Jyh-Ching Juang

An LMI-based nonlinear attitude control approach

This paper presents a nonlinear control law for large-angle attitude control of spacecraft. For the ROCSAT-3 spacecraft, a highly accurate and robust attitude control is desired during the orbit-raising phase. The three-axis attitude control is achieved using four body-fixed canted thrusters. In the paper, the nonlinear dynamic equations of the satellite are derived and the control requirements are stated. A novel nonlinear attitude control structure is then proposed for spacecraft control problems. The nonlinear controller contains linear feedback terms for stabilization and nonlinear terms for performance enhancement. One salient feature of the proposed approach is that the nonlinear controller parameters are designed using a linear matrix inequality (LMI) method. It turns out the controller design of stabilization and H/sub /spl infin//-type performance problems for spacecraft dynamics become rather transparent when the proposed controller structure and LMI method are employed. The design is shown to generalize many existing results. Simulation results based on the ROCSAT-3 system are then presented to demonstrate the proposed design method.

Stability analysis of Hopfield-type neural networks

The paper applies several concepts in robust control research such as linear matrix inequalities, edge theorem, parameter-dependent Lyapunov function, and Popov criteria to investigate the stability property of Hopfield-type neural networks. The existence and uniqueness of an equilibrium is formulated as a matrix determinant problem. An induction scheme is used to find the equilibrium. To verify whether the determinant is nozero for a class of matrix, a numerical range test is proposed. Several robust control techniques in particular linear matrix inequalities are used to characterize the local stability of the neural networks around the equilibrium. The global stability of the Hopfield neural networks is then addressed using a parameter-dependent Lyapunov function technique. All these results are shown to generalize existing results in verifying the existence/uniqueness of the equilibrium and local/global stability of Hopfield-type neural networks.

Development of GPS-based attitude determination algorithms

This paper describes two Global Positioning System (GPS) based attitude determination algorithms which contain steps of integer ambiguity resolution and attitude computation. The first algorithm extends the ambiguity function method to account for the unique requirement of attitude determination. The second algorithm explores the artificial neural network approach to find the attitude. A test platform is set up for verifying these algorithms.

Spacecraft robust attitude tracking design: PID control approach

The paper presents a robust PID control design method for spacecraft attitude tracking. Spacecraft control for large-angle control is subject to nonlinear couplings and uncertainties. Although nonlinear robust control design methods, such as nonlinear H/sub /spl infin// control can be applied to address these issues. The solving of the associated Hamilton-Jacobi equation is often extremely complicated and the resulting controller is not easy to implement. In the paper, the PID controller architecture is assumed for the spacecraft attitude control and concepts in nonlinear H/sub /spl infin// control theory are applied to obtain robustness properties. Results of both PD control and PID control are derived and applied to the ROCSAT-3 satellite of a science model system for verification.

Quantum-Based Algorithm for Optimizing Artificial Neural Networks

This paper presents a quantum-based algorithm for evolving artificial neural networks (ANNs). The aim is to design an ANN with few connections and high classification performance by simultaneously optimizing the network structure and the connection weights. Unlike most previous studies, the proposed algorithm uses quantum bit representation to codify the network. As a result, the connectivity bits do not indicate the actual links but the probability of the existence of the connections, thus alleviating mapping problems and reducing the risk of throwing away a potential candidate. In addition, in the proposed model, each weight space is decomposed into subspaces in terms of quantum bits. Thus, the algorithm performs a region by region exploration, and evolves gradually to find promising subspaces for further exploitation. This is helpful to provide a set of appropriate weights when evolving the network structure and to alleviate the noisy fitness evaluation problem. The proposed model is tested on four benchmark problems, namely breast cancer and iris, heart, and diabetes problems. The experimental results show that the proposed algorithm can produce compact ANN structures with good generalization ability compared to other algorithms.This paper presents a quantum-based algorithm for evolving artificial neural networks (ANNs). The aim is to design an ANN with few connections and high classification performance by simultaneously optimizing the network structure and the connection weights. Unlike most previous studies, the proposed algorithm uses quantum bit representation to codify the network. As a result, the connectivity bits do not indicate the actual links but the probability of the existence of the connections, thus alleviating mapping problems and reducing the risk of throwing away a potential candidate. In addition, in the proposed model, each weight space is decomposed into subspaces in terms of quantum bits. Thus, the algorithm performs a region by region exploration, and evolves gradually to find promising subspaces for further exploitation. This is helpful to provide a set of appropriate weights when evolving the network structure and to alleviate the noisy fitness evaluation problem. The proposed model is tested on four benchmark problems, namely breast cancer and iris, heart, and diabetes problems. The experimental results show that the proposed algorithm can produce compact ANN structures with good generalization ability compared to other algorithms.

On GPS positioning and integrity monitoring

The paper reinvestigates the measurement model associated with Global Positioning System (GPS) signal processing. It is argued that the GPS positioning model is better formulated as a linear equation with errors in both the data matrix and measurement variables. Furthermore, depending on the nature of the measurement errors, the model is categorized into unstructured and structured perturbation cases. In the former, the total least squares method is proposed for position fixing and clock bias determination. In the latter, an iteration method is developed to search for the optimal solution. In addition to the position update, both the total least squares and optimization methods provide estimates of the model mismatch which leads to a measure of GPS receiver autonomous integrity monitoring. Two new GPS fault detection metrics are then proposed and discussed. The first integrity test statistic is the norm of the residual vector in the total least squares estimate. Statistical properties of this test statistic are obtained for integrity monitoring. The second metric is a two-dimensional vector that characterizes the norm of the residual vector and mismatch matrix, both are outcomes of the total least squares method or the optimization method. The positioning and integrity monitoring schemes are verified using simulated examples.

Outlier-Detection-Based Indoor Localization System for Wireless Sensor Networks

The paper exploits the outlier detection techniques for wireless-sensor-network- (WSN-) based localization problem and proposes an outlier detection scheme to cope with noisy sensor data. The cheap and widely available measurement technique—received signal strength (RSS)—is usually taken into account in the indoor localization system, but the RSS measurements are known to be sensitive to the change of the environment. The paper develops an outlier detection scheme to deal with abnormal RSS data so as to obtain more reliable measurements for localization. The effectiveness of the proposed approach is verified experimentally in an indoor environment.

Design and Implementation of Real-Time Software Radio for Anti-Interference GPS/WAAS Sensors

Adaptive antenna array processing is widely known to provide significant anti-interference capabilities within a Global Navigation Satellite Systems (GNSS) receiver. A main challenge in the quest for such receiver architecture has always been the computational/processing requirements. Even more demanding would be to try and incorporate the flexibility of the Software-Defined Radio (SDR) design philosophy in such an implementation. This paper documents a feasible approach to a real-time SDR implementation of a beam-steered GNSS receiver and validates its performance. This research implements a real-time software receiver on a widely-available x86-based multi-core microprocessor to process four-element antenna array data streams sampled with 16-bit resolution. The software receiver is capable of 12 channels all-in-view Controlled Reception Pattern Antenna (CRPA) array processing capable of rejecting multiple interferers. Single Instruction Multiple Data (SIMD) instructions assembly coding and multithreaded programming, the key to such an implementation to reduce computational complexity, are fully documented within the paper. In conventional antenna array systems, receivers use the geometry of antennas and cable lengths known in advance. The documented CRPA implementation is architected to operate without extensive set-up and pre-calibration and leverages Space-Time Adaptive Processing (STAP) to provide adaptation in both the frequency and space domains. The validation component of the paper demonstrates that the developed software receiver operates in real time with live Global Positioning System (GPS) and Wide Area Augmentation System (WAAS) L1 C/A code signal. Further, interference rejection capabilities of the implementation are also demonstrated using multiple synthetic interferers which are added to the live data stream.

An adaptive multipath mitigation filter for GNSS applications

Global navigation satellite system (GNSS) is designed to serve both civilian and military applications. However, the GNSS performance suffers from several errors, such as ionosphere delay, troposphere delay, ephemeris error, and receiver noise and multipath. Among these errors, the multipath is one of the most unpredictable error sources in high-accuracy navigation. This paper applies a modified adaptive filter to reduce code and carrier multipath errors in GPS. The filter employs a tap-delay line with an Adaline network to estimate the direction and the delayed-signal parameters. Then, the multipath effect is mitigated by subtracting the estimated multipath effects from the processed correlation function. The hardware complexity of the method is also compared with other existing methods. Simulation results show that the proposed method using field data has a significant reduction in multipath error especially in short-delay multipath scenarios.

Phase/Frequency Tracking in a GNSS Software Receiver

In the paper, a software-based technique for the tracking of frequency and phase of Global Navigation Satellite System (GNSS) signals is proposed. It is shown that a software-based receiver with precomputed, zero-phase carrier replicas for phase tracking is subject to phase jump in phase tracking and ambiguity problems in frequency estimation. A phase/frequency tracking architecture is proposed in which the frequency of the incoming signal is estimated based on a lead-lag structure which is similar to a traditional delay locked loop and the phase, after de-rotation, is estimated using a frequency-aiding phase locked loop. The effects of thermal noise and resolution-induced noise are analyzed and verified using simulation. The significance of frequency aiding in enhancing the phase tracking response is also described.