Adaptive Observer Research Articles

Global terminal sliding-mode finite-time rotary control of hydraulic roofbolter based on an adaptive observer

Global terminal sliding-mode finite-time rotary control of hydraulic roofbolter based on an adaptive observer

Synthesis of adaptive observer for nonlinear nonstationary systems

Synthesis of adaptive observer for nonlinear nonstationary systems

Experimental and numerical study on data-driven prediction for wildfire spread incorporating adaptive observation error adjustment

In recent wildfire prediction research, data assimilation (DA) methods like Ensemble Kalman filtering have gained traction for integrating observation data to enhance prediction accuracy. Most previous studies trusted that the observation data were accurate, and set a small observation error, which causes unreliable predicted results for scenarios with large observation error. To tackle this, our study introduced a method that iteratively adjusted the potential range of observation errors by comparing observation and simulation data over time. We conducted a 30-m experiment and kilometer-scale numerical simulations. Unlike prior research, we adopted larger error ranges (the similarity index with true data ranges from 0.6 to 1) for both real and synthetic observation data. In the experiment, to increase the complexity of fire spread, a heterogeneous fuel arrangement was employed. Irregular flame fronts appeared due to incomplete combustion and were difficult to replicate in simulations. Better accuracy was achieved using real observation data to revise predictions. Furthermore, to improve the applicability of the algorithm, numerical simulations were designed to consider observation error changing over time or not. The Root Mean Square Errors for the fire front prediction using the proposed method remained lower than that of the traditional DA approach.

Fuzzy-type 2 fractional fault tolerant adaptive controller for wind turbine based on adaptive RBF neural network observer

Fuzzy-type 2 fractional fault tolerant adaptive controller for wind turbine based on adaptive RBF neural network observer

Prescribed-time adaptive state observer approach for distributed output-feedback formation tracking of networked uncertain underactuated surface vehicles

Prescribed-time adaptive state observer approach for distributed output-feedback formation tracking of networked uncertain underactuated surface vehicles

Resilient adaptive event-triggered containment control of nonlinear multi-agent system under concurrent DoS attacks and disturbances

This paper presents a secure containment control problem of nonlinear Multi-Agent Systems (MASs) under aperiodic Denial of Service (DoS) attacks and external disturbances simultaneously. A novel adaptive neural network (NN)-based event-triggered control is considered that uses the nonlinear estimator to predict the state of other agents. Since data access is denied during DoS attacks, the overall system switches between two modes of stable and unstable containment behaviours. Therefore, the maximum of attack duration and frequency is determined such that the overall system evolution leads to containment convergence in the presence of DoS attacks. We proposed an adaptive NN-based distributed disturbance observer to estimate external disturbances in a nonlinear system's dynamics. The state estimator predicts neighbouring agents' states, and each agent's input and event times are determined without monitoring other agents. The directed graph topology is used to determine data exchange among agents instead of an undirected graph that reduces implementation conditions. Zeno-free behaviour is also proved by analysis of the system. Eventually, the numerical simulation of the proposed approach is shown. Abbreviations: DoS attacks, Containment control of multi-agent system

Observer-based adaptive memory event-triggered consensus tracking control for high-speed train under DoS attacks

This paper focuses on the event-triggered consensus tracking of high-speed trains under denial-of-service (DoS) attacks. A novel anti-disturbance control strategy is proposed, based on an adaptive memory state observer and a disturbance observer. A memory is introduced to buffer system output information which enhancing the precision of the state observer, and the gust disturbance during the high-speed train operation is estimated by the disturbance observer. The Linear Matrix inequality technique is used to obtain the observer feedback coefficient and the controller gain, and the Lyapunov theory is employed to demonstrate the controller’s consistency tracking performance. Applying the memory to the event-triggered mechanism, a new adaptive memory event-triggered scheme is designed to better ensure system performance and effectively prevent the occurrence of Zeno behavior. To achieve precise identification of DoS attacks in the event-triggered environment, a new detection algorithm is proposed. Finally, simulation examples are used to validate the effectiveness and practicality of the proposed scheme.

A novel concurrent learning-based fixed-time convergent visual depth observer for weakly persistently exciting perspective dynamical systems

The problem of synthesizing a fixed-time depth observer based on monocular image feedback is considered in this study. The key challenge lies in the fact that the perspective dynamical system used to model visual motion is typically characterized as being weakly persistently exciting, which complicates observer synthesis. Furthermore, the key to achieving the task objective (safe obstacle avoidance, for instance) lies in the synthesis of a depth observer that achieves rapid convergence of the (static) obstacle depth estimate to the ground truth in a known fixed-time. To address these challenges, and in contrast with prior schemes that rely on a motion-restrictive persistency of excitation (PE) condition for ensuring exponential convergence, a novel adaptive observer framework is considered in this study that incorporates a concurrent learning (CL) term for ensuring fixed-time observer convergence. In particular, the use of concurrent learning allows for the synthesis of a relaxed finite-time excitation condition that relies on historical data recorded over a dynamic sliding window in the recent past that the proposed observer relies on to ensure fixed-time convergence. Thus, a continuous-time reduced-order observer formulation is presented that relies on camera motion data to achieve fixed-time convergence to a uniform ultimate bound for a suitably large choice of the observer gains. Experimental results are used to demonstrate the efficacy of the proposed scheme in the presence of significant measurement noise. A performance comparison study is also undertaken to demonstrate superior performance of the proposed scheme compared to leading alternative designs. Finally, the practical applicability of the proposed scheme is verified by incorporating the proposed scheme within a reactive navigation scheme to accomplish obstacle avoidance. By incorporating a suitably informative CL term within the observer framework, the proposed scheme eliminates the need to rely on a difficult-to-verify PE condition, thus rendering it more suitable for practical applications like visual target-tracking and visual servo control.

Indirect adaptive observer control (I-AOC) design for truck–trailer model based on T–S fuzzy system with unknown nonlinear function

Tracking is a crucial problem for nonlinear systems as it ensures stability and enables the system to accurately follow a desired reference signal. Using Takagi–Sugeno (T–S) fuzzy models, this paper addresses the problem of fuzzy observer and control design for a class of nonlinear systems. The Takagi–Sugeno (T–S) fuzzy models can represent nonlinear systems because it is a universal approximation. Firstly, the T–S fuzzy modeling is applied to get the dynamics of an observational system in order to estimate the unmeasurable states of an unknown nonlinear system. There are various kinds of nonlinear systems that can be modeled using T–S fuzzy systems by combining the input state variables linearly. Secondly, the T–S fuzzy systems can handle unknown states as well as parameters known to the indirect adaptive fuzzy observer. A simple feedback method is used to implement the proposed controller. As a result, the feedback linearization method allows for solving the singularity problem without using any additional algorithms. A fuzzy model representation of the observation system comprises parameters and a feedback gain. The Lyapunov function and Lipschitz conditions are used in constructing the adaptive law. This method is then illustrated by an illustrative example to prove its effectiveness with different kinds of nonlinear functions. A well-designed controller is effective and its performance index minimizes network utilization—this factor is particularly significant when applied to wireless communication systems.

Monitoring and Reconstruction of Actuator and Sensor Attacks for Lipschitz Nonlinear Dynamic Systems Using Two Types of Augmented Descriptor Observers

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.

Output Feedback Active Fault Tolerant Control for a 3-DOF Laboratory Helicopter With Sensor Fault

In this paper, an output feedback-based active fault tolerant control (FTC) scheme is proposed for an unstable three degree-of-freedom (3-DOF) helicopter equipped only with angular position sensors, of which a single sensor can be faulty. Limited by the available outputs, existing fault estimation (FE) and FTC schemes cannot be applied to this helicopter because when the sensor measuring the elevation or travel angle is faulty, it does not satisfy the minimum-phase and matching conditions required by standard observers. To circumvent this problem, an adaptive interval observer is firstly designed as a fault detection and isolation (FDI) unit to indicate the occurrence and location of a fault, in contrast to the existing FDI methods that require a bank of observers and incur a high computational cost. Then a high-gain observer is combined with a sliding mode observer to form an FE unit that does not require the matching and minimum-phase conditions. Based on the estimate of the fault, an FTC scheme is constructed to ensure an <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathcal{H}_{\infty}$</tex-math> </inline-formula> performance of the faulty system. Finally, physical experiments on the 3-DOF helicopter verify the effectiveness of the proposed scheme. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —The 3-DOF lab helicopter serves as an ideal experimental platform for control strategies. In this paper, an active fault tolerant control (FTC) scheme is developed for a 3-DOF lab helicopter when any single sensor can be faulty. The helicopter is nonlinear and subject to external disturbances. There are only encoders measuring the attitude angles and no sensors for angular velocities. In this paper, we firstly design a fault detection and isolation (FDI) unit based on an adaptive interval observer to detect the fault and identify its location; it incurs a lower computational cost compared to existing methods that use a bank of observers. Then a high-gain observer is combined with a sliding mode observer to estimate the fault. Based on the estimate of the fault, an FTC scheme is constructed and designed using Linear Matrix Inequalities to ensure an <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathcal{H}_{\infty}$</tex-math> </inline-formula> performance of the faulty system.

Fault Tolerant Control of Fuzzy Stochastic Distribution Systems With Packet Dropout and Time Delay

The fault diagnosis (FD) and fault tolerant control (FTC) problem of the fuzzy stochastic distribution system (SDS) over packet dropout and time delay is studied. Firstly, the static model of linear fuzzy logic system that approximates the output PDF and the dynamic model with the multiplicative fault in a fuzzy system are established. The random packet dropout and time delay caused by the network are described in a unified framework, in which the lost data is compensated with the latest data received by the buffer. On this basis, an adaptive observer is devised to estimate the unknown multiplicative fault. The design of sliding mode predictive fault tolerant controller is discussed to ensure that the system still has good tracking performance after the fault occurs. Numerical simulations on a molecular weight distribution control system are supplied to prove the effectiveness of the presented scheme. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —The motivation of this paper is to improve the reliability of the fuzzy stochastic distribution system with packet dropout and time delay. This kind of stochastic distribution systems is described by the relationship between the input and the output PDF rather than the normal relationship between the input and the output. When the multiplicative fault occurs in the system, the design of fault diagnosis strategy and fault-tolerant controller becomes an important challenge, especially when the system is subject to packet dropout and time delay. To address the above problems, a new fault diagnosis and fault-tolerant control scheme is proposed, which can accurately estimate the time and size of the fault and obtain the satisfactory fault-tolerant control effect.

Dynamic assessment approach for intelligent power distribution systems based on runtime verification with requirements updates

The study aims to address the challenge of dynamic assessment in power systems by proposing a design scheme for an intelligent adaptive power distribution system based on runtime verification. The system architecture is built upon cloud–edge-end collaboration, enabling comprehensive monitoring and precise management of the power grid through coordinated efforts across different levels. Specifically, the study employs the adaptive observer approach, allowing dynamic adjustments to observers to reflect updates in requirements and ensure system reliability. This method covers both structural and parametric adjustments to specifications, including updating time protection conditions, updating events, and adding or removing responses. The results demonstrate that with the implementation of adaptive observers, the system becomes more flexible in responding to changes, significantly enhancing its level of efficiency. By employing dynamically changing verification specifications, the system achieves real-time and flexible verification. This research provides technical support for the safe, efficient, and reliable operation of electrical power distribution systems.

Adaptive Periodic Disturbance Observer Based on Fuzzy Logic Compensation for Speed Fluctuation Suppression of PMSM Under Periodic Loads

Adaptive Periodic Disturbance Observer Based on Fuzzy Logic Compensation for Speed Fluctuation Suppression of PMSM Under Periodic Loads

O iii] λ5007 emissions in extremely red quasars (ERQs) are compact

ABSTRACT ‘Extremely red quasars’ (ERQs) are a non-radio-selected, intrinsically luminous population of quasars at cosmic noon selected by their extremely red colour from rest-frame UV to mid-IR. ERQs are uniquely associated with exceptionally broad and blueshifted [O iii] $\lambda$5007 emission reaching speeds $\gt $6000 km s$^{-1}$. We obtained adaptive optics integral-field spectroscopic observations using Keck/OSIRIS and Gemini/NIFS of a sample of 10 ERQs with bolometric luminosities (10$^{47.0}$–10$^{47.9}$) erg s$^{-1}$ at $z\sim$ (2.3–3.0). The goal is to measure the sizes and spatially resolved kinematics of the [O iii]-emitting regions. We study the surface brightness maps and aperture-extracted spectra and model the point-spread functions. We identify signs of merger activities in the continuum emissions. We identify physically distinct [O iii] kinematic components that are bimodal and respectively trace ERQ-driven outflows of velocity dispersion $\gtrsim$250 km s$^{-1}$ and dynamically quiescent interstellar media. We find that the ERQ-driven ionized outflows are typically at $\sim$1 kpc scales whereas the quiescent ionized gas extend to a few kpc. Compared to normal quasars the extremely fast ERQ-driven [O iii] outflows tend to be more compact, supporting the notion that ERQs are in a young stage of quasar/galaxy evolution and represent unique physical conditions beyond orientation differences with normal quasar populations. The kinematically quiescent [O iii] emissions in ERQs tend to be spatially resolved but less extended than in normal quasars, which can be explained by global and patchy dust obscuration. The hint of ionization cones suggests some of the obscuration can be partially explained by a patchy torus.

Adaptation of two woody plant species – Betula pubescens and Salix udensis to the high Arctic conditions (Tiksi settlement, Russia)

The positive trends in climate change that have become apparent in recent decades are accompanied by a shift in the range boundaries of many boreal plant species further north, to the shores of the Arctic Ocean. Monitoring the displacement of these boundaries, the introduction of plants unusual to the Arctic, as well as monitoring their adaptation to new conditions, is an important component of botanical research. In 2016, in the village Tiksi identified 2 species of woody plants – Betula pubescens and Salix udensis, known from the forest zone and not previously observed in this area. Obviously, both species appeared here due to the unintentional introduction of seeds by humans. Repeated observations of the condition of these species, conducted in 2023, showed that they exhibit stable vitality, withstood 7 winter periods and gave some increase in phytomass. Measurements of the area of the leaf blade of Betula pubescens and the size of 10 leaves of Salix udensis showed that these indicators are within the normal biological parameters of these species. Both species do not form generative structures yet. Salix udensis has grown significantly during the observation period, reaching a height of about 2.5 m. Perhaps this is the result of the protective influence of the building behind which it develops. Betula pubescens grows more slowly, hardly retains orthotropic shoots exceeding the average snow cover levels, which reach a height of 54 cm, but at the same time partially drained and deprived of foliage. Nevertheless, the lower part of the plant has fully leafy branches. Both species are valuable objects for further observations of their adaptation to Arctic conditions. Since Betula pubescens grows near the slope of the highway, it is recommended to move it to a safe place less susceptible to anthropogenic influence. Tiksi village, located in high latitudes, could serve as a good reference base for a wide range of scientific observations of plant adaptation to Arctic conditions. The optimal solution for this would be the creation of the Arctic Botanical Garden in Tiksi.

Adaptive-neural command filtered synchronization control of tele-robotic systems using disturbance observer with safety enhancement

Due to the nature of the tele-robotic systems, some main problems are imposed, such as external disturbances, time-varying delays and external torques. However, some applications such as tele-surgery systems, require high precision, safety and accurate transmission of information between the leader and follower robots. In order to facilitate the imposed restrictions and to guarantee transient-state, and even steady-state performances in the presence of external disturbances and system uncertainties, the time-varying full state constrained control is employed by applying the Barrier Lyapunov function (BLF). In this regard, a new adaptive neural network torque observer is proposed to make the system independent from the force sensors. Moreover, the independence of the proposed algorithm from perfect knowledge of the manipulator dynamics and time-delay’s derivative of communication channels are advantages of this paper. Furthermore, the key idea of this paper is to lessen the computational complexity in the backstepping-based adaptive neural controller by means of the command filter strategy and the BLF approach is combined to converge the synchronization error signals into a predefined constraint. In order to consider practical limitations, time-varying delays in the communication channel and input saturation constraint are also considered in the design mechanism. Finally, the stability analysis of the observer and controller together is conducted and the evaluation of the performance of the proposed method is performed through a series of various scenarios and comparisons.

Observer-based adaptive neural network control design for nonlinear systems under cyber-attacks through sensor networks

This paper considers the adaptive neural tracking control for uncertain nonlinear strict-feedback systems under state-dependent cyber-attacks through sensor networks. As a distinctive feature, by constructing a novel adaptive neural observer, the estimates of system states are used for feedback control instead of the compromised system states corrupted by sensor attacks, such that the assumption on the derivative of attack weight can be removed from the adaptive controller design process. Then, an observer-based control scheme is developed such that all the system signals are bounded and the tracking error converges to a small neighborhood of zero. During the procedure of control design, adaptive backstepping technique is combined with radial basis function neural networks (RBFNNs) to construct controllers. Finally, two examples validate the efficacy of the proposed control scheme.

Adaptive observer design for coupled ODE–hyperbolic PDE systems with application to traffic flow estimation

This paper studies the state estimation problem for a class of coupled linear ODE–hyperbolic PDE systems with unknown in-domain parameters. Based on the swapping transformation and the least squares parameter estimation method, a Luenberger-type adaptive boundary observer is designed to estimate the ODE–PDE states under the unknown parameters. The exponential convergence conditions w.r.t. the observer feedback gains are given by employing the Lyapunov technique. Finally, an application to traffic state estimation of the Aw–Rascle–Zhang (ARZ) model involving the unknown relaxation time and the boundary input speed deviation is provided to validate the theoretical result.

Fault estimation for a class of nonlinear distributed systems using the adaptive observer technique

AbstractIn 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.