Real-Time Implementation of Intelligent Actuator Control with a Transducer Health Monitoring Capability

This paper presents a concept of feedback control for smart actuators that are compatible with smart sensors, communication protocols, and a hierarchical Integrated System Health Management (ISHM) architecture developed by NASA s Stennis Space Center. Smart sensors and actuators typically provide functionalities such as automatic configuration, system condition awareness and self-diagnosis. Spacecraft and rocket test facilities are in the early stages of adopting these concepts. The paper presents a concept combining the IEEE 1451-based ISHM architecture with a transducer health monitoring capability to enhance the control process. A control system testbed for intelligent actuator control, with on-board ISHM capabilities, has been developed and implemented. Overviews of the IEEE 1451 standard, the smart actuator architecture, and control based on this architecture are presented

Formation Control of Nonlinear Multi-Agent Systems Using Three-Layer Neural Networks

This paper considers a leader-following formation control problem for heterogeneous, second-order, uncertain, input-affine, nonlinear multi-agent systems modeled by a directed graph. A tunable, three-layer neural network (NN) is proposed with an input layer, two hidden layers, and an output layer to approximate an unknown nonlinearity. Unlike commonly used trial and error efforts to select the number of neurons in a conventional NN, in this case an \textit{a priori} knowledge allows one to set up the number of neurons in each layer. The NN weights tuning laws are derived using the Lyapunov theory. The leader-following and formation control problems are addressed by a robust integral of the sign of the error (RISE) feedback and a NN-based control. The RISE feedback term compensates for unknown leader dynamics and the unknown, bounded disturbance in the agent error dynamics. The NN-based term compensates for the unknown nonlinearity in the dynamics of multi-agent systems, and semi-global asymptotic tracking results are rigorously proven using the Lyapunov stability theory. The results of the paper are compared with two previous results to evaluate the efficiency and performance of the proposed method.Comment: 12 pages, 11 figures, submitted to IEEE Transactions on Neural Networks and Learning System

Wireless Sensor Networks Fault Detection and Identification

We have developed and experimentally tested a set of models for the detection and identification of sensor faults that commonly occur in wireless sensor networks. Considered faults include outlier, spike, variance, high-frequency noise, offset, gain, and drift faults. These faults affect the system operations and can endanger operators, final users, and the general public. The fault detection models are divided into two classes: data-centric models, which only analyze a single data stream, and system-centric models, which consider the overall system. For data-centric models, we use the magnitude, the gradient, and the variance of raw sensor data to model faults. For system-centric models, we introduce variogram-based techniques that allow faults to be detected by comparing readings from multiple sensors that measure related phenomena. For data-centric and system-centric sensor fault detection, we show how a few model parameters affect the sensitivity of wireless sensor network fault models. We present simulation and experimental results that illustrate the fault detection and identification models. The system is intended for health monitoring applications of the NASA Stennis Space Center (SSC) test stands and widely distributed support systems, including pressurized gas lines, propellant delivery systems, and water coolant lines. The testbed consists of Coremicro® reconfigurable embedded smart sensor nodes [29] capable of wireless communication, a network-capable application processor, a wireless base station, the software that supports sensor and actuator health monitoring, a database server, and a smartphone running a health monitoring Android application

Neural network control of a rehabilitation robot by state and output feedback

In this paper, neural network control is presented for a rehabilitation robot with unknown system dynamics. To deal with the system uncertainties and improve the system robustness, adaptive neural networks are used to approximate the unknown model of the robot and adapt interactions between the robot and the patient. Both full state feedback control and output feedback control are considered in this paper. With the proposed control, uniform ultimate boundedness of the closed loop system is achieved in the context of Lyapunov’s stability theory and its associated techniques. The state of the system is proven to converge to a small neighborhood of zero by appropriately choosing design parameters. Extensive simulations for a rehabilitation robot with constraints are carried out to illustrate the effectiveness of the proposed control

Total prostatectomy as a treatment for prostatic carcinoma in 25 dogs

Objective: To describe the complications and outcome after total prostatectomy in dogs with histologically confirmed prostatic carcinoma. Study Design: Multi-institutional retrospective case series. Animals: 25 client-owned dogs. Methods: Medical records of dogs undergoing total prostatectomy were reviewed from 2004 to 2016. Data retrieved included signalment, presenting signs, preoperative clinical findings, laboratory data, diagnostic imaging, surgical technique, histologic diagnosis, postoperative complications, occurrence of postoperative metastasis, and survival. Results: Twenty-five dogs underwent total prostatectomy for prostatic carcinoma. Urinary anastomotic techniques included urethrourethral anastomosis in 14 dogs, cystourethral anastomosis in 9 dogs, ureterocolonic anastomosis in 1 dog, and anastomosis between the bladder neck and penile urethra in 1 dog. All dogs survived to discharge. Fifteen dogs were diagnosed with transitional cell carcinoma, 8 dogs with prostatic adenocarcinoma, 1 with prostatic cystadenocarcinoma, and 1 with an undifferentiated carcinoma. Permanent postoperative urinary incontinence was present in 8 of 23 dogs. The median survival time was shorter in dogs with extracapsular tumor extension compared with those with intracapsular tumors. The overall median survival time was 231 days (range, 24-1255), with 1- and 2-year survival rates equal to 32% and 12%, respectively. Conclusion and Clinical Significance: Total prostatectomy, combined with adjunct therapies, prolongs survival and lowers complication rates compared to previous reports of dogs with prostatic carcinoma. It should be noted, however, that case selection likely played a significant role in postoperative outcome

Comparative review of human and canine osteosarcoma: morphology, epidemiology, prognosis, treatment and genetics

Osteosarcoma (OSA) is a rare cancer in people. However OSA incidence rates in dogs are 27 times higher than in people. Prognosis in both species is poor, with five year osteosarcoma survival rates in people not having improved in decades. For dogs, one year survival rates are only around ~45%. Improved and novel treatment regimens are urgently required to improve survival in both humans and dogs with OSA. Utilising information from genetic studies could assist in this in both species, with the higher incidence rates in dogs contributing to the dog population being a good model of human disease. This review compares the clinical characteristics, gross morphology and histopathology, aetiology, epidemiology, and genetics of canine and human osteosarcoma. Finally, the current position of canine osteosarcoma genetic research is discussed and areas for additional work within the canine population are identified