuroprosthetic technology to restore complex movements in people living with paralysis. Neuroprosthetics aim to restore or substitute for a lost function such as motion, hearing, vision, cognition, or memory in patients suffering from neurological disorders. Current neuroprosthetics systems have successfully linked intracortical signals from electrodes in the brain to external devices including a computer cursor, wheelchair and robotic arm 1–11. Recently, a neuroprosthetic bridge that bypassed the injury of the spinal cord was developed to link intracortical signals to a neuromuscular electrical stimulation (NMES) system and enable cortical control of discrete hand movements to a paralyzed person 12. However, cortical control of more complex rhythmic or oscillatory movements that normally require involvement of the spinal cord has not been demonstrated by current neuroprosthetics technologies. We hypothesized that one of the reasons for this limitation is the fact that while many rhythmic activities, such as breathing, walking/running, stirring, teeth-brushing, scratching, or playing a musical instrument, are initiated in the brain but they also require further downstream coordination in the spinal cord. Networks of neurons in the spinal cord known as Central Pattern Generators (CPGs) are responsible for producing the rhythmic activities (for a review see ref. 13). CPGs integrate input signal from the brain with sensory feedback from the limbs to produce rhythmic movements. Considerable progress has been made in understanding the behavior of biological CPGs by studying isolated spinal cords in animals (for a review see ref. 14) and using electrical stimulation of the spinal cord to further characterize the properties of the CPGs 15–17. Researchers have also developed mathematical models of CPGs 18,19 that can generate rhythmic/oscillatory output. These models have been successfully applied in simulation studies of animal and human movements 20–23 , rehabilitation of paralyzed limbs 24,25 and

Nature, Jan 13, 2016

Proceedings of the 1999 IEEE International Conference on Control Applications (Cat. No.99CH36328), 1999

... of hybrid electric vehicles, resulting in a highly scalable and reconfigurable modeling tool.... more ... of hybrid electric vehicles, resulting in a highly scalable and reconfigurable modeling tool. Furthermore, this simulation tool is used in conjunction with a fuzzy logic, ruled-based controller to optimize the energy efficiency through the control of the power flows of a parallel HEV ...

SAE Technical Paper Series, 2000

ECS Transactions, 2008

ABSTRACT Improved CO tolerance in PEM fuel cells was achieved by periodically varying the anodic ... more ABSTRACT Improved CO tolerance in PEM fuel cells was achieved by periodically varying the anodic overvoltage to convert CO to CO2 directly on the anode. The conversion was controlled by a feedback control algorithm, which used current pulsing and time- varying flow rate parameters as the control variables. Single cell performance data was obtained with 1 and 3 percent CO in a synthetic reformate mixture at 50 C using conventional catalysts and a Nafion 115 membrane. Favorable comparisons are made to 20 ppm CO in H2 for conventional operation and to representative DMFC performance. Durability remains a challenge, but preliminary data with less degradation is presented.

Journal of The Electrochemical Society, 2012

IEEE/ASME Transactions on Mechatronics, 2000

The work in this paper presents techniques for design, development, and control of hybrid electri... more The work in this paper presents techniques for design, development, and control of hybrid electric vehicles (HEV's). Toward these ends, four issues are explored. First, the development of HEV's is presented. This synopsis includes a novel definition of degree of hybridization for automotive vehicles. Second, a load-leveling vehicle operation strategy is developed. In order to accomplish the strategy, a fuzzy logic controller is proposed. Fuzzy logic control is chosen because of the need for a controller for a nonlinear, multidomain, and time-varying plant with multiple uncertainties. Third, a novel technique for system integration and component sizing is presented. Fourth, the system design and control strategy is both simulated and then implemented in an actual vehicle. The controller examined in this study increased the fuel economy of a conventional full-sized vehicle from 40 to 55.7 mi/h and increased the average efficiency over the Federal Urban Driving Schedule from 23% to 35.4%. The paper concludes with a discussion of the implications of intelligent control and mechatronic systems as they apply to automobiles.