Andrew B. Wright

Planting the seeds for a Mechatronic curriculum at UALR

In February 1999, the University of Arkansas at Little Rock (UALR) launched a program in systems engineering. The program recognises the need for highly multidisciplinary, design-oriented education. This created a natural context into which a mechatronics discipline can be introduced. A course sequence in mechatronics is being developed to supplement the systems engineering curriculum and consists of the sequence Introduction to Engineering, C Programming, Elements of Mechanical Design, Circuits and Systems, Digital Systems, Control Theory, Instrumentation and Measurements, and Mechatronics. Facilities have been developed to allow students to design and analyse their parts in CAD, export them to a CAM program, which generates NC code for the CNC milling machine or lathe. This allows students to rapidly generate parts, and therefore create synergistic, integrated mechanical/electrical designs within a semester.

Instrumentation of UALR labscale hybrid rocket motor

The Central Arkansas Combustion Group has used a NASA EPSCoR grant to improve the instrumentation and control of its labscale hybrid rocket facility. The research group investigates fundamental aspects of combustion in hybrid rocket motors. This paper describes the new instrumentation, provides examples of measurements taken, and describes novel instrumentation which is in the process of development. A six degree-of-freedom thrust system measures the total work done during a burn to compare the efficiency of fuels and fuel additives. The new system measures the forces and moments in three spatial dimensions. An accurate measure of thrust oscillations will lead to better understanding of the cause and eventual minimization of the oscillations. Plume spectrometers are employed to determine and measure the reaction intermediates and products of combustion at the exhaust. The new control system features an oxygen mass flow controller, which allows the accurate measurement of the oxidant introduced into the motor.

A Method for Locomotion Mode Identification Using Muscle Synergies

Active lower limb transfemoral prostheses have enabled amputees to perform different locomotion modes such as walking, stair ascent, stair descent, ramp ascent and ramp descent. To achieve seamless mode transitions, these devices either rely on neural information from the amputee’s residual limbs or sensors attached to the prosthesis to identify the intended locomotion modes or both. We present an approach for classification of locomotion modes based on the framework of muscle synergies underlying electromyography signals. Neural information at the critical instances (e.g., heel contact and toe-off) was decoded for this purpose. Non-negative matrix factorization was used to extract the muscles synergies from the muscle feature matrix. The estimation of the neural command was done using non-negative least squares. The muscle synergy approach was compared with linear discriminant analysis (LDA), support vector machine (SVM), and neural network (NN) and was tested on seven able-bodied subjects. There was no significant difference (

Task discrimination for non-weight-bearing movements using muscle synergies.

p > 0.05

A six degree-of-freedom thrust sensor for a labscale hybrid rocket

) in transitional and steady state classification errors during stance phase. The muscle synergy approach performed significantly better (

Layered mode selection logic control for border security

p < 0.01

UV SPECTROSCOPIC MONITORING OF ROCKET MOTOR COMBUSTION EFFICIENCY

) than NN and LDA during swing phase while results were similar to SVM. These results suggest that the muscle synergy approach can be used to discriminate between locomotion modes involving transitions.

A comparison of white test signals used in active sound cancellation

Myoelectric control of lower limb prostheses requires discrimination of task-specific muscle patterns. In this paper we present a method based on the notion of muscle synergies to discriminate between various non-weight-bearing movements such as knee extension/flexion, femur rotation in/out, tibia rotation in/out and ankle dorsiflexion/plantarflexion. Data is recorded from eight targeted muscle sites on the thigh. Non-negative matrix factorization is used to identify the muscle synergies using multiple features and estimation of electromyographic (EMG) patterns is done using non-negative least squares (NNLS). Classification accuracy for the movements involving the knee joint was higher than the movements involving the ankle joint. The proposed algorithm performs at par with the common machine learning algorithm Linear Discriminant Analysis (LDA) in offline analysis.

Use of schroeder-phased waveform to investigate convergence and tracking of the LMS algorithm in active sound control

A six degree-of-freedom thrust sensor was designed, constructed, calibrated, and tested using the labscale hybrid rocket at the University of Arkansas at Little Rock. The system consisted of six independent legs: one parallel to the axis of symmetry of the rocket for main thrust measurement, two vertical legs near the nozzle end of the rocket, one vertical leg near the oxygen input end of the rocket, and two separated horizontal legs near the nozzle end. Each leg was composed of a rotational bearing, a load cell, and a universal joint above and below the load cell. The leg was designed to create point contact along only one direction and minimize the non-axial forces applied to the load cell. With this system, force in each direction and moments for roll, pitch, and yaw can be measured. The system was calibrated and tested using a labscale hybrid rocket using gaseous oxygen and hydroxyl-terminated polybutadiene solid fuel. The thrust stand proved to be stable during calibration tests. Thrust force vector components and roll, pitch, and yaw moments were calculated for test firings with an oxygen mass flow rate range of 0.0174?0.0348?kg s?1.

Locomotion mode identification for lower limbs using neuromuscular and joint kinematic signals

Challenges in border security may be resolved through a team of autonomous mobile robots configured as a flexible sensor array. The robots will have a prearranged formation along a section of a border, and each robot will attempt to maintain a uniform distance with its nearest neighbors. The robots will carry sensor packages which can detect a signature that is representative of a human (for instance, a thermal signature). When a robot detects an intruder, it will move away such that it attempts to maintain a constant distance from the intruder and move away from the border (i.e. into its home territory). As the robot moves away from the border, its neighbors will move away from the border to maintain a uniform distance with the moving robot and with their fixed neighbors. The pattern of motion in the team of robots can be identified, either algorithmically by a computer or by a human monitor of a display. Unique patterns are indicative of animal movement, human movement, and mass human movement. To realize such a scheme, a new control architecture must be developed. This architecture must be fault tolerant to sensor and manipulator failures, scalable in number of agents, and adaptable to different robotic base platforms (for instance, a UGV may be appropriate at the southern border and a UAV may be appropriate at the northern border). The Central Arkansas Robotics Consortium has developed an architecture, called Layered Mode Selection Logic (LMSL), which addresses all of these concerns. The overall LMSL scheme as applied to a multi-agent flexible sensor array is described in this paper.