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In this article, a fault estimation method is proposed for a class of nonlinear distributed systems, in which the adaptive observer technique is exploited to estimate the parameters related to the fault in the interconnection items. First, we consider the simple case that the interconnection items only contain measurable variables. And then the result will be extended to the case that the interconnection items contain unmeasurable variables. Due to the existing of nonlinear item, the state and parameter estimations cannot be decoupled from each other, which makes the analysis of the convergence of the state and parameter estimations much more difficult. To overcome this problem, by designing of an integral Lyapunov function, and by exploiting the converse theorem of Lyapunov, a sufficient condition for the exponential convergence of the proposed algorithm is derived. Moreover, the detail relationship between the observer gain, Lipschitz parameter of the nonlinear item and the PE level of the estimation system is also derived. Simulation results show the effectiveness of the proposed fault identification scheme. [ABSTRACT FROM AUTHOR]

Controlling variable wind speed turbine (VWT) system based on a doubly-fed induction generator (DFIG) is a challenging task. It requires a control law that is both adaptable and robust enough to handle the complex dynamics of the closed control loop system. Sliding mode control (SMC) is a robust control technology that has shown good performance when employed as a passive fault-tolerant control for wind energy systems. To improve the closed control loop of VWT based on DFIG with the aim of improving energy efficiency, even in presence of nonlinearities and a certain range of bounded parametric uncertainties, whether electrically or mechanically, an adaptive passive fault tolerant control (AP-FTC) based on a self-tuning fractional integral sliding mode control law (ST-FISMC) developed from a novel hyperbolic fractional surface is proposed in this paper. ST-FISMC introduces a nonlinear hyperbolic function into the sliding manifold for self-tuning adaptation of control law, while fractional integral of the control law smooths discontinuous sign function to reduce chattering. Additionally, this work introduces an adaptive observer, developed and proved based on a chosen Lyapunov function. This observer is designed to estimate variations in electrical parameters and stator flux, ensuring sensorless decoupling in indirect field-oriented control (SI-FOC) of DFIG. Lyapunov theory is also used to prove stability of states vectors in closed control loop with presence of bounded parameters uncertainties or external disturbances. Simulation results show that the proposed approach offers better performance in capturing optimal wind energy, as well as the ability to regulate active/reactive power and high resilience in presence of occurring parameter uncertainties or external disturbances. [ABSTRACT FROM AUTHOR]

This paper is devoted to the observer-based adaptive robust control for fractional-order Takagi–Sugeno (T-S) fuzzy systems with input uncertainties and output disturbances. By combining system states and output perturbations as new state variables, an augmented fuzzy system whose state variables are unknown is built. Furthermore, an observer is devised to simultaneously estimate unmeasurable system states together with unknown external disturbances. Two stability theorems are derived to prove the asymptotic stability of the error system based on linear matrix inequalities and Lyapunov stability theory. Finally, simulation results are provided to demonstrate the effectiveness of the designed method. [ABSTRACT FROM AUTHOR]

A H ∞ robust adaptive nonlinear observer for state and parameter estimation of a class of Lipschitz nonlinear systems with disturbances is presented in this work. The objective is to estimate parameters and monitor the performance of nonlinear processes with model uncertainties. The behavior of the observer in the presence of disturbances is analyzed using Lyapunov stability theory and by considering an H ∞ performance criterion. Numerical simulations were carried out to demonstrate the applicability of this observer for a semi-active car suspension. The adaptive observer performed well in estimating the tire rigidity (as an unknown parameter) and induced disturbances representing damage to the damper. The main contribution is the proposal of an alternative methodology for simultaneous parameter and actuator disturbance estimation for a more general class of nonlinear systems. [ABSTRACT FROM AUTHOR]

Fault data injection attacks may lead to a decrease in system performance and even a malfunction in system operation for an automatic feedback control system, which has motive to develop an effective method for rapidly detecting such attacks so that appropriate measures can be taken correspondingly. In this study, a secure descriptor estimation technique is proposed for continuous-time Lipschitz nonlinear cyber physical systems affected by actuator attacks, sensor attacks, and unknown process uncertainties. Specifically, by forming a new state vector composed of original system states and sensor faults, an equivalent descriptor dynamic system is built. A proportional and derivate sliding-mode observer is presented so that the system states, sensor attack, and actuator attack can be reconstructed successfully. The observer gains are obtained by using linear matrix inequality to secure robustly stable estimation error dynamics. Moreover, a robust descriptor fast adaptive observer estimator is presented as a complement. Finally, the efficacy levels of the proposed design approaches are validated using a vertical take-off and landing aircraft system. Comparison studies are also carried out to assess the tracking performances of the proposed algorithms. [ABSTRACT FROM AUTHOR]

This study addresses the antivibration issue of a full aircraft active landing gear system (LGS) under landing impact and runway excitations via a novel adaptive observer-based integral event-triggered control method. First, a full aircraft active LGS comprising a front gear and two synchronized main left and right gears with active suspensions is established, where the information exchange among them is not implemented directly. Second, two adaptive observers are designed separately for the front and main LGSs to identify the varying damping parameters and estimate the system states simultaneously. Third, estimated states are transmitted to a shared network through a novel integral event-triggered mechanism (I-ETM); thus, the front and main LGSs are interconnected, and a networked control scheme for the aircraft LGS is constructed. Additionally, the integrated active control law is designed with the aid of the transmitted states of the front and main LGSs, enhancing the robustness against the pitch vibration of the aircraft. Furthermore, the L 2 -stability of the hybrid system is guaranteed to depress the vertical vibration effect of the landing impact and irregular runway excitations, thus improving passenger safety and comfort. Moreover, a codesign method is provided to obtain feasible solutions to the gain matrices of the observers and controllers simultaneously. Simulation and comparison results are presented to illustrate the effectiveness and superiority of the proposed control scheme in antivibration and saving communication resources. [ABSTRACT FROM AUTHOR]

Summary: In this paper, an adaptive observer based output feedback prescribed‐time control is designed for a class of nonlinear systems with unknown parameters. According to the characteristics of the nonlinear systems, a novel adaptive observer is first designed. Then by imposing parametric Lyapunov equations, a time‐varying high‐gain observer based output feedback adaptive controller is constructed, by which, it is proven that the states of both the observer and the original system can be driven to zero within any prescribed time. Moreover, the control signal is proven uniformly bounded by a scalar. Two numerical examples are illustrated to verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

A novel scheme of fault‐tolerant control (FTC) and fault estimation (FE) for the stochastic distribution control (SDC) system with disturbance is investigated. The output probability density function (PDF) is modelled by the weight vector of the B‐spline neural network with the given basis function, and control of output probability density function can be converted to control of the weight vector of the B‐spline neural network. A k‐step fault estimation algorithm aiming at enhancing the accuracy of fault estimation is first applied to the stochastic distribution control system. Subsequently, a dynamic output feedback fault‐tolerant control algorithm is constructed by using the k‐step fault estimation information to guarantee that the target weight vector can be still approximated by the output weight vector in case of actuator fault. A simulation example is used to confirm the availability of the presented method. [ABSTRACT FROM AUTHOR]

This study addresses the issue of integrating renewable energy sources into the power grid by proposing advanced power management strategies based on an adaptive observer. The system comprises a wind turbine farm (WTF) connected to an energy storage system via a Vienna rectifier, which serves as the voltage source converter (VSC), and is linked to the DC grid through a high-voltage direct current (HVDC) transmission line. The main objective of this study is to design a nonlinear controller to improve energy extraction from the wind power frame and enhance the stability of the power grid. The system controller is designed to operate in two modes. The main feature of this controller is its ability to achieve multi-objective control using different references. In this study, we have four references but only two control laws. Furthermore, an adaptive observer is designed to estimate the power grid requirements, allowing us to produce depending on the grid state. Finally, simulation results using MATLAB/Simulink will be displayed to demonstrate the stability and efficiency of the proposed implementation.

For simple-input and simple-output (SISO) discrete-time nonlinear systems, an observer-based event-triggered model-free adaptive sliding mode predictive control technique (EMFASPC) is put forth in this study. The estimate of pseudo partial derivatives (PPD) and the transmission of I/O data are both carried out periodically at the time of event triggering to conserve network resources. A unified framework of event-triggered modelfree adaptive control with an adaptive observer and an event-triggered PPD estimation method is constructed based on the equivalent data model after compact format dynamic linearization (CFDL). The controller part adopts integral sliding mode control (SMC) combined with a rolling optimization idea of model predictive control (MPC) to predict the expected trajectory of the sliding mode state and generate the optimal control input. According to the relationship among the system tracking error, current measurement data, and the previous trigger time output, the event trigger condition is set to determine the next event trigger time, which reduces the unnecessary transmission on the premise of system stability. The stability performance of the closed-loop system is analyzed by the Lyapunov method. Finally, numerical simulation and the shell-and-tube heat exchanger control system simulation are carried out to verify that the proposed algorithm has good robustness and tracking accuracy under the limited bandwidth and computing resources.

Summary: We are considering the problem of designing observers for heat partial differential equations (PDEs) that are subject to sensor delay and parameter uncertainty. In order to get finite‐dimensional observers, described by ordinary differential equations (ODE), we develop a design method based on the modal decomposition approach. The approach is extended so that both parameter uncertainty and sensor delay effects are compensated for. To cope more effectively with sensor delay, an output predictor is designed and the online provided output predictions are substituted to the future output values in the observer. To compensate for parameter uncertainty, we design a parameter estimator providing online parameter estimates, which are substituted to the unknown parameters in the observer. The parameter estimator design is made decoupled from the observer gain design by using an appropriate decoupling transformation. Using an analysis of the small‐gain‐theorem type, the whole (state and parameter) estimation error system is shown to be exponentially stable, under well‐defined conditions on the observer dimension, the sensor delay, and signal persistent excitation (PE). [ABSTRACT FROM AUTHOR]

In this article, a non-fragile adaptive fuzzy observer is proposed for nonlinear systems with uncertain external disturbance and measurement noise. Firstly, the nonlinear system is augmented by an output filtered transformation. The output with measurement disturbance is put into the state equation of the augment system. Then, we introduce fuzzy logic system (FLS) to approximate the measurement disturbance, and construct an augmented non-fragile adaptive fuzzy observer for the augment system. A Lyapunov function is constructed to reveal that the characteristic of estimation errors is uniformly ultimately boundedness (UUB). Finally, two experimental simulations are offered to confirm the validity of the proposed design method. [ABSTRACT FROM AUTHOR]

In this paper, we present the definitions of nonfragile high‐gain observers and design method for lower‐triangular nonlinear systems with output uncertainty. Radial basis function neural networks (RBFNNs) are used to approximate the output uncertainty. By inserting an output filter and an input‐output filter, a new augmented adaptive observable canonical form is derived. Then, a corresponding observer with gain perturbations is designed to estimate the states and the coefficients of the RBFNNs, and a disturbance observer is designed to estimate the approximation error. The maximum allowable gain perturbation is also given. Then, the obtained results are extended to nonlinear systems in adaptive observer form with output uncertainty. Finally, some numerical simulations are offered to corroborate the theoretical results. [ABSTRACT FROM AUTHOR]

This study proposes a non-fragile fault-tolerant control design based on adaptive robust observer for a type of Markov-type active suspension system with sensor and actuator faults under the external road disturbance. First, the faulty active suspension system is reconfigured into an augmented system by extending the sensor fault as part of the system state vector. Then, an appropriate adaptive observer is designed to estimate the sensor fault, the actuator fault, as well as the active suspension system states simultaneously. Afterwards, an expected non-fragile fault-tolerant control scheme based on state feedback technology is presented to asymptotically stabilize the vertical and pitch motions of the faulty active suspension system. Benefit from its non-fragility, the proposed controller has been proven to be highly stable against its internal gain perturbation. Finally, the comparative simulation results of a half-vehicle active suspension system are provided to validate the effectiveness of the proposed fault-tolerant controller. It is shown that the designed adaptive observer can precisely estimate the sensor and actuator faults, together with the active suspension system states, and the designed non-fragile fault-tolerant control controller can effectively compensate the performance loss of the faulty active suspension system and improve the reliability of the vehicle active suspension system. [ABSTRACT FROM AUTHOR]

In this paper, a novel model-based fault detection in the battery management system of an electric vehicle is proposed. Two adaptive observers are designed to detect state-of-charge faults and voltage sensor faults, considering the impact of battery aging. Battery aging primarily affects capacity and resistance, becoming more pronounced in the later stages of a battery lifespan. By incorporating aging effects into our fault diagnosis scheme, our proposed approach prevents false or missed alarms for the aged battery cells. The aging effect of battery, capacity fading and resistance growth, are considered unknown parameters. An adaptive observer is employed to design a fault detector, considering unknown parameters in the battery model. The adaptive observers are designed for two different scenarios: In the first scenario, it is presumed that aging effects remain constant over time due to their slow rate of change. Then, it is assumed that aging effects are time-varying. Therefore, the fault detection scheme can detect faults of new battery cells as well as aged cells. Some simulations have been conducted on a Lithium-ion battery cell and extended to battery pack, to demonstrate the performance of the proposed approach in more real-world scenarios. The results showed that the designed observers can detect faults correctly in a seven years old battery as well as a new one.

Induction motors (IMs) experience power losses when a portion of the input power is converted to heat instead of driving the load. The combined effect of copper losses, core losses, and mechanical losses results in IM power losses. Unfortunately, the core losses in the motor, which have a considerable impact on its energy efficiency, are not taken into account by the generally employed dynamic model in the majority of the studies. Due to this, the motor rating often corresponds to the worst-case load in applications, but the motor frequently operates below rated conditions. A hybridized model reference adaptive system (MRAS) with sliding mode control (SMC) is used in this study for sensorless speed control of an induction motor with core loss, allowing the motor to operate under a variety of load conditions. As a result, the machine can run at maximum efficiency while carrying its rated load. By adjusting the ��-axis current in the �� - �� reference frame in vector-controlled drives, the system’s performance is enhanced by running the motor at its optimum flux. Regarding the torque and speed of both induction motors with and without core loss, the Adaptive Observer Sliding Mode Control (AOSMC) has been constructed and simulated in this case. The AOSMC with core loss produced good performance when the proposed controller was tested.

The traditional manual masonry technology of building wall has the problems of low construction efficiency and poor safety. The research of automatic masonry robot has become an urgent need. Harmonic reducer is the key component to ensure the construction robot to achieve good quality construction. This paper takes the "flexible assembly" of harmonic reducer as the research object. The system dynamics model is simplified and the reducer fault mathematical model is established. An adaptive state observer with greater design freedom is designed based on the fault model. This paper introduces adaptive threshold and performs parameter optimization. When no fault occurs in the system, the distance between the adaptive threshold and the residual is small and higher than the residual. When the system fails, the residual is higher than the adaptive threshold. This method solves the fault leakage alarm of the traditional detection method with a fixed threshold. The effectiveness of the fault detection scheme under constant fault and time‐varying fault is verified by simulation. The results show that the adaptive threshold method can effectively reduce the false alarm whether the reducer has a constant fault or a time‐varying fault, which shows that the fault detection scheme designed in this paper is effective. [ABSTRACT FROM AUTHOR]

In this study, we delve into the intricacies of addressing the challenge posed by simultaneous external disturbances and ever-changing actuator and sensor faults in the context of linear parameter-varying (LPV) systems. Our focus is on fault estimation (FE) and the pursuit of fault-tolerant tracking control (FTTC). LPV systems are described through a polytopic LPV representation with measurable gain scheduling functions. An adaptive LPV sliding mode observer (ASMO) is developed for the purpose of simultaneously estimating the system states and faults despite external disturbances. Compared with other conventional ASMO designs, the proposed observer has the capability to reconstruct the actuator faults by exploiting the equivalent output error injection signal required to maintain sliding motion and to directly estimate sensor faults using an adaptive algorithm. Based on online FE information, an FTTC is synthesized to compensate for the fault effect and to force closed-loop system states to track their desired reference signals. Sufficient conditions to ensure the stability of the state estimation errors and closed-loop system are established using Lyapunov stability theory together with H ∞ techniques. These requirements are articulated using linear matrix inequalities (LMIs), which can be effortlessly addressed through optimization problem-solving methods. To illustrate the potency of the proposed approaches, an illustrative example is provided. To illustrate the potency of the proposed approaches and to validate their practical effectiveness, we offer an illustrative example featuring a vertical takeoff and landing aircraft. This real-world case study serves as a practical application of our theoretical contributions, demonstrating the adaptability and robustness of our approach in the face of complex, real-world challenges. [ABSTRACT FROM AUTHOR]

The objective of this paper is to propose a novel self-learning observer for effectively estimating unmeasured states of a dynamical information-poor system. By inspiring the success of convolutional neural network (CNN) in image and voice recognition applications, the dynamical convolutional neural network (DCNN) architecture (which is nonlinear-in-parameter unlike traditional basis functions–based neural network or fuzzy logic architectures) is proposed for approximating the unknown system dynamics, uncertainties, and disturbances. Instead of using an output observation error filtering method, complex weight-updating mechanism, strictly positive real (SPR) assumption, offline-tuning mechanisms, static approximation of gradients in back-propagation algorithm, or any other strong or strict requirement, the simple yet effective and novel rules for tuning DCNN weights and kernels are derived. Furthermore, the problems such as parameters over-tuning and unnecessary consumption of large amounts of computational power commonly found in traditional neuro-adaptive and fuzzy-adaptive controllers/observers are addressed and their solution is incorporated in the design of the observer. Thus, the proposed DCNN-based adaptive observer is energy-efficient. A quadratic function based on the convolution operator is also proposed which is utilized to provide the closed-loop stability analysis using Lyapunov's approach. An example is simulated to demonstrate the performance of the proposed observer. [ABSTRACT FROM AUTHOR]

Summary: In this paper, an enclosing control problem is investigated for nonholonomic mobile agents with a moving target of unknown velocity. An adaptive observer containing two internal variables is first designed for each agent to compensate for the lack of the target velocity information. One variable is designed to estimate the unknown target velocity and further its estimation error is assessed by the other internal variable to subsequently guarantee the control performance. Then using the estimated information from the adaptive observer, a dynamic control law for circular formation of nonholonomic agents around the moving target is designed by a backstepping process. The global asymptotical stability of the closed‐loop system is achieved under the proposed dynamic control law with the adaptive observer. Finally, a simulation is conducted to demonstrate the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

The problem of state reconstruction is considered for a class of linear systems with time-invariant unknown parameters and overparameterization that are affected by external perturbations generated by a known exosystem with unknown initial conditions. An extended adaptive observer is proposed, which, in contrast to existing approaches, solves state and perturbation adaptive estimation problems for systems that are not represented in the observer canonical form. The obtained theoretical results are validated via mathematical modeling. [ABSTRACT FROM AUTHOR]

This paper studies the quantized output data observer based data-driven model-free adaptive control(qMFAC) for discrete-time nonlinear systems with unknown structures and network transmission constraints. First, an adaptive observer based on quantized output data is generated with the use of a logarithmic quantizer, and a pseudo-biased derivative(PPD) estimation scheme based on the output quantized data observer is proposed. By dynamic linearization(DL) techniques, a incomplete equivalent data model containing quantized output data are built. Then, the observer output is used to develop a data-driven model-free adaptive control strategy that only makes use of quantified output and input. With the Lyapunov function and sector boundary approaches, the bounded tracking performance of the proposed qMFAC is strictly theoretical analyzed, and the effectiveness of qMFAC is verified through numerical simulation and simulation experiments of the shell and tube heat exchanger control system.

A H∞ robust adaptive nonlinear observer for state and parameter estimation of a class of Lipschitz nonlinear systems with disturbances is presented in this work. The objective is to estimate parameters and monitor the performance of nonlinear processes with model uncertainties. The behavior of the observer in the presence of disturbances is analyzed using Lyapunov stability theory and by considering an H∞ performance criterion. Numerical simulations were carried out to demonstrate the applicability of this observer for a semi-active car suspension. The adaptive observer performed well in estimating the tire rigidity (as an unknown parameter) and induced disturbances representing damage to the damper. The main contribution is the proposal of an alternative methodology for simultaneous parameter and actuator disturbance estimation for a more general class of nonlinear systems.

This paper presents a new approach of fault-tolerant control (FTC) for the transmission line as a neutral variable time-delay system. The main goal of this work guarantees faulty neutral variable time delay system stabilization using the state feedback control design based on Lyapunov function and the Linear Matrix Inequality resolution. The use of the FTC method is to achieve actuator and sensor fault compensation. This method is based on two steps. The first one is the synthesis of a nominal control, which remains to maintain the closed-loop system stability. The second step is based on adding a new control law to the nominal one to compensate the fault effect on system behaviour and maintain the desired performance in the closed loop system. Then, a conception of an adaptive observer is used to detect and estimate the fault. Finally, the developed approach is applied for the transmission line. The given results are presented to prove the effectiveness of this approach.

Induction motors (IMs) experience power losses when a portion of the input power is converted to heat instead of driving the load. The combined effect of copper losses, core losses, and mechanical losses results in IM power losses. Unfortunately, the core losses in the motor, which have a considerable impact on its energy efficiency, are not taken into account by the generally employed dynamic model in the majority of the studies. Due to this, the motor rating often corresponds to the worst-case load in applications, but the motor frequently operates below rated conditions. A hybridized model reference adaptive system (MRAS) with sliding mode control (SMC) is used in this study for sensorless speed control of an induction motor with core loss, allowing the motor to operate under a variety of load conditions. As a result, the machine can run at maximum efficiency while carrying its rated load. By adjusting the α-axis current in the α - β reference frame in vector-controlled drives, the system's performance is enhanced by running the motor at its optimum flux. Regarding the torque and speed of both induction motors with and without core loss, the Adaptive Observer Sliding Mode Control (AOSMC) has been constructed and simulated in this case. The AOSMC with core loss produced good performance when the proposed controller was tested. [ABSTRACT FROM AUTHOR]

In this article, state and parameter estimation problems are investigated for ODE-PDE coupled systems, for which the parabolic PDE sensor includes nonlinear dynamics and parameter uncertainty. The major difficulty we face is that the link point among the ODE part and the PDE part is not convenient to measurable. For this reason, the objective of this paper is to build an adaptive observer to provide online estimates of states and unknown parameters on the basis that only boundary state is available for measurement. First of all, the observer error system is converted to a tractable target error system by applying the decoupling transformation. Then, the least-squares parameter adaptive law is built. Under an ad hoc persistent excitation condition, the exponentially decaying of the observer error system is demonstrated by applying Lyapunov–Krasovskii functional. Finally, the effectiveness of the theoretical results is confirmed by a simulation example. [ABSTRACT FROM AUTHOR]

Summary: This article proposes a virtual angle‐based adaptive control method for trajectory tracking and balancing of ball‐balancing robots without velocity measurements. The trajectory tracking and balancing control of ball‐balancing robots is challenging due to underactuation and uncertain nonlinearities. The hierarchical control strategy, which designs the control system using a linear combination of trajectory tracking and balancing errors, can be a solution. However, it has a local minimum problem where the convergence of tracking and balancing errors is not guaranteed even though the linear combination error is zero. Therefore, a virtual angle‐based control method is presented, which can solve the underactuation problem without the local minimum issue. In addition, a neural network‐based observer is developed to estimate the velocity information of ball‐balancing robots with uncertainties, and the input saturation problem is considered. It is proven that all tracking and balancing errors are bounded and can be made arbitrarily small in the Lyapunov sense. Finally, simulation results are provided to verify the effectiveness of the proposed control system. [ABSTRACT FROM AUTHOR]

This paper aims to solve the control problem of fixed-time rigid formation for a class of nonlinear multi-agent systems with uncertain state perturbation and external disturbance. Firstly, in order to compensate the system uncertainty with uncertain dynamics and disturbances in fixed-time, an adaptive disturbance state observer independent of the initial state is therefore designed. Secondly, a fixed-time flocking control strategy is proposed by combining backstepping control method with fixed time control. It is strictly proved that the designed controller realises the fixed-time boundedness of the closed-loop error system independent of the initial state based on the rigidity theory and Lyapunov framework. Finally, the performance of the observer and controller is verified by numerical simulation, and the flocking formation control of a class of UAV group is realised on the simulation platform based on UE4. [ABSTRACT FROM AUTHOR]

This paper presents a new approach of fault-tolerant control (FTC) for the transmission line as a neutral variable time-delay system. The main goal of this work guarantees faulty neutral variable time delay system stabilization using the state feedback control design based on Lyapunov function and the Linear Matrix Inequality resolution. The use of the FTC method is to achieve actuator and sensor fault compensation. This method is based on two steps. The first one is the synthesis of a nominal control, which remains to maintain the closed-loop system stability. The second step is based on adding a new control law to the nominal one to compensate the fault effect on system behaviour and maintain the desired performance in the closed loop system. Then, a conception of an adaptive observer is used to detect and estimate the fault. Finally, the developed approach is applied for the transmission line. The given results are presented to prove the effectiveness of this approach. [ABSTRACT FROM AUTHOR]

This paper proposes an adaptive sensorless control based on sliding mode approach for Interior Permanent Magnet Synchronous Motor (IPMSM) using the method of the angular position estimation error extraction. The proposed strategy combines a novel Adaptive Super-Twisting Controller (ASTWC) with a novel Adaptive Observer High-Order Sliding Mode (AOHOSM), where: (i) The control and observer gains are parameterized in terms of a single parameter simplifying its implementation and reducing the tuning time. (ii) By using an auxiliary system, which does not depend on machine parameters, an AOHOSM is designed for estimating the angular position, speed and acceleration in a wide speed range of the IPMSM, (iii) and these are used in an ASTWC to track a desired reference of speed and direct-axis current. (iv) Sufficient conditions are given to ensure the stability of the closed-loop system using a Lyapunov approach. Furthermore, the proposed strategy is validated throughout experimental set-up in order to show its effectiveness. Finally, a comparative study between the proposed strategy and other strategies published in the literature is addressed. • An extraction of the angular error allows to design a parameter-free observer • A parameter-free observer is able to overcome the issues of parametric uncertainties • Adaptive sliding mode avoids overestimation of gains and reduces chattering • Closed-loop analysis of the system is easier due to separation principle [ABSTRACT FROM AUTHOR]

Abstract This paper introduces a distributed observer‐based emotional command‐filtered backstepping (DOECFB) approach for leader‐following cooperative output‐feedback control of heterogenous strict‐feedback multi‐agent systems (MAS) under mismatched uncertainties and input saturation. A novel state observer is designed based on radial‐basis emotional neural networks (RBENNs) that approximate uncertainties of model dynamics. To model inter‐agent dynamics with less complexity, emotion‐inspired approximated dynamics are shared among neighbouring followers, like emotional contagion in a group of people. An auxiliary system is also used to attenuate input saturation's negative effect on the cooperative tracking performance. Also, command filters and compensating signals are applied to avoid the ‘explosion of complexity’ in the backstepping design. Only local information from other agents is required for the proposed approach to guarantee convergence of the cooperative tracking error to a small region around zero and cooperatively semi‐globally uniformly ultimately boundedness of closed‐loop signals. Simulation examples on a second‐order uncertain MAS and multiple forced‐damped pendulums are conducted, and quantitative comparisons verify the effectiveness of DOECFB and the proposed observer.

Part of the motivation of this paper comes from the recent paper [W. Guo and H.-Z. Zhou, SIAM J. Control Optim., 57 (2019), pp. 1890--1928], where an adaptive control approach was used to estimate in real time all amplitudes and phases of the external harmonic sinusoidal disturbance with known frequencies to achieve output regulation for a 1-d Euler--Bernoulli beam equation. In our paper, we change the landscape in a completely different way for the output regulation of the Euler--Bernoulli beam equation for which the disturbances are generated from a completely unknown finite-dimensional exosystem; i.e., the frequencies of the sinusoidal signals that appear in the reference signal and disturbances are completely unknown. In addition, the number of frequencies is also assumed to be unknown yet has a known upper bound. We apply the technique of the Sylvester equation to decouple the ODE of the exosystem and the PDE of the plant first, and then adopt the adaptive observer to estimate all possible unknown frequencies that have entered into the tracking error. By designing two different adaptive observers to estimate the unknown frequencies as well as the state of the plant and exosystem, we propose two tracking error based feedback controls for achieving output regulation for this PDE. [ABSTRACT FROM AUTHOR]

Due to the increasing demand for autonomous driving, cyber‐attacks detection nowadays receives significant attention. Among several different types of attacks, Denial‐of‐Service (DoS) attacks can consume resources and cause the system under attack to stop responding, which can be considered a time delay model affecting the response of the attacked system. To detect the possible DoS attacks in Cooperative Adaptive Cruise Control platoon model, an adaptive observer design is proposed in this paper, where the time delay caused by DoS attacks can be fast and accurately estimated. To highlight the performance of the proposed adaptive observer, numerical simulations are applied and the performance is compared against the sliding mode observer in terms of the DoS attack time delay estimation convergence and accuracy. [ABSTRACT FROM AUTHOR]

In this article, an adaptive observer is designed for patients with Type 1 Diabetes Mellitus. The adaptive observer, synthesized using the so‐called Bergman's Minimal Model, simultaneously estimates the states and the parameter corresponding to the insulin‐independent glucose disappearance rate. The adaptive observer deals with parameter uncertainties, whereas the food intake is regarded as an external disturbance. The adaptive observer relies on intravenous glucose measurements. The state estimation error converges to a neighborhood of the origin despite the effects of the external disturbances and uncertainties, while the parameter estimation error converges in a fixed time to a neighborhood of the origin. The adaptive observer synthesis is given by a constructive method based on linear matrix inequalities. Simulation results show the feasibility of the proposed scheme. Moreover, the approach is validated in UVA/Padova metabolic simulator for ten in silico adult patients. [ABSTRACT FROM AUTHOR]

Summary: In this paper, the problem of fault estimation based on intermediate variable‐based adaptive observer for a class of nonlinear systems with Lipschitzian nonlinearities and faults is studied. An adaptive observer based on lower triangular matrix transformation is proposed, which comprise the output derivatives information so as to enhance the accuracy and speed of fault estimation effectually. The designed observer can be simplified to the lower triangular estimation observer, and is superior to the comprehensive estimation observer in the related literature. In addition, a more general error dynamics is derived based on the improved adaptive estimation observer, which makes it less conservative to obtain stability conditions. The uniformly ultimately bounded stability conditions of the estimated error dynamics are given by linear matrix inequalities (LMIs). Numerical examples are given to illustrate the effectiveness and merits of the proposed method. [ABSTRACT FROM AUTHOR]

In the high-power applications of cascaded H-bridge (CHB) converters, when considering the limitations of the switching characteristics of the power device, the phase-shifted carrier's pulse width modulation (PSC-PWM) is used to increase the equivalent switching frequency to improve the output quality. However, interleaved carriers make the CHB output characteristics change. Thus, the optimal performance of the traditional fixed switching frequency model predictive control (MPC) using synchronous carrier modulation is no longer applicable. Moreover, it is difficult for the traditional state-space predictive model to quickly eliminate the prediction error caused by a model mismatch in the transient response process. Therefore, parameter mismatch under high uncertainties leads to a significant decrease in the transient optimization performance. In this study, a PSC-PWM modulated MPC is proposed to replace the fixed switching frequency with PSC-PWM and to suppress the parameter mismatching in the predictive model by an adaptive observer. The CHB output voltage is regarded as a whole based on the voltage-second balanced rule within an equivalent switching period to use the adaptive observer. The CHB optimal vector duration calculated by the optimization strategy at each sampling time is implemented by the PSC-PWM within an associated H-bridge carrier period. Excellent dynamic and transient performances of the reference tracking can be obtained by the proposed method at similar carrier frequencies. Finally, the tracking performances are verified by experiments conducted on a 5L-CHB. [ABSTRACT FROM AUTHOR]

This paper proposes a new observer-based model-free adaptive sliding mode predictive control method (MFASPC) for discrete-time nonlinear systems. This scheme first equates the discrete-time nonlinear system to a linear form using a data-driven compact form dynamic linearization (CFDL) technique, establishes a data model consisting of only the pseudo partial derivatives (PPD), input data and output data, designs adaptive observers to achieve the estimation of the unknown PPD. The controller design part uses integral sliding mode control (SMC) to ensure the system’s robustness. In contrast, with its constraint characteristics, the model predictive control (MPC) replaces the traditional switching control of SMC. The closed-loop control quantities are obtained by solving a rolling optimization problem in the finite time domain to provide dynamic optimal control action. The theoretical derivation of the Lyapunov function is used to demonstrate the system’s stability. In order to verify the effectiveness of the proposed algorithm, numerical simulations and Photovoltaic power generation system simulation experiments are conducted, respectively, and the results show that the proposed control algorithm has a very reliable tracking capability and control accuracy.

This paper introduces a distributed observer‐based emotional command‐filtered backstepping (DOECFB) approach for leader‐following cooperative output‐feedback control of heterogenous strict‐feedback multi‐agent systems (MAS) under mismatched uncertainties and input saturation. A novel state observer is designed based on radial‐basis emotional neural networks (RBENNs) that approximate uncertainties of model dynamics. To model inter‐agent dynamics with less complexity, emotion‐inspired approximated dynamics are shared among neighbouring followers, like emotional contagion in a group of people. An auxiliary system is also used to attenuate input saturation's negative effect on the cooperative tracking performance. Also, command filters and compensating signals are applied to avoid the 'explosion of complexity' in the backstepping design. Only local information from other agents is required for the proposed approach to guarantee convergence of the cooperative tracking error to a small region around zero and cooperatively semi‐globally uniformly ultimately boundedness of closed‐loop signals. Simulation examples on a second‐order uncertain MAS and multiple forced‐damped pendulums are conducted, and quantitative comparisons verify the effectiveness of DOECFB and the proposed observer. [ABSTRACT FROM AUTHOR]

In this paper, a robust fault estimation for polynomial systems with time delay via a polynomial adaptive observer is presented and described. In fact, in contrast to previous research works on actuator faults, the learning rate utilized to govern the convergence speed of states and faults errors is not predetermined but is derived using the sum of squares optimization problem. Furthermore, the H ∞ optimization approach was created to reduce the impact of disturbances on state estimation error and fault estimation error. Finally, an illustrative practical example is given to validate the effectiveness of the suggested methodology. [ABSTRACT FROM AUTHOR]

In this paper, the degenerate kernel in conjunction with the bipolar coordinates is employed to solve the anti-plane problems of interaction between a screw dislocation and circular holes or rigid inclusions. Once the degenerate kernel of the angular basis function (ABF) is provided in terms of the bipolar coordinates, the analytical solution for cases of one or two circular holes and rigid inclusions can be derived. Not only the radial basis function (RBF) but also the ABF is used. First, the observer objectivity of the degenerate kernel in terms of the bipolar coordinates is examined numerically. A special case, one circular hole or rigid inclusion, is considered to demonstrate the validity of the present approach. Finally, the cases containing two circular holes and two circular rigid inclusions were examined. The comparison between available results and ours is well done. Besides, for the solutions of two holes and rigid inclusions, it is interesting to find that the present method provides an analytical solution with a series form of explicitly determined coefficients, while the coefficients provided by the complex variable need to be determined using the recursive formulae. [ABSTRACT FROM AUTHOR]

Distributed generation is an effective solution to clean, reliable, and cost-efficient energy supply. In this framework, inverters play a key role as energy converters. Accordingly, this article investigates the utilization of nonlinear inductive–capacitive (LC) filters for grid-connected inverters. Nonlinear LC filters are made of inductors with nonlinear B–H curves, which makes them have a smaller volume, less dissipation loss, and lower cost. The main drawback of using these filters, however, is their nonlinear current-dependent characteristics. To harness the undesirable impacts of nonlinear filters on the system performance, an adaptive observer-based control scheme is developed in this article. The proposed controller is based on a concurrent estimation of nonlinear inductor values, capacitor voltages, and grid-side currents. Based on its fast adaptation to plant changes, this scheme provides an improved stability margin and an inherent resonant damping characteristic under grid impedance uncertainty. Compared with the conventional approaches, this method requires no extra sensors. A comparative simulation and experimental analysis, based on a three-phase 7-kW inverter, is provided to validate the efficacy of the proposed control strategy. [ABSTRACT FROM AUTHOR]

In this study, we delve into the intricacies of addressing the challenge posed by simultaneous external disturbances and ever-changing actuator and sensor faults in the context of linear parameter-varying (LPV) systems. Our focus is on fault estimation (FE) and the pursuit of fault-tolerant tracking control (FTTC). LPV systems are described through a polytopic LPV representation with measurable gain scheduling functions. An adaptive LPV sliding mode observer (ASMO) is developed for the purpose of simultaneously estimating the system states and faults despite external disturbances. Compared with other conventional ASMO designs, the proposed observer has the capability to reconstruct the actuator faults by exploiting the equivalent output error injection signal required to maintain sliding motion and to directly estimate sensor faults using an adaptive algorithm. Based on online FE information, an FTTC is synthesized to compensate for the fault effect and to force closed-loop system states to track their desired reference signals. Sufficient conditions to ensure the stability of the state estimation errors and closed-loop system are established using Lyapunov stability theory together with H∞ techniques. These requirements are articulated using linear matrix inequalities (LMIs), which can be effortlessly addressed through optimization problem-solving methods. To illustrate the potency of the proposed approaches, an illustrative example is provided. To illustrate the potency of the proposed approaches and to validate their practical effectiveness, we offer an illustrative example featuring a vertical takeoff and landing aircraft. This real-world case study serves as a practical application of our theoretical contributions, demonstrating the adaptability and robustness of our approach in the face of complex, real-world challenges.