Bahram Shafai - Academia.edu (original) (raw)
Papers by Bahram Shafai
2018 World Automation Congress (WAC), 2018
Brain computer interfaces (BCIs) have attracted great attention for human computer interaction. I... more Brain computer interfaces (BCIs) have attracted great attention for human computer interaction. In BCIs that are based on electroencephalography (EEG), low signal-to-noise ratio causes user intent inference to be error-prone and uncertain. Thus, the control of such systems becomes a challenging task. This paper presents an adaptive proportional BCI controller for linear dynamical systems. An approach to optimize the closed-loop control performance of dynamic systems driven by probabilistic desired/reference inputs is illustrated by designing an adaptive proportional controller that exerts forces on a mass moving linearly in one dimensional space, where the user intent is inferred by an EEG-based BCI. The BCI provides a continuous-valued output by fusing all available EEG evidence; the adaptive proportional controller optimally selects a time-varying gain that considers prior/context knowledge of user intent/environment and the probabilistic error characteristics of the BCI in inferring user intent. Such an adaptive controller that considers human-intent-inference error statistics in human-in-the-loop control systems, especially relevant for BCI-based user intent inference where accuracies may be low, improves overall closed-loop system performance measured by mean-squared tracking error. The performance of the proposed BCI-controller has been evaluated by computer simulations using code based visual evoked potentials (c-VEPs). The employed datasets were collected from healthy individuals between 20 and 30 years old with normal or corrected normal vision during calibration sessions for BCI. Results indicate that this adaptive control scheme is particularly beneficial for users with poor BCI performance.
2019 IEEE Conference on Control Technology and Applications (CCTA), 2019
This paper considers the problem of robustly identifying m intruders in a network consisting of n... more This paper considers the problem of robustly identifying m intruders in a network consisting of n cooperative agents which are subject to unknown disturbances. First, a distributed system model is introduced so that the relationship between agents, the attacks and unknown disturbances can be captured. Next, the distributed identification scheme is formulated as a spectral assignment problem and necessary filter gains are obtained through a carefully constructed linear system of equations. Necessary and sufficient conditions to decouple the unknown disturbances from the agents residual generators are derived in terms of filter gain matrices. It is shown that the problem of discriminating between unknown disturbances and attacks in a distributed system under consensus dynamics can be reduced to the problem of determining a set of constraints on the spectrum of the residual generator coefficient matrices. The approach is illustrated through an example.
2016 World Automation Congress (WAC), 2016
This paper considers the problem of positive stabilization of linear continuous-time singular sys... more This paper considers the problem of positive stabilization of linear continuous-time singular systems by state feedback. First, necessary and sufficient conditions for a singular system to be admissible (stable, regular, and impulse-free) are given in terms of LMI. Based on this result, the state feedback admissibility problem is solved without positivity constraints. Then, it is shown that the LMI based stabilization results of singular systems can conveniently be combined with positivity constraints to establish positive stabilization of general singular systems. We also show that the well-known positive stabilization technique based on LMI for conventional linear systems can be applied to singular systems represented by its standard decomposition form. Numerical examples are provided to support the theoretical results.
2019 IEEE Conference on Control Technology and Applications (CCTA)
This paper presents a simple model of the heave (up-and-down) dynamics of an air cushioned vehicl... more This paper presents a simple model of the heave (up-and-down) dynamics of an air cushioned vehicle. A set of nonlinear differential equations are extended to the case of a bagged air skates using a Forchheimer porosity approximation. The model exhibits stability under atmospheric pressure, and a tendency towards instability in near-vacuum conditions. Inner estimates of regions of attractions are found to verify stability in atmosphere, and track simulation shows system disturbance rejection under a drifting subtrack height and gaps.
Advances in Computer Vision and Computational Biology, 2021
2018 26th Mediterranean Conference on Control and Automation (MED), 2018
We consider robust fault detection in distributed systems with first and second order agent model... more We consider robust fault detection in distributed systems with first and second order agent models. In such systems, we show that a ubiquitous class of consensus protocols leads to collective dynamics that lie in a nonnegative invariant set. Based on this, we derive LMI conditions for residual generators to sense faults in the nonnegative invariant set. An illustrative example is provided to highlight our approach and to show that it can reduce the time interval between fault occurrence and fault detection.
2017 IEEE High Performance Extreme Computing Conference (HPEC), 2017
Structural controllability, as a generic structure-based property in determining the ability of a... more Structural controllability, as a generic structure-based property in determining the ability of a complex network to reach the desired configuration, is addressed in this work. Using a robust measure derived from robust control theory, this paper deals with structural controllability of a type of weighted network of networks (NetoNets) involving linear couplings between its corresponding networks and clusters. Unlike the structural controllability degrees rooted in graph theory, this paper takes the advantage of uncertain systems to define the notion of structural controllability in a straightforward and less computationally complex way. Moreover, the spectrum of required energy is discussed. Eventually, the results for the proposed measure of structural controllability of scale-free networks are given to justify the proposed measure of an efficient and effective guarantee for fully controllability of the NetoNets in exposure to cluster and network-dependency connections. The proposed measure is an optimal solution according to structural energy-related control of the NetoNet where the upper bound of the required energy is illustrated an efficient measure for structural controllability of the class of NetoNet. Arbitrarily connectivity of low connected vertices to their higher connected counterparts in clusters results in effective controllability. In the same direction, as seminal works in structural controllability of complex networks to avoid the highly-connected nodes, the larger the cluster/network connectivity degree is, the less fully controllability of NetoNet is guaranteed.
2018 World Automation Congress (WAC), 2018
This paper considers the problem of eigenvalue assignment for discrete-time positive systems by s... more This paper considers the problem of eigenvalue assignment for discrete-time positive systems by state feedback control law. The goal of the paper is to solve the stabilization problem of discrete-time positive systems under the constraint that the eigenvalues of the closed-loop system are placed in the desired location while maintaining the positivity structure. Although the problem of positive stabilization has been solved using LP and LMI methods, the problem of eigenvalue assignment with positivity constraints is complex and remains challenging. It has only been tackled for a restricted class of single-input discrete-time positive systems. This paper aims to provide a solution for the multi-input case. After a brief review of positive systems and their stability properties, spectral characteristics of stable positive discrete-time systems is analyzed and the eigenvalue assignment is achieved by solving a set of chain equations. Numerical examples are provided to support theoretic...
Advances in Neuroergonomics and Cognitive Engineering, 2020
Time series modeling and analysis provides means of predicting the future and has been widely use... more Time series modeling and analysis provides means of predicting the future and has been widely used in a variety of fields ranging from seismology for predicting earthquake and volcanic eruption, to finance for risk assessment, and to quantum information processing. The conventional integer order models can only capture short-range dependence; for example, Poisson processes, Markov processes, autoregressive (AR), moving average (MA), autoregressive moving average (ARMA) and autoregressive integrated moving average (ARIMA) processes. In time series analysis, one of the conventional assumptions is that the coupling between values at different time instants decreases rapidly as the time difference or distance increases. However, there are situations where strong coupling between values at different times exhibit properties of long range dependence which cannot be processed by the conventional time series analysis. Typical examples of long range dependence signals include financial time series, underwater noise, electroencephalography (EEG) signal, etc. ARFIMA, a fractional order signal processing technique, is the generalization of the conventional integer order techniques, namely, ARIMA and ARMA methods. Hence, it is capable of capturing both short-range dependence and long-range dependence in signals. Compared to conventional integer order models, the ARFIMA model gives a better fit and result when dealing with the data which possess the long range dependence property. In this paper, we investigate the application of the ARFIMA as well as AR methods to model EEG signals obtained from different brain channels. We analyze the resulting correlations for comparison the benefits of ARFIMA over AR on the EEG data exhibiting the long range dependency property. The results showed that the prediction results have a better performance compared to the conventional ARMA models.
IET Control Theory & Applications, 2018
This study considers the problem of disturbance estimation and fault detection for positive syste... more This study considers the problem of disturbance estimation and fault detection for positive systems. Depending on the occurrences of disturbance and/or faults on positive systems, the authors use two types of observer structures and provide design strategies for reliable estimation of states, disturbance, and faults. Although various positive observers have been designed for positive systems, they fail to estimate the states in presence of unknown inputs. To realise simultaneous estimation of states and unknown inputs for positive systems, a special type of unknown input observer (UIO) called positive UIO (PUIO) is introduced and an linear matrix inequality (LMI) based approach is provided to make its design possible. The authors also show that simultaneous states and sensor fault estimation can be solved by a conversion scheme through a positive filtering process of the output. This allows to convert the sensor fault as an unknown input and employ the PUIO design. Finally, the problem of robust fault detection for positive systems is formulated and solved by combining the capability of UIO with proportional-integral (PI) observer. This integrated observer (PIUIO) decouples the unknown input disturbance through its UIO part while allowing PI part to estimate the fault. Several examples are constructed to support the theoretical results.
Advances in Industrial Control, 2014
Presenting current trends in the development and applications of intelligent systems in engineeri... more Presenting current trends in the development and applications of intelligent systems in engineering, this monograph focuses on recent research results in system identification and control. The recurrent neurofuzzy and the fuzzy cognitive network (FCN) models are presented. Both models are suitable for partially-known or unknown complex time-varying systems. Neurofuzzy Adaptive Control contains rigorous proofs of its statements which result in concrete conclusions for the selection of the design parameters of the algorithms presented. The neurofuzzy model combines concepts from fuzzy systems and recurrent high-order neural networks to produce powerful system approximations that are used for adaptive control. The FCN model stems from fuzzy cognitive maps and uses the notion of “concepts” and their causal relationships to capture the behavior of complex systems. The book shows how, with the benefit of proper training algorithms, these models are potent system emulators suitable for use in engineering systems. All chapters are supported by illustrative simulation experiments, while separate chapters are devoted to the potential industrial applications of each model including projects in: • contemporary power generation; • process control; and • conventional benchmarking problems. Researchers and graduate students working in adaptive estimation and intelligent control will find Neurofuzzy Adaptive Control of interest both for the currency of its models and because it demonstrates their relevance for real systems. The monograph also shows industrial engineers how to test intelligent adaptive control easily using proven theoretical results. Advances in Industrial Control aims to report and encourage the transfer of technology in control engineering. The rapid development of control technology has an impact on all areas of the control discipline. The series offers an opportunity for researchers to present an extended exposition of new work in all aspects of industrial control. aims to report and encourage the transfer of technology in control engineering. The rapid development of control technology has an impact on all areas of the control discipline. The series offers an opportunity for researchers to present an extended exposition of new work in all aspects of industrial control
ABSTRACT Due to many resource constraints of wireless sensor networks and characteristics of sens... more ABSTRACT Due to many resource constraints of wireless sensor networks and characteristics of sensor data stream, some of the data management techniques that work well for traditional database are insufficient for managing and processing sensor network data. In this paper we present a cluster based query protocol for wireless sensor networks which uses self-organized sensor clusters to register queries, process queries and disseminate data within the sensor network. This protocol can provide an integrated solution to address some of the challenge problems of wireless sensor networks, including locating sensors, energy efficient data query processing, and fault tolerant network operations.
ABSTRACT We propose a formal method to automatically integrate security rules regarding an access... more ABSTRACT We propose a formal method to automatically integrate security rules regarding an access control policy (expressed in Or-BAC) in Java programs. Given an untrusted application and a set of Or-BAC security rules, our method derives corresponding AspectJ aspects. Derived aspects modify the behaviour of the underlying program so as to meet the policy. Then, these aspects are weaved into the target program (using the AspectJ compiler). The result is a trusted program on which the security policy is enforced. This approach was applied in order to secure the behaviour of a travel agency application.
Advances in Difference Equations, 2012
This paper is concerned with the robust stability and stabilization for a class of switched discr... more This paper is concerned with the robust stability and stabilization for a class of switched discrete-time systems with state parameter uncertainty. Firstly, a new matrix inequality considering uncertainties is introduced and proved. By means of it, a novel sufficient condition for robust stability and stabilization of a class of uncertain switched discrete-time systems is presented. Furthermore, based on the result obtained, the switching law is designed and has been performed well, and some sufficient conditions of robust stability and stabilization have been derived for the uncertain switched discrete-time systems using the Lyapunov stability theorem, block matrix method, and inequality technology. Finally, some examples are exploited to illustrate the effectiveness of the proposed schemes.
2019 IEEE 15th International Conference on Control and Automation (ICCA)
This paper considers observer based fault detection and isolation in a distributed setting with a... more This paper considers observer based fault detection and isolation in a distributed setting with agents under consensus dynamics. The distributed fault detection and isolation problem is introduced along with system models to represent nominal and faulty conditions. It is shown that the positive unknown input observer (PUIO) effectively estimates the fault signal in an agent and that it can be used for residual generation and fault isolation. The conditions under which fault isolation is achievable are derived as a function of the distributed topology of the agents in the network. An LMI is derived that can be used to conveniently obtain the residual generator gains. The distributed PUIO design approach is illustrated with an example.
2021 International Conference on Computational Science and Computational Intelligence (CSCI)
IEEE 30th Annual Northeast Bioengineering Conference, 2004. Proceedings of the
Contactless electrical conductivity imaging (CECI) is a new medical imaging modality that uses ma... more Contactless electrical conductivity imaging (CECI) is a new medical imaging modality that uses magnetic field measurements from magnetic excitations. Due to strong attenuation of magnetic fields with distance, the major challenge of CECI is imaging deep voxels. In this study, the vertical line spread, and, vertical line separation functions of the CECI are assessed to explore this phenomenon. It is found that CECI can produce low resolution images up to a depth of 3 cm. The modality appears to be a promising tool to provide conductivity images of the brain.
2017 IEEE 30th Canadian Conference on Electrical and Computer Engineering (CCECE), 2017
This paper considers the stabilization problem of linear dynamical systems with combined structur... more This paper considers the stabilization problem of linear dynamical systems with combined structural properties of symmetry and positivity. Such systems arise in various applications including electromechanical systems, aerodynamics, structural vibration, robotics and compartmental systems, in which the stabilization and performance improvement are the main objectives. Although the goal of the paper is to design controller for such systems, we broaden the scope by designing controller for general systems such that the closed-loop systems admits the structural constraints of symmetry and positivity. We concentrate on two different classes of symmetric positive systems. The first class has the state-space symmetric representation A = AT, C = BT with A being a Metzler matrix and the positive pair of matrices (B, C). The stability conditions of this class are used to formulate and solve symmetric positive stabilization by means of state feedback for systems with arbitrary state-space parameters. The second class is defined through the block controllable canonical form in which the block sub-matrices associated with the system matrix A are symmetric Metzlerian. Assuming that such systems are unstable, we design state feedback control law such that the closed-loop system becomes stable and maintains its structure. A generalized symmetric Metzlerian stabilization algorithm is provided through a set of chain equations to achieve this goal. Numerical examples are provided for stabilization of both classes.
Abstract. This paper shows the possibility of including weighting functions in H∞/LTR design to i... more Abstract. This paper shows the possibility of including weighting functions in H∞/LTR design to improve the recovery in specic frequency ranges. It turns out that it is still possible to derive a solution by solving only one Riccati equation in both cases. The observer gain is given in explicit form. The weighted LTR design method can be applied to both minimum phase systems as well as to non-minimum phase systems.
2018 World Automation Congress (WAC), 2018
Brain computer interfaces (BCIs) have attracted great attention for human computer interaction. I... more Brain computer interfaces (BCIs) have attracted great attention for human computer interaction. In BCIs that are based on electroencephalography (EEG), low signal-to-noise ratio causes user intent inference to be error-prone and uncertain. Thus, the control of such systems becomes a challenging task. This paper presents an adaptive proportional BCI controller for linear dynamical systems. An approach to optimize the closed-loop control performance of dynamic systems driven by probabilistic desired/reference inputs is illustrated by designing an adaptive proportional controller that exerts forces on a mass moving linearly in one dimensional space, where the user intent is inferred by an EEG-based BCI. The BCI provides a continuous-valued output by fusing all available EEG evidence; the adaptive proportional controller optimally selects a time-varying gain that considers prior/context knowledge of user intent/environment and the probabilistic error characteristics of the BCI in inferring user intent. Such an adaptive controller that considers human-intent-inference error statistics in human-in-the-loop control systems, especially relevant for BCI-based user intent inference where accuracies may be low, improves overall closed-loop system performance measured by mean-squared tracking error. The performance of the proposed BCI-controller has been evaluated by computer simulations using code based visual evoked potentials (c-VEPs). The employed datasets were collected from healthy individuals between 20 and 30 years old with normal or corrected normal vision during calibration sessions for BCI. Results indicate that this adaptive control scheme is particularly beneficial for users with poor BCI performance.
2019 IEEE Conference on Control Technology and Applications (CCTA), 2019
This paper considers the problem of robustly identifying m intruders in a network consisting of n... more This paper considers the problem of robustly identifying m intruders in a network consisting of n cooperative agents which are subject to unknown disturbances. First, a distributed system model is introduced so that the relationship between agents, the attacks and unknown disturbances can be captured. Next, the distributed identification scheme is formulated as a spectral assignment problem and necessary filter gains are obtained through a carefully constructed linear system of equations. Necessary and sufficient conditions to decouple the unknown disturbances from the agents residual generators are derived in terms of filter gain matrices. It is shown that the problem of discriminating between unknown disturbances and attacks in a distributed system under consensus dynamics can be reduced to the problem of determining a set of constraints on the spectrum of the residual generator coefficient matrices. The approach is illustrated through an example.
2016 World Automation Congress (WAC), 2016
This paper considers the problem of positive stabilization of linear continuous-time singular sys... more This paper considers the problem of positive stabilization of linear continuous-time singular systems by state feedback. First, necessary and sufficient conditions for a singular system to be admissible (stable, regular, and impulse-free) are given in terms of LMI. Based on this result, the state feedback admissibility problem is solved without positivity constraints. Then, it is shown that the LMI based stabilization results of singular systems can conveniently be combined with positivity constraints to establish positive stabilization of general singular systems. We also show that the well-known positive stabilization technique based on LMI for conventional linear systems can be applied to singular systems represented by its standard decomposition form. Numerical examples are provided to support the theoretical results.
2019 IEEE Conference on Control Technology and Applications (CCTA)
This paper presents a simple model of the heave (up-and-down) dynamics of an air cushioned vehicl... more This paper presents a simple model of the heave (up-and-down) dynamics of an air cushioned vehicle. A set of nonlinear differential equations are extended to the case of a bagged air skates using a Forchheimer porosity approximation. The model exhibits stability under atmospheric pressure, and a tendency towards instability in near-vacuum conditions. Inner estimates of regions of attractions are found to verify stability in atmosphere, and track simulation shows system disturbance rejection under a drifting subtrack height and gaps.
Advances in Computer Vision and Computational Biology, 2021
2018 26th Mediterranean Conference on Control and Automation (MED), 2018
We consider robust fault detection in distributed systems with first and second order agent model... more We consider robust fault detection in distributed systems with first and second order agent models. In such systems, we show that a ubiquitous class of consensus protocols leads to collective dynamics that lie in a nonnegative invariant set. Based on this, we derive LMI conditions for residual generators to sense faults in the nonnegative invariant set. An illustrative example is provided to highlight our approach and to show that it can reduce the time interval between fault occurrence and fault detection.
2017 IEEE High Performance Extreme Computing Conference (HPEC), 2017
Structural controllability, as a generic structure-based property in determining the ability of a... more Structural controllability, as a generic structure-based property in determining the ability of a complex network to reach the desired configuration, is addressed in this work. Using a robust measure derived from robust control theory, this paper deals with structural controllability of a type of weighted network of networks (NetoNets) involving linear couplings between its corresponding networks and clusters. Unlike the structural controllability degrees rooted in graph theory, this paper takes the advantage of uncertain systems to define the notion of structural controllability in a straightforward and less computationally complex way. Moreover, the spectrum of required energy is discussed. Eventually, the results for the proposed measure of structural controllability of scale-free networks are given to justify the proposed measure of an efficient and effective guarantee for fully controllability of the NetoNets in exposure to cluster and network-dependency connections. The proposed measure is an optimal solution according to structural energy-related control of the NetoNet where the upper bound of the required energy is illustrated an efficient measure for structural controllability of the class of NetoNet. Arbitrarily connectivity of low connected vertices to their higher connected counterparts in clusters results in effective controllability. In the same direction, as seminal works in structural controllability of complex networks to avoid the highly-connected nodes, the larger the cluster/network connectivity degree is, the less fully controllability of NetoNet is guaranteed.
2018 World Automation Congress (WAC), 2018
This paper considers the problem of eigenvalue assignment for discrete-time positive systems by s... more This paper considers the problem of eigenvalue assignment for discrete-time positive systems by state feedback control law. The goal of the paper is to solve the stabilization problem of discrete-time positive systems under the constraint that the eigenvalues of the closed-loop system are placed in the desired location while maintaining the positivity structure. Although the problem of positive stabilization has been solved using LP and LMI methods, the problem of eigenvalue assignment with positivity constraints is complex and remains challenging. It has only been tackled for a restricted class of single-input discrete-time positive systems. This paper aims to provide a solution for the multi-input case. After a brief review of positive systems and their stability properties, spectral characteristics of stable positive discrete-time systems is analyzed and the eigenvalue assignment is achieved by solving a set of chain equations. Numerical examples are provided to support theoretic...
Advances in Neuroergonomics and Cognitive Engineering, 2020
Time series modeling and analysis provides means of predicting the future and has been widely use... more Time series modeling and analysis provides means of predicting the future and has been widely used in a variety of fields ranging from seismology for predicting earthquake and volcanic eruption, to finance for risk assessment, and to quantum information processing. The conventional integer order models can only capture short-range dependence; for example, Poisson processes, Markov processes, autoregressive (AR), moving average (MA), autoregressive moving average (ARMA) and autoregressive integrated moving average (ARIMA) processes. In time series analysis, one of the conventional assumptions is that the coupling between values at different time instants decreases rapidly as the time difference or distance increases. However, there are situations where strong coupling between values at different times exhibit properties of long range dependence which cannot be processed by the conventional time series analysis. Typical examples of long range dependence signals include financial time series, underwater noise, electroencephalography (EEG) signal, etc. ARFIMA, a fractional order signal processing technique, is the generalization of the conventional integer order techniques, namely, ARIMA and ARMA methods. Hence, it is capable of capturing both short-range dependence and long-range dependence in signals. Compared to conventional integer order models, the ARFIMA model gives a better fit and result when dealing with the data which possess the long range dependence property. In this paper, we investigate the application of the ARFIMA as well as AR methods to model EEG signals obtained from different brain channels. We analyze the resulting correlations for comparison the benefits of ARFIMA over AR on the EEG data exhibiting the long range dependency property. The results showed that the prediction results have a better performance compared to the conventional ARMA models.
IET Control Theory & Applications, 2018
This study considers the problem of disturbance estimation and fault detection for positive syste... more This study considers the problem of disturbance estimation and fault detection for positive systems. Depending on the occurrences of disturbance and/or faults on positive systems, the authors use two types of observer structures and provide design strategies for reliable estimation of states, disturbance, and faults. Although various positive observers have been designed for positive systems, they fail to estimate the states in presence of unknown inputs. To realise simultaneous estimation of states and unknown inputs for positive systems, a special type of unknown input observer (UIO) called positive UIO (PUIO) is introduced and an linear matrix inequality (LMI) based approach is provided to make its design possible. The authors also show that simultaneous states and sensor fault estimation can be solved by a conversion scheme through a positive filtering process of the output. This allows to convert the sensor fault as an unknown input and employ the PUIO design. Finally, the problem of robust fault detection for positive systems is formulated and solved by combining the capability of UIO with proportional-integral (PI) observer. This integrated observer (PIUIO) decouples the unknown input disturbance through its UIO part while allowing PI part to estimate the fault. Several examples are constructed to support the theoretical results.
Advances in Industrial Control, 2014
Presenting current trends in the development and applications of intelligent systems in engineeri... more Presenting current trends in the development and applications of intelligent systems in engineering, this monograph focuses on recent research results in system identification and control. The recurrent neurofuzzy and the fuzzy cognitive network (FCN) models are presented. Both models are suitable for partially-known or unknown complex time-varying systems. Neurofuzzy Adaptive Control contains rigorous proofs of its statements which result in concrete conclusions for the selection of the design parameters of the algorithms presented. The neurofuzzy model combines concepts from fuzzy systems and recurrent high-order neural networks to produce powerful system approximations that are used for adaptive control. The FCN model stems from fuzzy cognitive maps and uses the notion of “concepts” and their causal relationships to capture the behavior of complex systems. The book shows how, with the benefit of proper training algorithms, these models are potent system emulators suitable for use in engineering systems. All chapters are supported by illustrative simulation experiments, while separate chapters are devoted to the potential industrial applications of each model including projects in: • contemporary power generation; • process control; and • conventional benchmarking problems. Researchers and graduate students working in adaptive estimation and intelligent control will find Neurofuzzy Adaptive Control of interest both for the currency of its models and because it demonstrates their relevance for real systems. The monograph also shows industrial engineers how to test intelligent adaptive control easily using proven theoretical results. Advances in Industrial Control aims to report and encourage the transfer of technology in control engineering. The rapid development of control technology has an impact on all areas of the control discipline. The series offers an opportunity for researchers to present an extended exposition of new work in all aspects of industrial control. aims to report and encourage the transfer of technology in control engineering. The rapid development of control technology has an impact on all areas of the control discipline. The series offers an opportunity for researchers to present an extended exposition of new work in all aspects of industrial control
ABSTRACT Due to many resource constraints of wireless sensor networks and characteristics of sens... more ABSTRACT Due to many resource constraints of wireless sensor networks and characteristics of sensor data stream, some of the data management techniques that work well for traditional database are insufficient for managing and processing sensor network data. In this paper we present a cluster based query protocol for wireless sensor networks which uses self-organized sensor clusters to register queries, process queries and disseminate data within the sensor network. This protocol can provide an integrated solution to address some of the challenge problems of wireless sensor networks, including locating sensors, energy efficient data query processing, and fault tolerant network operations.
ABSTRACT We propose a formal method to automatically integrate security rules regarding an access... more ABSTRACT We propose a formal method to automatically integrate security rules regarding an access control policy (expressed in Or-BAC) in Java programs. Given an untrusted application and a set of Or-BAC security rules, our method derives corresponding AspectJ aspects. Derived aspects modify the behaviour of the underlying program so as to meet the policy. Then, these aspects are weaved into the target program (using the AspectJ compiler). The result is a trusted program on which the security policy is enforced. This approach was applied in order to secure the behaviour of a travel agency application.
Advances in Difference Equations, 2012
This paper is concerned with the robust stability and stabilization for a class of switched discr... more This paper is concerned with the robust stability and stabilization for a class of switched discrete-time systems with state parameter uncertainty. Firstly, a new matrix inequality considering uncertainties is introduced and proved. By means of it, a novel sufficient condition for robust stability and stabilization of a class of uncertain switched discrete-time systems is presented. Furthermore, based on the result obtained, the switching law is designed and has been performed well, and some sufficient conditions of robust stability and stabilization have been derived for the uncertain switched discrete-time systems using the Lyapunov stability theorem, block matrix method, and inequality technology. Finally, some examples are exploited to illustrate the effectiveness of the proposed schemes.
2019 IEEE 15th International Conference on Control and Automation (ICCA)
This paper considers observer based fault detection and isolation in a distributed setting with a... more This paper considers observer based fault detection and isolation in a distributed setting with agents under consensus dynamics. The distributed fault detection and isolation problem is introduced along with system models to represent nominal and faulty conditions. It is shown that the positive unknown input observer (PUIO) effectively estimates the fault signal in an agent and that it can be used for residual generation and fault isolation. The conditions under which fault isolation is achievable are derived as a function of the distributed topology of the agents in the network. An LMI is derived that can be used to conveniently obtain the residual generator gains. The distributed PUIO design approach is illustrated with an example.
2021 International Conference on Computational Science and Computational Intelligence (CSCI)
IEEE 30th Annual Northeast Bioengineering Conference, 2004. Proceedings of the
Contactless electrical conductivity imaging (CECI) is a new medical imaging modality that uses ma... more Contactless electrical conductivity imaging (CECI) is a new medical imaging modality that uses magnetic field measurements from magnetic excitations. Due to strong attenuation of magnetic fields with distance, the major challenge of CECI is imaging deep voxels. In this study, the vertical line spread, and, vertical line separation functions of the CECI are assessed to explore this phenomenon. It is found that CECI can produce low resolution images up to a depth of 3 cm. The modality appears to be a promising tool to provide conductivity images of the brain.
2017 IEEE 30th Canadian Conference on Electrical and Computer Engineering (CCECE), 2017
This paper considers the stabilization problem of linear dynamical systems with combined structur... more This paper considers the stabilization problem of linear dynamical systems with combined structural properties of symmetry and positivity. Such systems arise in various applications including electromechanical systems, aerodynamics, structural vibration, robotics and compartmental systems, in which the stabilization and performance improvement are the main objectives. Although the goal of the paper is to design controller for such systems, we broaden the scope by designing controller for general systems such that the closed-loop systems admits the structural constraints of symmetry and positivity. We concentrate on two different classes of symmetric positive systems. The first class has the state-space symmetric representation A = AT, C = BT with A being a Metzler matrix and the positive pair of matrices (B, C). The stability conditions of this class are used to formulate and solve symmetric positive stabilization by means of state feedback for systems with arbitrary state-space parameters. The second class is defined through the block controllable canonical form in which the block sub-matrices associated with the system matrix A are symmetric Metzlerian. Assuming that such systems are unstable, we design state feedback control law such that the closed-loop system becomes stable and maintains its structure. A generalized symmetric Metzlerian stabilization algorithm is provided through a set of chain equations to achieve this goal. Numerical examples are provided for stabilization of both classes.
Abstract. This paper shows the possibility of including weighting functions in H∞/LTR design to i... more Abstract. This paper shows the possibility of including weighting functions in H∞/LTR design to improve the recovery in specic frequency ranges. It turns out that it is still possible to derive a solution by solving only one Riccati equation in both cases. The observer gain is given in explicit form. The weighted LTR design method can be applied to both minimum phase systems as well as to non-minimum phase systems.