Observer-based Technique Research Articles

Signed-data reinforced observer-based fault diagnosis for virtually-coupled electric multiple units trains

Virtual coupling has been recognized as a promising yet challenging technology for the next generation of train control systems. As a mean of guaranteeing the safety of closer-running of virtually-coupled electric multiple units (EMU) trains, fault detection and diagnosis play a critical role in perceiving abnormal running conditions. This paper develops a new signed-data reinforced observer-based technique for dealing with actuator faults’ detection and diagnosis for virtually-coupled EMU trains. The introduction of novel signed-data reinforcement technique ensures that unbiased fault diagnosis can be guaranteed with any regressor vector composed of control signals on EMU trains. Fault detection and diagnosis observers, together with fault alarming principle, information transmission coding strategy are elaborated using Lyapunov stability theorem. Simulation results are given to demonstrate the effectiveness of proposed algorithm.

Multi-Agent and State Observer-Based Technique for Microgrid Protection

Multi-Agent and State Observer-Based Technique for Microgrid Protection

Estimating rumor source in social networks using incomplete observer information

Utilization of observers in social networks for rumor source estimation is quite common. However, existing observer-based techniques have several drawbacks. Mostly existing approaches use the information available (such as timestamps) with many observers to locate the source node, which degrades their performance. Estimation of exact timestamps of non-observer nodes also poses an added challenge. To overcome these issues, we introduced a Radial Observer Source Estimator (ROSE) algorithm to estimate the source node in the network. The ROSE algorithm utilizes shares of a particular message instead of timestamps, and it considers the information available with only those observers that have minimum shares. The overall complexity of the algorithm is O(log(N)∗N2), where N is the number of nodes in a network. We evaluate our algorithm on small-scale, large-scale, and artificial networks. Empirical results show that the proposed algorithm is highly efficient in terms of execution time compared to four other state-of-the-art rumor source identification algorithms. Also, it mostly performs well in accurately discovering a rumor source node, with a higher success rate and fewer distance errors.

Observer-Based Boundary Fuzzy Control Design of Nonlinear Parabolic PDE Systems Using Mobile Sensors

This paper studies the boundary fuzzy control problem for nonlinear parabolic partial differential equation (PDE) systems under spatially noncollocated mobile sensors. In a real setup, sensors and actuators can never be placed at the same location, and the noncollocated setting may be beneficial in some application scenarios. The control design is very difficult due to the noncollocated mobile observation, which can be solved by an observer-based technique. At first, a Takagi-Sugeno fuzzy PDE model is devoted to accurately representing the nonlinear parabolic PDE system. Next, we present a state estimation scheme including fuzzy Luenberger-type PDE state observer plus mobile sensor guidance. Then, an observer-based boundary fuzzy controller is posed to render the resulting closedloop system exponentially stable, and the exponential decay rate is increased by the designed mobile sensor guidance laws. At last, two examples verify the proposed method.

Accurate SM Disturbance Observer-Based Demagnetization Fault Diagnosis With Parameter Mismatch Impacts Eliminated for IPM Motors

This paper proposes a novel sliding mode (SM) disturbance observer-based technique to diagnose demagnetization fault of interior permanent magnet (IPM) motors with stator parameter mismatch impacts eliminated. First, the IPM motor model incorporating the disturbances caused by the PM demagnetization and stator parameter mismatch is established. Then, an SM disturbance observer is constructed to identify the overall disturbance caused by all parameters, with its stability discussed by using the Lyapunov function. Third, a current-analysis-based method is developed to extract the disturbance only caused by flux linkage mismatch from the overall disturbance. Third, the extracted disturbance is adopted to calculate the real flux linkage, achieving demagnetization fault judgment and demagnetization degree calculation. Finally, experiment is conducted on two IPM motors to validate the proposed flux linkage estimation and fault diagnosis methods.

Soft Sensor Design via Switching Observers.

The goal of the paper is the design of soft sensors for single input single output (SISO) nonlinear processes. This goal is of essential importance for process monitoring, fault detection and fault isolation. The observer-based technique, being a fruitful direction in soft sensor design, is followed to develop soft sensors for nonlinear processes with known dynamics and unknown physical parameters. A new and general approach, based on the identified I/O linear approximant system descriptions, around prespecified operating points, and a bank of switching linear observers, will be developed. The system property of the I/O reconstructability of the state space linear approximant of a nonlinear model is presented. The design of each observer is based on the I/O measurements and structural characteristics of the nonlinear process. Observer-oriented target areas are introduced, and the respective dense web principle is formulated. The design is completed by the design of a data-driven rule-based system, providing stepwise switching among the observers of the bank. The number of observers of the bank is equal to the number of the linear approximants of the nonlinear process model and is equal to the number of the respective target operating areas. The target operating areas are required to satisfy the dense web principle. The information provided by the soft sensor is the estimation of the non-measured variables of the process. The information used by the soft sensor is the identified I/O approximants of the process as well as the real time values of the measurement variables. The efficiency of the design scheme is illustrated through symbolic and numerical simulation results for a chemostat. The nonlinear model of the chemostat is initially approximated by a set of ten linear approximants. After, the I/O approximants are identified, the respective observers are designed and the target operating areas are determined, where several cases of the satisfaction of the dense web principle are investigated. The soft sensor is composed in terms of the designed observers. Simulation results illustrate the satisfactory performance of the designed soft sensor.

Application of the unified control and detection framework to detecting stealthy integrity cyber-attacks on feedback control systems

This paper addresses issues of detecting stealthy integrity cyber-attacks on automatic control systems. A general form of integrity cyber-attacks that cannot be detected using the established observer-based technique is first introduced as kernel attacks. The well-known replay, zero dynamics and covert attacks are special forms of the kernel attacks. Existence conditions for the kernel attacks are presented. It is demonstrated, in the unified framework of control and detection, that all kernel attacks can be structurally detected when not only the observer-based residual, but also the control signal based residual signals are used for the detection purpose. Two schemes for detecting kernel attacks are then proposed, which allow reliable attack detection without loss of control performance. While the first scheme is similar to the well-established moving target method and auxiliary system aided detection scheme, the second one is realised with encrypted transmissions of control and monitoring signals in the feedback control system, which prevents adversary to gain system knowledge by means of eavesdropping attacks. Both schemes are illustrated by an experimental study on a three-tank control system.

A Sensor Fault Detection Scheme as a Functional Safety Feature for DC-DC Converters.

DC-DC converters are widely used in a large number of power conversion applications. As in many other systems, they are designed to automatically prevent dangerous failures or control them when they arise; this is called functional safety. Therefore, random hardware failures such as sensor faults have to be detected and handled properly. This proper handling means achieving or maintaining a safe state according to ISO 26262. However, to achieve or maintain a safe state, a fault has to be detected first. Sensor faults within DC-DC converters are generally detected with hardware-redundant sensors, despite all their drawbacks. Within this article, this redundancy is addressed using observer-based techniques utilizing Extended Kalman Filters (EKFs). Moreover, the paper proposes a fault detection and isolation scheme to guarantee functional safety. For this, a cross-EKF structure is implemented to work in cross-parallel to the real sensors and to replace the sensors in case of a fault. This ensures the continuity of the service in case of sensor faults. This idea is based on the concept of the virtual sensor which replaces the sensor in case of fault. Moreover, the concept of the virtual sensor is broader. In fact, if a system is observable, the observer offers a better performance than the sensor. In this context, this paper gives a contribution in this area. The effectiveness of this approach is tested with measurements on a buck converter prototype.

Observer-Based Sliding Mode Control for Networked Fuzzy Singularly Perturbed Systems Under Weighted Try-Once-Discard Protocol

In this article, the sliding mode control issue is investigated for a class of discrete-time Takagi–Sugeno fuzzy networked singularly perturbed systems via an observer-based technique. Moreover, to process the measurement output and schedule the transmission sequence for relieving the communication burden, a logarithmic quantizer and a weighted try-once-discard protocol are synthesized, which can further improve the network bandwidth utilization in networked control systems. Based on the fuzzy observer states, a novel fuzzy sliding surface is established by considering the singularly perturbed parameter properly, and we endeavor to synthesize a sliding mode control law such that the reachability of the prescribed sliding surface could be guaranteed. In addition, by virtue of the convex optimization theory and Lyapunov approach, sufficient conditions are developed to guarantee the asymptotic stability of the sliding mode dynamics as well as the error system with an expected <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$H_{\infty }$</tex-math></inline-formula> performance. Finally, a verification example is presented to illustrate the feasibility and effectivity of the proposed method.

Online Observer-Based Inverse Reinforcement Learning

In this letter, a novel approach to the output-feedback inverse reinforcement learning (IRL) problem is developed by casting the IRL problem, for linear systems with quadratic cost functions, as a state estimation problem. Two observer-based techniques for IRL are developed, including a novel observer method that re-uses previous state estimates via history stacks. Theoretical guarantees for convergence and robustness are established under appropriate excitation conditions. Simulations demonstrate the performance of the developed observers and filters under noisy and noise-free measurements.

Nonlinear extended state observer-based output feedback stabilization control for uncertain nonlinear half-car active suspension systems

This paper proposes a nonlinear extended state observer-based output feedback stabilization controller for a half-car active suspension system, to overcome factors leading to performance deterioration, such as nonlinearities, parameter uncertainties, unmodeled dynamics, and uncertain external disturbances. Nonlinear extended state observers are first developed to estimate the unmeasurable states and unknown dynamics of heave and pitch motions. Then, finite-time stabilization control laws are synthesized to improve the vehicle body attitude and ride comfort. The proposed control scheme is an improvement over the existing linear extended state observer-based techniques, given its high observation quality and finite-time convergence. From the perspective of practical implementation, the controller is independent of an accurate mathematical model and only requires the measurable output signals. By constructing weighted error and auxiliary state systems, and employing geometric homogeneity theory, the finite-time stability of estimation errors and suspension states is systematically proven within the Lyapunov framework. Furthermore, the zero dynamics stability is analyzed to guarantee the suspension space constraint and road holding. Finally, numerical simulations are conducted on some representative road excitations and the results are compared to the existing solution and passive suspension. The analysis has confirmed the effectiveness and robustness of the proposed control method.

A Lyapunov-based design of dynamic feedback compensator for linear parabolic MIMO PDEs

Abstract This paper discusses dynamic feedback compensator design for a linear parabolic partial differential equation (PDE) with multiple inputs and multiple outputs. Actuating control inputs are provided by actuators distributed over partial areas (or active at specified positions) of the spatial domain, and observation outputs are taken from the non-collocated sensors distributed over partial areas of the spatial domain. An observer-based dynamic feedback compensator is constructed via the observer-based feedback control technique to exponentially stabilize the multi-input–multi-output PDE in the spatial $\mathscr{L}^2$ norm. By constructing an appropriate Lyapunov function candidate and using two variants of Poincaré–Wirtinger inequality, sufficient conditions on the existence of such observer-based dynamic feedback compensator are developed and presented in terms of linear matrix inequalities. The well posedness of the closed-loop coupled PDEs is also analyzed within the framework of $C_0$ semigroup theory. Finally, numerical simulation results are given to show the effectiveness of the proposed method.

Model-Based Battery Thermal Fault Diagnostics: Algorithms, Analysis, and Experiments

Safety and reliability remain critical issues for lithium-ion (Li-ion) batteries. Out of many possible degradation modes, thermal faults constitute a significant part of critical causes that lead to battery degradation and failure. Therefore, it is extremely important to diagnose these thermal faults in real time to ensure battery safety. Motivated by this fact, we propose a partial differential equation (PDE) model-based real-time scheme in this paper for diagnosing thermal faults in Li-ion batteries. The objective of the diagnostic scheme is to detect and estimate the size of the thermal fault. We utilize a distributed parameter 1-D thermal model for cylindrical battery cells in conjunction with PDE observer-based techniques to design the scheme. Furthermore, we apply threshold-based technique to ensure robustness against modeling and measurement uncertainties. The effectiveness of the scheme is illustrated by: 1) analytical convergence verification of the PDE observers under heathy and faulty conditions utilizing Lyapunov stability theory; 2) extensive simulation case studies; 3) robustness analysis against model parametric uncertainties; and 4) experimental studies on a commercial Li-ion battery cell.

Robust Performance and Observer Based Control for Periodic Discrete-Time Uncertain Systems

This paper presents an approach for the synthesis of observer-based controllers for discrete-time periodic linear systems. The H2 performance criterion has been employed to design both the observer and the controller. For the periodic observer design, two conditions in the form of Linear Matrix Inequalities (LMIs) are proposed, which stem from the Lyapunov Theory applied over the dynamics of the estimation error. The LMI condition obtained for the periodic state-feedback controller results from the application of the duality principle over the periodic system, under the assumption that only the estimated states are available to be used in the state-feedback compensation. Numerical experiments illustrate the potential of the proposed observer-based control technique.

Body temperature, activity patterns and hunting in free-living cheetah: biologging reveals new insights.

As one of the few felids that is predominantly diurnal, cheetahs (Acinonyx jubatus) can be exposed to high heat loads in their natural habitat. Little is known about long-term patterns of body temperature and activity (including hunting) in cheetahs because long-term concurrent measurements of body temperature and activity have never been reported for cheetahs, or, indeed, for any free-living felid. We report here body temperature and locomotor activity measured with implanted data loggers over 7 months in 5 free-living cheetahs in Namibia. Air temperature ranged from a maximum of 39 °C in summer to -2 °C in winter. Cheetahs had higher (∼0.4 °C) maximum 24-h body temperatures, later acrophase (∼1 h), with larger fluctuations in the range of the 24-h body temperature rhythm (approximately 0.4 °C) during a hot-dry period than during a cool-dry period, but maintained homeothermy irrespective of the climatic conditions. As ambient temperatures increased, the cheetahs shifted from a diurnal to a crepuscular activity pattern, with reduced activity between 900 and 1500 hours and increased nocturnal activity. The timing of hunts followed the general pattern of activity; the cheetahs hunted when they were on the move. Cheetahs hunted if an opportunity presented itself; on occasion they hunted in the midday heat or in total darkness (new moon). Biologging revealed insights into cheetah biology that are not accessible by traditional observer-based techniques.

Observer-Based Pulsed Signal Injection for Grid Impedance Estimation in Three-Phase Systems

This paper addresses the estimation of the grid impedance and the control of grid-tied converters by combining pulsed signal injection (PSI) and observer-based techniques. A Luenberger-based observer is used for controlling the grid-side current of an LCL filter by only measuring the converter-side currents and the grid-side voltage. This configuration mitigates the effects of parameter variation at the LCL filter. Under grid impedance changes, the observer control signal will be used for triggering the signal injection. A PSI approach is employed for estimating online the grid impedance using a recursive least square algorithm. Compared with existing grid-impedance estimation techniques, the proposed method can: 1) identify a generic RL grid impedance, even under unbalanced conditions; 2) reduce the distortion induced by the excitation signal by relying on the observer to triggering the injection when a grid impedance change is detected; and 3) identify grid impedance values much lower than the converter filter impedance, which is the usual situation when the converter rated power is well below the grid rating. Finally, it is worth pointing out that the proposed estimation technique is well-suited to be incorporated into an adaptive current controller scheme.

Parameter Identification of Inverter-Fed Induction Motors: A Review

Induction motor parameters are essential for high-performance control. However, motor parameters vary because of winding temperature rise, skin effect, and flux saturation. Mismatched parameters will consequently lead to motor performance degradation. To provide accurate motor parameters, in this paper, a comprehensive review of offline and online identification methods is presented. In the implementation of offline identification, either a DC voltage or single-phase AC voltage signal is injected to keep the induction motor standstill, and the corresponding identification algorithms are discussed in the paper. Moreover, the online parameter identification methods are illustrated, including the recursive least square, model reference adaptive system, DC and high-frequency AC voltage injection, and observer-based techniques, etc. Simulations on selected identification techniques applied to an example induction motor are presented to demonstrate their performance and exemplify the parameter identification methods.

Approaches to team performance assessment: a comparison of self-assessment reports and behavioral observer scales

Human factors research popularly employs perception-based techniques to investigate team performance and its dependency to cognitive processes. Such studies frequently rely upon either observer-based or self-assessment techniques to collect data. In this study, we examined behavioral observer ratings and self-assessment ratings for measuring team performance in virtual teams, with team performance regarded as a combination of task outcome and team cognition. Juxtaposing self-assessments and observer ratings from a quasi-experiment comparing team performance rating techniques reveals that they indeed produce overall similar results, with both singling out teamwork effectiveness ratings as the strongest contributor to overall team performance. However, the comparisons show remarkably low correlation on individual questionnaire items. The most striking difference is that the team members’ self-assessments of workload are lower than the corresponding observer ratings. In particular, the self-assessments do not correlate at all with overall team performance, whereas the observers’ workload ratings are more consistent with contemporary research that suggests a strong correlation between workload and team performance, suggesting that observer-based techniques are more reliable than self-assessments for assessing workload. For other ratings, the results show that the two techniques are fairly equal, suggesting that the choice between methods to employ can be deferred to other considerations such as obtrusiveness, accessibility, and resource availability.

Detectability in stochastic discrete event systems

A discrete event system possesses the property of detectability if it allows an observer to perfectly estimate the current state of the system after a finite number of observed symbols, i.e., detectability captures the ability of an observer to eventually perfectly estimate the system state. In this paper we analyze detectability in stochastic discrete event systems (SDES) that can be modeled as probabilistic finite automata. More specifically, we define the notion of A-detectability, which characterizes our ability to estimate the current state of a given SDES with increasing certainty as we observe more output symbols. The notion of A-detectability is differentiated from previous notions for detectability in SDES because it takes into account the probability of problematic observation sequences (that do not allow us to perfectly deduce the system state), whereas previous notions for detectability in SDES considered each observation sequence that can be generated by the underlying system. We discuss observer-based techniques that can be used to verify A-detectability, and provide associated necessary and sufficient conditions. We also prove that A-detectability is a PSPACE-hard problem.

An Adaptive Observer-Based Switched Methodology for the Identification of a Perturbed Sinusoidal Signal: Theory and Experiments

This paper deals with a novel adaptive observer-based technique for estimating the amplitude, frequency, and phase of a single sinusoidal signal from a measurement affected by structured and unstructured disturbances. The structured disturbances are modeled as a time-polynomial so as to represent bias and drift phenomena typically present in applications, whereas the unstructured disturbances are modelled as bounded noise signals. The proposed estimation technique exploits a specific adaptive observer scheme equipped with a switching criterion allowing to properly address in a stable way poor excitation scenarios. The estimator's stability properties are analyzed by input-to-state stability arguments. The practical characteristics of the proposed estimation approach are evaluated and compared with other existing tools by extensive simulation trials. Real experimental results are provided as well.