Stability Analysis of Networked Control Systems based on <Emphasis Type="Italic">L</Emphasis><Subscript>p</Subscript> Properties

To handle the communication constraint imposed by the serial communication channel in networked control systems (NCSs), we discuss a popular dynamic scheduling protocol called Maximum-Error-First (MEF) protocol. An important parameter in this protocol is the maximum allowable transmission interval (MATI), which indicates the communication cost for the task of control. To take as large MATI as possible under the constraint of guaranteeing stability is one formalization of the design requirement to consume as little communication resources as possible with the control performances ensured. A method to estimate this parameter based on the ? _p norm is suggested in this paper, which gives larger estimation than some methods do in the literature through simulation examples.     

Multiple-model Rao-Blackwellized particle probability hypothesis density filter for multitarget tracking

Multitarget tracking (MTT) is a frequent topic in visual surveillance systems. Although the multiple-model probability hypothesis density (MM-PHD) filter plays an important role in the MTT, both computerized intractability and imprecise estimate are still inevitable. To solve the problems, a novel filter is presented in this paper. Different from the previous work, the Rao-Blackwellized particle filtering algorithm is incorporated with the MM-PHD filter to reduce computational load, where the sequence Monte Carlo method is adopted to estimate the nonlinear state of targets, and the linear state is predicted using the Kalman filter with the information embedded in the estimated nonlinear state. With respect to tracking precision, we find that the reweighting scheme can be realized for the numberestimate of both undetected targets and false alarms. The result is useful in balancing the required particle number in order to stabilize target estimates during the surveillance period. The illustrative simulation is finally provided to show the effectiveness of the proposed filter.     

Move blocking strategy applied to re-entrant manufacturing line scheduling

Model predictive control (MPC)-based approach to fab-wide scheduling has been suggested to solve constraint-aware production optimization and in-process inventory level control simultaneously at each scheduling instance. However, application of this approach to real fab suffers from computational difficulties brought by the need to solve a huge optimization problem on-line as real fab scheduling problems are characterized by long cycle times, multiple product types, hundreds of machines/processing steps and re-entrant product flows. This study explores the use of an offset-blocking strategy combined with a modified recursive least square (RLS) estimation in the fab-wide scheduler, in order to alleviate the difficulty. The strategy is tested on a modified version of published case study called Intel Mini-Fab (IMF) problem. Despite its simplicity, the blocking strategy showed excellent performance in the face of realistic demand changes and plant/model mismatch.     

Algorithm of gaussian sum filter based on high-order UKF for dynamic state estimation

In this work we consider the state estimation problem in nonlinear/non-Gaussian systems. A new version of Gaussian sum estimation algorithm is developed here based on high-order unscented Kalman filter (HUKF). A sigma point selection method, high-order unscented transformation (HUT) technique is proposed for the HUKF, which can approximate the Gaussian distributions more accurately. We present the systematic formulation of Gaussian filters and develop efficient and accurate numerical integration of the optimal filter. We then go on to extend the use of the HUKF to discrete-time, nonlinear systems with additive, possibly non-Gaussian noise. The resulting filtering algorithm, called the Gaussian sum high-order unscented Kalman filter (GS-HUKF) approximates the predicted and posterior densities as a finite number of weighted sums of Gaussian densities. It is corroborated in the theoretical analysis and the simulation that the proposed Gaussian sum HUKF has integrated advantages with respect to computational accuracy and time complexity for nonlinear non-Gaussian filtering problems.     

High-speed train navigation system based on multi-sensor data fusion and map matching algorithm

Navigation system for high-speed trains is necessary for increased operational safety and efficiency, new services for customers, and low maintenance cost. This paper proposes a high accuracy navigation system for high-speed trains based on a sensor fusion algorithm, with non-holonomic constraints, for multiple sensors, such as accelerometers, gyroscopes, tachometers, Doppler radar, differential GPS, and RFID, and a map matching algorithm. In the proposed system, we consider the federated Kalman filter for sensor fusion, where local filters utilize filter models developed for various sensor types. Especially, the local Kalman filter for RFID positioning, that is detected at irregular time intervals due to the varying train speed and RFID tag spacing, is developed to maintain high performance during GPS outage. In addition, an orthogonal projection map matching algorithm is developed to improve the performance of the proposed system. The performance of the proposed system is demonstrated with numerous simulations for a high-speed train in Korea. The simulation results are analyzed with respect to the existence of tunnel, RFID deployment spacing, RFID location uncertainty, and DGPS error.     

Data Fusion-based resilient control system under DoS attacks: A game theoretic approach

In this paper, the resilient control under the Denial-of-Service (DoS) attack is rebuilt within the framework of Joint Directors of Laboratories (JDL) data fusion model. The JDL data fusion process is characterized by the so-called Game-in-Game approach, where decisions are made at different layers. The interactions between different JDL levels are considered which take the form of Packet Delivery Rate of the communication channel. Some criterions to judge whether the cyber defense system is able to protect the underlying control system is provided. Finally, a numerical example is proposed to verify the validity of the proposed method.     

Calibration of imprecise and inaccurate numerical models considering fidelity and robustness: a multi-objective optimization-based approach

Traditionally, model calibration is formulated as a single objective problem, where fidelity to measurements is maximized by adjusting model parameters. In such a formulation however, the model with best fidelity merely represents an optimum compromise between various forms of errors and uncertainties and thus, multiple calibrated models can be found to demonstrate comparable fidelity producing non-unique solutions. To alleviate this problem, the authors formulate model calibration as a multi-objective problem with two distinct objectives: fidelity and robustness. Herein, robustness is defined as the maximum allowable uncertainty in calibrating model parameters with which the model continues to yield acceptable agreement with measurements. The proposed approach is demonstrated through the calibration of a finite element model of a steel moment resisting frame.     

Analysis of two authorization protocols using Colored Petri Nets

To prevent unauthorized access to protected trusted platform module (TPM) objects, authorization protocols, such as the object-specific authorization protocol (OSAP), have been introduced by the trusted computing group (TCG). By using OSAP, processes trying to gain access to the protected TPM objects need to prove their knowledge of relevant authorization data before access to the objects can be granted. Chen and Ryan’s 2009 analysis has demonstrated OSAP’s authentication vulnerability in sessions with shared authorization data. They also proposed the Session Key Authorization Protocol (SKAP) with fewer stages as an alternative to OSAP. Chen and Ryan’s analysis of SKAP using ProVerif proves the authentication property. The purpose of this paper was to examine the usefulness of Colored Petri Nets (CPN) and CPN Tools for security analysis. Using OSAP and SKAP as case studies, we construct intruder and authentication property models in CPN. CPN Tools is used to verify the authentication property using a Dolev–Yao-based model. Verification of the authentication property in both models using the state space tool produces results consistent with those of Chen and Ryan.     

Generating a Visual Language of Performance-Driven Configurations for the Principal Façade of a Prototype Sustainable House

A performance-driven application of shape grammars is presented. A parametric shape grammar that generates a language of pattern designs for the principal façade of a prototype house—featuring a 5 × 20 matrix of electrochromic windows—based equally on performance and aesthetic criteria is described. The adjustment of the chromatism and light transmittance of each individual windowpane on the façade enables the adjustment of solar radiation at the house interior. The novel aspect of the grammar is that it encodes performance constraints of interior daylight illuminance and associates them to visual, symmetry principles of two-dimensional pattern generation. Twelve parametric rules account for the generation of the façade pattern language and five subclasses account for the symmetry of the patterns in the language.     

Coordinated design of TCSC and PSS controllers using VURPSO and Genetic algorithms for multi-machine power system stability

Thyristor controlled series capacitor (TCSC) can regulate line impedance and therefore increase transferred power of the system. On the other hand power system stabilizer (PSS) increases dynamic stability of generator. To enhance the stability, combination of TCSC and PSS can be applied, and in such a case coordination of TCSC and PSS is essential. This paper applies this combined controller in order to enhance the stability of multi-machine system. Parameters of these controllers are optimized by velocity update relaxation particle swarm optimization (VURPSO) algorithm and Genetic algorithm (GA). The simulation results show that the combination of VURPSO algorithm and GA leads to a better design and stability.