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The problem of designing event-triggered natural second-order state observers for second-order vector systems subject to external disturbances is considered in this paper. In this work, state observer is constructed using discrete measurements at instants when an event-triggered condition is satisfied. Due to the errors that arise by using the event-triggered mechanism and the external disturbance, the method in this paper only robustly estimates the state position and the state velocity of the second-order vector observers instead of estimating them asymptotically. Instead of using the method based on a parameter-dependent Lyapunov function in investigating the stability of the dynamic error system of natural second-order observers, a convex optimization problem is established in this paper to give an event-triggered mechanism and minimized levels in evaluating the boundedness of the dynamic error system of the event-triggered natural second-order observers. An automobile suspension and two numerical examples demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

In this paper we construct a general observer theory for differential delay systems based on different types of symmetries (exact symmetry or asymptotic symmetry), ending up with a certain number of semi‐global and global observers, with bounded or unbounded system's solutions. We introduce the notions of symmetry for a differential delay system, being inspired by well‐known definitions of symmetry for an ordinary or partial differential system, and variational symmetry for the associated variational differential delay system. We illustrate observer design procedures in details, by proving that the existence of a (variational asymptotic) symmetry with system's detectability in the first approximation have a central role in the design of a state observer. The symmetry is a one‐parameter group of transformations which maps the system into itself (exact symmetry) or into a different system (asymptotic symmetry), approximating the original one with better and better accuracy as the parameter of the symmetry is larger. The types of symmetries we consider here show an important contractive action on the state and input spaces for which the system's solutions are mapped into arbitrarily small neighbourhoods of the origin in which the transformed system can be well‐approximated by its linearization. The parameter of the symmetry may be constant (semiglobal observers) or updated on‐line by a state norm estimator (global observers). [ABSTRACT FROM AUTHOR]

During the hypersonic flight process of rigid hypersonic flight vehicles (HFVs), the flight-path angle and angle of attack are usually small and sensitive to flight condition variation, the accurate measurements are difficult, subsequently the formulation of states observers is an urgent issue. A distributed high-gain observer is proposed to reconstruct the admissible system state dynamics based on limited measured output. First, a distributed observer is proposed for the cascade strict-feedback system to ensure the global convergence of the state estimation errors between the estimated state in each local observer and system states, for the prescribed system initial values. Second, the distributed high-gain observer-based nonlinear control is investigated on the longitudinal dynamics of HFVs with partial measurable states. The rigid body system dynamic is divided into the altitude subsystem and the velocity subsystem. In the altitude subsystem, the proposed distributed high-gain observer is formulated to exact estimate the flight-path angle and angle of attack, and then the back-stepping design is proposed for constructing the controllers. The nonlinear dynamic inversion controller is introduced to accomplish the velocity tracking. Finally, simulation examples are served to verify that the proposed output feedback control possesses the superior properties of high tracking performance, fast convergence of state estimation error and easy-implementation. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of event-triggered robust state and fault simultaneous estimation for nonlinear time-delay systems subject to actuator and sensor unknown disturbances. Based on a fault decomposition technique and some basic mathematical transformations, we obtain an augmented system where the state vector consists of the variable of the original system and the fault. Then a novel event-triggered state observer for the augmented system is proposed to robustly estimate the variable of the original system and the fault. We next established a sufficient condition for the existence of such an observer. We translated it into a linear matrix inequality (LMI), which can be effectively solved using the MATLAB LMI Control Toolbox. Finally, an illustrative example is applied to test the proposed method. [ABSTRACT FROM AUTHOR]

This paper provides a procedure to design robust controllers based on observed states applied to three-phase inverters with LCL filters connected to a grid with uncertain and possibly time-varying impedances, which can arise in renewable energy systems and microgrid applications. Linear matrix inequalities are used to rapidly compute, off-line, based only on the choice of two scalar parameters for pole location, sets of gains for the controller and the observer, and also to provide a theoretical certificate of the closed-loop stability, including a limit for the rate of variations of the grid impedances. The proposed design procedure allows the easy implementation of robust state feedback controllers with a reduced number of sensors, ensuring good performance for different sets of grid impedances. Additionally, larger regions of guaranteed stability are provided by the proposed procedure, when compared with a similar condition from the literature. The control law using the observed states can ensure grid currents with low harmonic content, complying with the IEEE 1547 Standard requirements, with negligible loss of performance concerning the feedback of the measured state variables. Three optimal state feedback controllers from the literature are reproduced here and successfully implemented using the observed state variables based on the proposed procedure. In all cases, the viability of the proposal was confirmed by simulations and experimental results. [ABSTRACT FROM AUTHOR]

This paper investigates the state estimation problem for switched linear systems involving additive disturbance and measurement noise, whose modes need not to be cooperative. Under the assumption that there exist nonsingular matrices that transform the observer error system matrices into Metzler ones, by using multiple Lyapunov functions and average dwell time method, hybrid state observers, which can produce a framer covering all possible state trajectories, are designed in two cases, that is, all the subsystems are stable and some of the subsystems may be unstable. The constructed interval observer for the considered systems are hybrid dynamical systems and the initialized states of the upper and lower observers at each switching instants are coupled, since the transformation matrices for different subsystems are different. Compared with the robust observer design for switched systems with arbitrary switching sequences in the literature, the difficulty consists in proposing new observer design conditions related to the state reset at the switching instants for switched systems under average dwell time scheme. Finally, two examples are given to show the efficiency of the proposed approach. [ABSTRACT FROM AUTHOR]

Online monitoring of fermentation processes is a necessary task to determine concentrations of key biochemical compounds, diagnose faults in process operations, and implement feedback controllers. However, obtaining the signals of all-important variables in a real process is a task that may be difficult and expensive due to the lack of adequate sensors, or simply because some variables cannot be directly measured. From the above, a model-based approach such as state observers may be a viable alternative to solve the estimation problem. This work shows a comparative analysis of the real-time performance of a family of sliding-mode observers for reconstructing key variables in a batch bioreactor for fermentative ethanol production. These observers were selected for their robust performance under model uncertainties and finite-time estimation convergence. The selected sliding-mode observers were the first-order sliding mode observer, the proportional sliding mode observer, and the high-order sliding mode observer. For estimation purposes, a power law kinetic model for ethanol production by Saccharomyces cerevisiae was performed. A hybrid methodology allows the kinetic parameters to be adjusted, and an approach based on inference diagrams allows the observability of the model to be determined. The experimental results reported here show that the observers under analysis were robust to modeling errors and measurement noise. Moreover, the proportional sliding-mode observer was the algorithm that exhibited the best performance. [ABSTRACT FROM AUTHOR]

This article considers the problem of designing robust event-triggered functional observers for linear interconnected systems with disturbances. The considered system comprises $N$ interconnected subsystems where each subsystem is subject to unknown-but-bounded input disturbances that do not necessarily satisfy the observer matching condition. First, novel Zeno-free event-triggered mechanisms (ETMs) for $N$ subsystems of the considered interconnected system are proposed. Then, robust distributed functional observers are designed based on the proposed ETMs. The designed event-triggered functional observers are robust such that the observer error for each subsystem is $\epsilon _i$ -convergent. This ensures that the $i$ th estimated state vector of the $i$ th subsystem converges robustly within an $\epsilon _i$ -bound of the true state vector of the $i$ th subsystem. Numerical examples are given to illustrate the effectiveness of the proposed design method. [ABSTRACT FROM AUTHOR]

This article undertakes the problem of simultaneous estimation of state and process faults in linear parameter varying systems. For this purpose, a novel strategy that exploits recent results on the design of observers for quadratic parameter varying systems is developed, and a complete design procedure is described. First, it is shown that by treating the process faults as additional states to be estimated, the arising augmented state‐space model is indeed expressed as a quadratic parameter varying system. Hence, the estimates provided by a quadratic parameter varying observer based on the so‐called linear output error injection principle would comprise both the actual state and the process faults estimates. Robust design conditions that minimize the effect of disturbances and measurement noise on some linear, and possibly parameter‐varying, combination of error variables are obtained using a Lyapunov‐based approach. Then, it is shown that the design problem can be reduced to a finite set of linear matrix inequalities that can be solved using available computational tools. The final part of the article exhibits an illustrative example, which clearly exposes the potential applicability and performance of the developed approach. [ABSTRACT FROM AUTHOR]

This paper develops a theory of output feedback control for a class of nonlinear, nonstandard two-time-scale systems using a controller and a state observer with guaranteed closed-loop stability. Using insights from geometric singular perturbation theory, a sequential controller is designed over two time-scales. For different choices of measurements, Lyapunov-based observer designs are investigated. Both the controller and the observer are designed to guarantee Lyapunov stability of the lower-order reduced subsystems. Using an extension of the composite Lyapunov analysis, it is proved that the full-order nonlinear system with the controller and the observer remains globally asymptotically stable up to a bound of the time-scale separation parameter. In addition, the composite Lyapunov analysis yields sufficient conditions as guidelines to select the gains. The approach and analysis are demonstrated on a nonlinear two-time-scale system for which the reduced subsystems are linear, but the composite Lyapunov analysis handles the nonlinearity present in the full-order dynamics. [ABSTRACT FROM AUTHOR]

This article presents a new droop-based supervisory-level controller for nanogrids (NGs) in the islanded mode of operation. The proposed control system is able to significantly improve the dynamics of power electronic interfaces in NGs. Unlike the existing techniques, the proposed controller takes into account the input-side (i.e., source-side) and the output-side (i.e., load-side) parameters as well as the dc-bus voltage dynamics. Theoretical analysis, simulation results, and experimental results confirm the feasibility of the proposed control system and demonstrate its superior performance. [ABSTRACT FROM AUTHOR]

In this paper, a model reference control strategy is proposed in order to perform trajectory tracking in Takagi–Sugeno–Lipschitz (TSL) systems. Since the state vector is assumed not to be completely available for measurement, a proportional observer is added to the control scheme in order to apply an estimate‐feedback control action instead of a state‐feedback one. The overall design of both the controller and the observer gains are performed using a Lyapunov‐based quadratic boundedness specification, in order to improve the robustness against unknown exogenous disturbances. It is shown that the conditions that ensure convergence within ellipsoidal regions of the tracking and estimation errors can be expressed in the form of a linear matrix inequality (LMI) formulation. The effectiveness of the developed control strategy is demonstrated by means of simulation results. [ABSTRACT FROM AUTHOR]

Fluid temperature predictions of geothermal borefields usually involve temporal superposition of its characteristic g-function, using load aggregation schemes to reduce computational times. Assuming that the ground has linear properties, it can be modelled as a linear state-space system where the states are the aggregated loads. However, the application and accuracy of these models is compromised when the borefield is already operating and its load history is not registered or there are gaps in the data. This paper assesses the performance of state observers to estimate the borefield load history to obtain accurate fluid predictions. Results show that both Time-Varying Kalman Filter (TVKF) and Moving Horizon Estimator (MHE) provide predictions with average and maximum errors below 0.1 ∘ C and 1 ∘ C, respectively. MHE outperforms TVKF in terms of n-step ahead output predictions and load history profile estimates at the expense of about five times more computational time. [ABSTRACT FROM AUTHOR]

The design problem of an adaptive state observer for linear time-varying systems is considered. Some parameters of a time-varying plant are assumed to be unknown numbers multiplied by known functions of time. The approach proposed below is based on identification methods of adaptation. In other words, the main idea is to transform a mathematical model in the form of a linear time-varying differential equation to a static linear regression model containing unknown parameters. In this case, both the unknown parameters of the plant and its initial conditions are the unknown parameters of the regression model under consideration. Further, standard gradient methods or other parametric identification approaches are used to estimate the unknown parameters of the regression model and to construct the observer. The results of computer simulations illustrate the effectiveness of the new state observer design method. [ABSTRACT FROM AUTHOR]

This paper presents a new set-membership observer design method for discrete-time linear parameter varying systems. The real process is assumed to be perturbed by unknown but bounded disturbances. The proposed set-membership observer provides a deterministic state interval that is build as the sum of the estimated system states and its corresponding estimation errors bounds. The observer design process is based on the offline solution of a finite number of linear matrix inequalities conditions that provide both the observer parameters and the ellipsoidal robustly positive invariant (RPI) sets for the estimation error dynamics. The main feature of the proposed approach concerns the fact that these RPI sets are used to frame, at every time-instant, the estimation error in an explicit way. Another novelty concerns the fact that the approach includes uniformly distributed random disturbances which belongs to the family of bounded disturbances. The a posteriori steady-state covariance matrix of the estimation error dynamics, perturbed by such disturbances, is used during the observer synthesis providing small RPI sets. Two numerical examples illustrate the behaviour of such observer and its easy implementation. [ABSTRACT FROM AUTHOR]

In this article, a phase-locked loop (PLL) rotor position and speed observer, including an integrated decoupled stator current observer, is proposed for wide-speed-range sensorless control of permanent magnet synchronous motor (PMSM) drives. This new PLL scheme consists of two interconnected observers: 1) a second-order PLL position observer using the direct current error as correction term and 2) a third-order extended Luenberger speed observer, based on the mechanical model of the drive, using the quadrature current error as the correction term. The estimated speed is just an unrefined reference for the position integrator, and it is further corrected by the PLL. The current observer is a subsystem integrated as a PLL phase detector. Key relations between the current errors and the position and speed errors are proven, and a practical two-step observer design method is presented. A method for real-time identification of the stator resistance at low speeds and of the permanent magnet flux at high speeds is proposed, using the current errors in the current reference frame. Experimental results for a 0.5-kW surface-mounted PMSM drive confirm the effectiveness, excellent dynamic performance, and robustness of the new PLL observer. Stable motor and generator operation at 15 r/min (0.01 p.u.) with ramp and step speed reversals, high-speed operation, and stator resistance adaptation is shown. [ABSTRACT FROM AUTHOR]

This article proposes a speed observer algorithm for the sensorless control of a doubly fed induction generator based on classical adaptive backstepping technique. The sensorless control is shown using classic stator field oriented control, which is use to active and reactive power control. Performance of the proposed algorithm of a speed observer is validated by simulation and experimental results obtained using a small-rating generator and bidirectional voltage source converter. The stability analysis of the presented solution with regard to observer gains changes is also considered. [ABSTRACT FROM AUTHOR]

This paper deals with the fixed-time tracking control problem of extended nonholonomic chained-form systems with state observers. According to the structure characteristic of such chained-form systems, two subsystems are considered to design controllers, respectively. First of all, using the fixed-time control theory, a controller is proposed to make the first tracking error subsystem converge to zero in bounded time independent initial state. Second, a state observer is proposed to estimate the unmeasurable states of the second subsystem. And the precise state estimation can be presented from the observer within finite time; moreover, the upper bound of time is a constant independent on the initial estimation error. Third, a fixed-time controller is designed to drive all states of the second chained-form subsystem to zero within pre-calculated time. Finally, the effectiveness of the proposed control scheme is validated by simulation results. [ABSTRACT FROM AUTHOR]

This paper presents a new harmonic compensation system along with a hybrid load observer used for micro-grids in the islanded mode. The proposed control system includes an online hybrid load observer, which is able to precisely estimate the harmonic contents of the load as well as the equivalent load impedance in the islanded mode. One of the main features of the proposed control system is its capability to deal with unknown load harmonics, which is a big challenge in micro-grids. Basically, the proposed control system is able to detect, estimate, and compensate various load disturbances and harmonics (e.g., synchronous and asynchronous harmonics, and dc components). Theoretical analysis, simulation results, and experimental results verify the feasibility and performance of the proposed control system. [ABSTRACT FROM AUTHOR]

In this paper, we address problem of distributed functional interval observers for a class of nonlinear fractional-order interconnected systems with bounded uncertainties. The system under consideration is formed by N ( N ≥ 2) interconnected subsystems. We first design N stand-alone distributed functional interval observers for the system. The designed interval observer for each subsystem is based on the local outputs, known bounds of the local uncertainty and additional outputs from other subsystems but without the need to exchange the state estimates among the observers. Then, conditions for the existence of such distributed functional interval observers are derived and an effective algorithm for computing unknown observer matrices is provided. Finally, two numerical examples and simulation results are given to illustrate the effectiveness of the proposed design method. [ABSTRACT FROM AUTHOR]

This study is concerned with the output feedback stabilisation problem for a class of large‐scale non‐linear time‐delay systems with unknown measurement sensitivities. The non‐linear terms are assumed to be bounded by states or delayed states multiplied by outputs polynomial functions. The state observers are given by the dynamic gain control design technique. By constructing an appropriate Lyapunov–Krasovskii functional, the global asymptotic stability of the closed‐loop control system is analysed. A numerical example is given to illustrate the effectiveness of the proposed control scheme. [ABSTRACT FROM AUTHOR]

The knowledge of tire-ground interaction forces is valuable for intelligent vehicles technologies. However, tire forces transducers are expensive and not suitable for ordinary passenger cars. An alternative is to estimate these forces using less sophisticated sensors. This paper presents a new delayed interconnected cascade-observer structure to reconstruct the forces acting on each tire in all directions. The addition of delayed interconnections overcomes the mutual dependence problem in the cascade estimator. Observers are developed based on nonlinear vehicle dynamic models. The Extended Kalman Filter and the Unscented Kalman Filter algorithms are applied for comparison purposes. Two experimental data are used to validate the estimator: a driving in ordinary urban streets and a high-speed slalom maneuver in a banked road. The results are compared with force measurements obtained with transducers and with an existing estimator from the literature. [ABSTRACT FROM AUTHOR]

The synthesis of externally positive filters that can be used to modulate control signals of output‐feedback sliding mode controllers is presented. These filters are useful to estimate the norm of unmeasured signals in systems with parametric uncertainties. These norm observers are a robust alternative to state observers, that may be difficult to design for uncertain systems. The first step of the synthesis is the search of the envelope function, which is an upper bound for the norm of each admissible impulse response of the uncertain system. After this step, the approximation of a positive filter with an impulse response that is an upper bound for the envelope function is carried out. The search of the envelope function and the synthesis of the approximation filters are formulated as non‐convex optimisation problems that can be solved by genetic algorithms. It is important to design norm observers that are not too conservative, because the modulation signals should be kept small in order to reduce the amplitude of the control signals, power losses, and undesirable chattering effects that may occur in sliding‐mode control. The proposed methods are illustrated by numerical examples. [ABSTRACT FROM AUTHOR]

Summary: A fuzzy adaptive optimal output‐feedback control problem is investigated for nonlinear continuous‐time interconnected systems with saturation constraints. The system dynamics and the mismatched interconnections of each subsystem are unknown, and each subsystem contains unavailable states. Based on fuzzy logic systems, a fuzzy state observer is designed to approximate the unknown system states and the interconnections of the nonlinear interconnected system. By using adaptive dynamic programming technology, the decentralized optimal controller of each subsystem is given and the saturation control input is updated via the critic fuzzy logic systems. The proposed fuzzy adaptive observer‐based optimal control scheme can guarantee that the closed‐loop interconnected system is ultimately uniformly bounded stable. A simulation example validates the effectiveness of the presented scheme. [ABSTRACT FROM AUTHOR]

In this paper, we mainly study the complete moment convergence of double-indexed randomly weighted sums of negatively superadditive-dependent (NSD, for short) random variables, which is stronger than complete convergence. In addition, the convergence of double-indexed randomly weighted sums of NSD random variables is also obtained. As applications, we obtain the complete consistency for the randomly weighted estimator in a nonparametric regression model based on NSD errors, and study the almost sure convergence and mean square convergence of the state observers of linear-time-invariant systems. [ABSTRACT FROM AUTHOR]

This paper presents a fresh approach to the design of state observers for a class of time-delay systems with one time delay in the state and output vectors. By proposing a new coordinate state transformation, the system is first transformed into the new coordinates where all the delay terms associated with the state variables are injected into the system's output and input. Thus, in the new coordinate system, a Luenberger-type state observer can be easily designed. Then, a backward state transformation problem is studied which allows us to reconstruct the original state vector of the system. Conditions for the existence of the state transformations and an algorithm for computing them are provided in this paper. We show that our approach works for a wider class of time-delay systems in the sense that when some existing state observer design methods fail to reconstruct the state vector, the proposed new change of coordinates and the observer scheme in this paper can still reconstruct the original state vector. Numerical examples and simulation results are given to illustrative the effectiveness of the proposed design approach. [ABSTRACT FROM AUTHOR]

In this paper, we derive new state transformations of linear systems with a time-varying delay in the state vector. We first provide a new algebraic and systematic method for computing forward state transformations to transform time-delay systems into a novel form where time-varying delay appears in the input and output vectors, but not in the state vector. In the new coordinate system, a Luenberger-type state observer with a guaranteed β-exponential stability margin can be designed. Then, a backward state transformation problem which allows us to reconstruct the original state vector of the system is investigated. By using both the forward and the backward state transformations, state observers for time-varying delay systems can be systematically designed. Conditions for ensuring the existence of the forward and backward state transformations and an effective algorithm for computing them are given in this paper. We illustrate our results by three examples and simulation results. [ABSTRACT FROM AUTHOR]

This paper proposes a model-based state observer to perform high-definition response estimation in partially instrumented building structures. The proposed estimator is verified in a simulated five-story shear-building structure and validated using measurements from a seven-story reinforced concrete building slice tested at the NEES-University of California at San Diego shake table. In both cases the proposed estimator yielded satisfactory results by estimating the time history of shear forces, bending moments, displacements, and strains at various points/sections of interest. The proposed algorithm can be used in instrumented buildings for various practical applications such as post-earthquake damage assessment, structural control, and building code calibration. Copyright © 2016 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

This study presents a new configuration and a design method for the control of a switched neutral‐delay system. The equivalent‐input‐disturbance (EID) approach is used to construct an EID estimator to estimate the influence of exogenous disturbances on the system. A new control law is constructed that incorporates an EID estimate to ensure satisfactory disturbance rejection performance. A sufficient condition for uniformly ultimate boundedness is derived in term of linear matrix inequalities (LMIs) to guarantee the stability of the system. Moreover, state feedback control laws and state observers are designed based on the LMIs for arbitrary switching rules. A numerical example demonstrates the validity of the method. [ABSTRACT FROM AUTHOR]

We propose a new observer where the model, decomposed in generalized canonical form of regulation described by Fliess, is dissociated from the part assuring error correction. The obtained stable exact estimates give direct access to state variables in the form of successive derivatives. The dynamic response of the observer converges exponentially, as long as the nonlinearities are locally of Lipschitz type. In this case, we demonstrate that a quadratic Lyapunov function provides a number of inequalities which guarantee at least local stability. A synthesis of gains is proposed, independent of the observation time scale. Simulations of a Düffing system and a Lorenz strange attractor illustrate theoretical developments. [ABSTRACT FROM AUTHOR]

Nowadays a major factor of interest in industries in relationship to development of new techniques for detection and localization of faults it is the concern with the security of their systems. There is need for supervising and monitoring in order to detect and correct any fault as fast as possible. It is verified actually, that some determined parameters of the systems may vary during the process, due to the specific characteristics or the natural wearing of its components. It is known that even in well-designed systems the occurrence of cracks in some components can cause economic losses or lead to dangerous situations. With the help of the state observers methodology one can reconstruct the unmeasured states of the system, since that it is observable, becoming possible in this way to estimate the measures for locations of difficult access. The technique of state observers consists in developing a model for the system under analysis and comparing the estimate of outcome with the measured one, the difference between these two resulting in a residue that is used for analysis. In this work a bank of signals associated to a model of crack was assembled in order to follow its progress. The data acquired from the computational simulations in a cantilever beam discretized by means of the technique of finite elements, had been sufficiently satisfactory, validating the proposed methodology. [ABSTRACT FROM AUTHOR]

This paper presents a comparison between control/state estimation methods applied on Par2 parallel manipulator for pick-and-place applications as well as a discussion about the mechanical vibrations issue that may become important when reaching very high accelerations. Real-time experiments were performed first to compare two controllers (a linear Proportional-Derivative controller and a nonlinear/adaptive Dual Mode (DM) controller) complied with the same High-Gain Observer (HGO) to estimate articular velocities, and second to compare three state observers (a Lead-lag-based, an Alpha-beta-gamma (ABG) and an HGO) complied with the same nonlinear DM controller. The stability analysis of the Par2 robot under the control of the proposed DM controller (complied with the HighGO for joint velocity estimation) is also provided. Some small mechanical vibrations were noted when reaching 20 G acceleration, which means that it can become an important issue for higher accelerations. Some suggestions are then made for future investigations to avoid/damp these vibrations. [ABSTRACT FROM PUBLISHER]

This paper presents a load frequency control scheme using electric vehicles (EVs) to help thermal turbine units to provide the stability fluctuated by load demands. First, a general framework for deriving a state-space model for general power system topologies is given. Then, a detailed model of a four-area power system incorporating a smart and renewable discharged EVs system is presented. The areas within the system are interconnected via a combination of alternating current/high voltage direct current links and thyristor controlled phase shifters. Based on some recent development on functional observers, novel distributed functional observers are designed, one at each local area, to implement any given global state feedback controller. The designed observers are of reduced order and dynamically decoupled from others in contrast to conventional centralized observer (CO)-based controllers. The proposed scheme can cope better against accidental failures than those CO-based controllers. Extensive simulations and comparisons are given to show the effectiveness of the proposed control scheme. [ABSTRACT FROM PUBLISHER]

By enabling the estimation of difficult-to-measure target variables using available indirect measurements, mechanistic soft sensors have become important tools for various bioprocess monitoring and control scenarios. Despite promising higher process efficiencies and increased process understanding, widespread application of soft sensors has been stalled by uncertainty about the feasibility and reliability of their estimations given present process analytical constraints. Observability analysis can provide an indication of the possibility and reliability of soft sensor estimations by analyzing the structural properties of first-principle (mechanistic) models. In addition, it can provide a criteria for selection of suitable measurement methods with respect to their information content; thereby leading to successful implementation of soft sensors in bioprocess development and manufacturing environments. We demonstrate the utility of observability analysis for two classes of upstream bioprocesses: the processes involving growth and ethanol formation by Saccharomyces cerevisiae and the process of penicillin production by Penicillium chrysogenum. Results obtained from laboratory-scale cultivations in addition to in-silico experiments enable a comparison of theoretical aspects of observability analysis and the real-life performance of soft sensors. By taking the expected error of measurements provided to the soft sensor into account, an innovative scaling approach facilitates a higher degree of comparability of observability results among various measurement configurations and process conditions. [ABSTRACT FROM AUTHOR]

This article addresses the problem of robustly estimating the dynamic state of a mechanism from a set of noisy sensor measurements. We start with a rigorous treatment of the problem from the perspective of graphical models, a popular formalism in the fields of statistical inference and machine learning. The modeling power of such a formalism is demonstrated by showing how the sequential estimation of a mechanism state with an extended Kalman filter (EKF), often used in previous works, becomes just one of the possible solutions. As an interesting alternative, we derive the formulation of a sequential Monte Carlo (SMC) filter, also known as a particle filter (PF), suitable for online tracking the state of a rigid mechanism. We validate our ideas with both simulated and real datasets. Moreover, we prove the usefulness of the particle filtering solution for real-work applications due to its unmatched capability of automatically inferring the initial states of the mechanism along with its 'assembly configuration' or 'branch' if several ones are possible, a feature not matched by any previously proposed state observer in the multibody literature. [ABSTRACT FROM AUTHOR]

The development of efficient and reliable fault detection approaches is necessary to improve performance, safety, and reliability in engineering systems. Moreover, these approaches have to be simple enough to provide quick diagnosis results and to reduce development and maintenance costs. Consistency-based diagnosis using possible conflicts (PCs) relies upon the simulation of numerical models to provide a simple and efficient fault diagnosis approach. However, simulation approaches need to know the initial state, and this assumption is not easily fulfilled in real systems, even in the presence of measurements related to state variables due to noise and parameter uncertainties. In this paper, we develop an approach where PCs are used to automatically compute structural models which can be implemented as simulation and state observer models. Using these models, we propose a framework which integrates those state observers to estimate the initial states for simulation within the consistency-based diagnosis framework. Then, both the simulation models and the state observers are used to provide quick detection decisions without increasing the complexity of the diagnoser. Our integration proposal is open to different kinds of state observers, except for the structural model, and different fault detection configurations. The proposal has been tested on a thermohydraulic reconfigurable laboratory plant using real data with satisfactory results. [ABSTRACT FROM AUTHOR]

Alternating current (ac) active electronic loads (AELs) based on pulsewidth modulation (PWM) converters, capable of emulating nonlinear loads, require effective current control techniques and power stages. These two aspects of an AEL are interdependent mainly due to the presence of electromagnetic compatibility filters to reduce electromagnetic emissions. However, the filter components affect the dynamics of the system. In this context, this paper proposes a digitally implemented control strategy comprising the state feedback, robust observer, phase-locked loop, and linear current controller to cope with ac AEL applications. The control-oriented modeling and the control system design are presented. Experimental results obtained from a 10-kW three-phase three-level PWM converter prototype illustrate and demonstrate the feasibility of the proposed AEL control strategy. [ABSTRACT FROM PUBLISHER]

This paper presents a method for designing state observers with exponential error decay for nonlinear systems whose output measurements are affected by known time-varying delays. A modular approach is followed, where subobservers are connected in cascade to achieve a desired exponential convergence rate (chain observer). When the delay is small, a single-step observer is sufficient to carry out the goal. Two or more subobservers are needed in the the presence of large delays. The observer employs delay-dependent time-varying gains to achieve the desired exponential error decay. The proposed approach allows to deal with vector output measurements, where each output component can be affected by a different delay. Relationships among the error decay rate, the bound on the measurement delays, the observer gains, and the Lipschitz constants of the system are presented. The method is illustrated on the synchronization problem of continuous-time hyperchaotic systems with buffered measurements. [ABSTRACT FROM AUTHOR]

ABSTRACT On-line estimation of biomass concentration in batch biotechnological processes is an active area of research because normally, the biomass is the desired process product output, and also because it is necessary for control purposes to replace the unavailable biomass concentration measurements with reliable and robust on-line estimations. This work presents five different alternatives to face the problem of biomass estimation in a particular batch bioprocess (δ-endotoxins production of Bacillus thuringiensis), namely: a phenomenological estimator based on dissolved oxygen balance, an extended Kalman filter estimator, a Gaussian process regression-based estimator, an artificial neural networks-based estimator, and finally, an estimator based on information fusion by a decentralized Kalman filter. Each proposed biomass estimation method has its own advantages and drawbacks according to their ability to take into account the model uncertainties and the measurement errors. First, the design techniques of these five biomass estimators are exposed, and finally, the behavior of each estimation method is compared. The availability of efficient biomass estimators is of great importance for engineers because, on the one hand, it allows developing new control strategies for other bioprocess variables such as for instance: the growth rate of the microorganism, the dissolved oxygen concentration, and so on. On the other hand, it is also important to improve the performance of the bioprocess optimization procedure. This work also aims to show the evolution on biomass estimation techniques from classical to more contemporary approaches, such as the design based on neural networks and Gaussian processes regression. © 2013 Curtin University of Technology and John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

We propose a new type of Proportional Integral (PI) state observer for a class of nonlinear systems in continuous time which ensures an asymptotic stable convergence of the state estimates. Approximations of nonlinearity are not necessary to obtain such results, but the functions must be, at least locally, of the Lipschitz type. The obtained state variables are exact and robust against noise. Naslin's damping criterion permits synthesizing gains in an algebraically simple and efficient way. Both the speed and damping of the observer response are controlled in this way. Model simulations based on a Sprott strange attractor are discussed as an example. [ABSTRACT FROM AUTHOR]

In this paper a straightforward procedure, to obtain a linear functional observer with order equal to the dimension of the vector to be estimated, is proposed. Inspired by [3], we have developed a procedure to construct this type of observer in which some significant simplifications have been made and allow computational burden reduction. The procedure developed in this work has the originality and the great advantage that all required matrices are obtained without calculating any generalized inverse. A novel approach which constitutes the coupling of a coordinates transformation and the use of the {1}-inverse property requires only the inversion of a nonsingular matrix. A procedure where all steps are clearly identified is written and a numerical example is provided to illustrate the effectiveness of the observer. [ABSTRACT FROM AUTHOR]

Model predictive control is a new alternative in the control of power converters. The practical application of this controller demands a correct compensation of the computation delay. This compensation requires an estimation of the load current, usually attained in an open-loop manner, i. e. without error feedback of the model parameters. However, this strategy gives rise to diverse perturbations in the waveform, when there is a certain amount of parameter variation. In this work, the effects of this variation are analyzed and a way to reduce the distortion is proposed, using a Luenberger observer. This new approach is called Robust Model Predictive Control. The proposal is applied to a grid-connected voltage-source inverter. To validate the proposal, simulations are included in the paper. [ABSTRACT FROM PUBLISHER]

Accurate estimation of the state of charge in battery systems is of essential importance for battery system management. Due to nonlinearity, high sensitivity of the inverse mapping from external measurements, and measurement errors, SOC estimation has remained a challenging task. This is further compounded by the fact that battery characteristic model parameters change with time and operating conditions. This paper introduces an adaptive nonlinear observer design that compensates nonlinearity and achieves better estimation accuracy. A two-time-scale signal processing method is employed to attenuate the effects of measurement noises on SOC estimates. The results are further expanded to derive an integrated algorithm to identify model parameters and initial SOC jointly. Simulations were performed to illustrate the capability and utility of the algorithms. Experimental verifications are conducted on Li-ion battery packs of different capacities under different load profiles. [ABSTRACT FROM AUTHOR]

The principal concerns in driving safety with standard vehicles or cybercars are understanding and preventing risky situations. A close examination of accident data reveals that losing control of the vehicle is the main reason for most car accidents. To help to prevent such accidents, vehicle-control systems may be used, which require certain input data concerning vehicle-dynamic parameters and vehicle–road interaction. Unfortunately, some fundamental parameters, like tire–road forces and sideslip angle are difficult to measure in a car, for both technical and economic reasons. Therefore, this study presents a dynamic modeling and observation method to estimate these variables. One of the major contributions of this study, with respect to our previous work and to the largest literature in the field of the lateral dynamic estimation, is the fact that lateral tire force at each wheel is discussed in details. To address system nonlinearities and unmodeled dynamics, two observers derived from extended and unscented Kalman filtering techniques are proposed and compared. The estimation process method is based on the dynamic response of a vehicle instrumented with available and potentially integrable sensors. Performances are tested using an experimental car. Experimental results demonstrate the ability of this approach to provide accurate estimations, and show its practical potential as a low-cost solution for calculating lateral tire forces and sideslip angle. [ABSTRACT FROM PUBLISHER]