State Estimation Research Articles

Data-Driven Fault Detection and Isolation for Multirotor System Using Koopman Operator

AbstractThis paper presents a data-driven fault detection and isolation (FDI) for a multirotor system using Koopman operator and Luenberger observer. Koopman operator is an infinite-dimensional linear operator that can transform nonlinear dynamical systems into linear ones. Using this transformation, our aim is to apply the linear fault detection method to the nonlinear system. Initially, a Koopman operator-based linear model is derived to represent the multirotor system, considering factors like non-diagonal inertial tensor, center of gravity variations, aerodynamic effects, and actuator dynamics. Various candidate lifting functions are evaluated for prediction performance and compared using the root mean square error to identify the most suitable one. Subsequently, a Koopman operator-based Luenberger observer is proposed using the lifted linear model to generate residuals for identifying faulty actuators. Simulation and experimental results demonstrate the effectiveness of the proposed observer in detecting actuator faults such as bias and loss of effectiveness, without the need for an explicitly defined fault dataset.

Research on design and control methods of a lightweight upper limb joint isokinetic rehabilitation training equipment

IntroductionIsokinetic exercise can improve joint muscle strength and stability, making it suitable for early rehabilitation of stroke patients. However, traditional isokinetic equipment is bulky and costly, and cannot effectively avoid external environmental interference.MethodsThis paper designed a lightweight upper limb joint isokinetic rehabilitation training equipment, with a control system that includes a speed planning strategy and speed control with disturbance rejection. Based on the established human-machine kinematic closed-loop model between the equipment and the user, a dynamic evaluation method of torque at the joint level was proposed.ResultsTo validate the effectiveness of the equipment, experiments were conducted by manually applying random disturbances to the equipment operated at an isokinetic speed. The results showed that the root mean square error between the observed torque curve of the second-order linear extended state observer used in this paper and the actual disturbance curve was 0.52, and the maximum speed tracking error of the speed control algorithm was 1.27%. In fast and slow sinusoidal speed curve tracking experiments, the root mean square errors of the speed tracking results for this algorithm were 9.65 and 5.27, respectively, while the tracking errors for the PID speed control algorithm under the same environment were 19.94 and 12.11.DiscussionThe research results indicate that compared with traditional PID control method, the proposed control strategy demonstrates superior performance in achieving isokinetic control and suppressing external disturbances, thereby exhibiting significant potential in promoting upper limb rehabilitation among patients.

Network control based load frequency control considering the transmission delay

AbstractThis study introduces a network‐based controller to regulate frequency within power systems. The primary objective of this investigation is to ensure the stability of power systems experiencing delays in control signal transmission. Initially, a modified networked predictor is proposed to anticipate future signals within a decentralized load frequency control framework. Subsequently, a timestamp technique is employed to actively compensate for the impact of transmission delays. Additionally, an event trigger mechanism is incorporated to complement the extended state observer and optimize signal transmission resource usage. The outcomes of the study indicate that the proposed method effectively mitigates delay effects while maintaining superior performance. Moreover, the proposed method remarkably reduces the required transmission resources.

Observer-based self-triggered adaptive neural network control for nonlinear systems with prescribed time

This paper is concerned with the adaptive neural networks (NNs) prescribed-time control problem for a class of strict-feedback nonlinear systems subject to unmeasured states via the self-triggered control (STC). By developing a new state observer with prescribed-time function, an adaptive NNs self-triggered controller is designed to solve the problem of prescribed-time performance (PTP). Due to the initiative of the STC, it has excellent practical significance in terms of contracting computing resources and network communication resources. With the proposed new strategy, the PTP of the closed-loop system can be guaranteed, and all the signals within the closed-loop system are bounded. Finally, the practicability and effectiveness of the above prescribed-time STC algorithm are verified via some physical simulations.

Improved Single Current Sensor Based PMSM Control under Low Frequency Ratio Using Discrete-Time Adaptive Luenberger Observer

Improved Single Current Sensor Based PMSM Control under Low Frequency Ratio Using Discrete-Time Adaptive Luenberger Observer

Collision-free trajectory tracking strategy of a UUV via finite-time extended state observer-based sliding mode predictive control

This paper focuses on a trajectory tracking problem for unmanned underwater vehicles (UUVs) subject to current disturbances and static obstacles. A double-loop framework is established. The kinematic governor employs model predictive control(MPC), which takes into account the UUV’s kinematic characteristics as well as the conditions for obstacle avoidance when calculating the control command and provides an optimal velocity command input for the dynamic controller. Then, the dynamic controller is developed based on a fast finite-time extended state observer (FESO) and a fast adaptive integral terminal sliding mode controller (FAITSMC). The construction of the fast FESO integrates UUV’s dynamics model, a proportional integral velocity variable, and a fractional order term of the output observation error, which can identify model uncertainties and external disturbances in finite time. By means of adaptive uncertainty compensation, the FAITSMC enables the error between actual speed and speed reference to converge to a minimum quickly. By applying Lyapunov stability theory and the finite-time analysis technique, sufficiency criteria are established to guide and keep the UUV on a reference trajectory via an inner-outer loop control structure. Finally, simulation examples in different scenarios are presented to verify the feasibility and effectiveness of the derived theoretical results.

New disturbance observer-based speed estimator for induction motor

<p>This paper discusses a novel disturbance observer designed as an estimator to determine the rotor speed of an induction motor. This observer is a solution to obtain a simple structure with a small number of compact observer gains. Furthermore, the adaptation law is no longer required to estimate induction motor speed values. This is a machine model-based computation method that uses a stationary reference frame. The nonlinearity problem is solved using an additional state vector in the observer model, which is known as an extended state observer. This approach easily and systematically determines the observer gain by applying the linear quadratic regulator (LQR) method, thereby avoiding time-consuming trial errors. The proposed observer, which was presented in both continuous and discrete forms, was tested using a sensorless V/f- controlled induction motor. The simulation results show that the proposed observer can accurately estimate all states, namely, the rotor flux and stator current; therefore, the proposed estimator provides the speed and electromagnetic torque for a wide operational range of speeds and load torques. It was also shown that the proposed observer was robust to noise and uncertainty in induction motor parameters.</p>

Smooth and Robust Current Control of PMSMs With Decoupling-Type Extended State Observers

Smooth and Robust Current Control of PMSMs With Decoupling-Type Extended State Observers

A Real-Valued Kalman Estimation Method for Harmonic Signal Analysis in Biomedical Applications

This work presents a methodology for obtaining the harmonic estimation of biomedical signals such as electrocardiogram, cardiorespiratory and blood pressure signals. The proposed methodology is achieved using polynomial approximation and the Kalman filter. As advantage, the technique includes instant estimations of signal harmonics and its derivatives using a real-valued model. Furthermore, a comparison of the results is conducted with the Savitzky-Gola, nonlinear tracking differentiator methods, extended state observer and digital differentiator base on Taylor series. The results suggest that the proposed method has the potential to enhance the quality of signal measurements, especially in the presence of noise.

Asymptotical Tracking Control of Complex Dynamical Network Based on Links State Observer

Asymptotical Tracking Control of Complex Dynamical Network Based on Links State Observer

Oxygen-sensor-based 2-DOF PI air-flow controller of fuel cell system

Oxygen starvation may occur when oxygen is not replenished quickly after a sudden increase of load in fuel-cell-powered vehicles, leading to permanent damage to the fuel cell membrane. To avoid this phenomenon, the oxygen excess ratio (OER) is usually estimated through observers as it cannot be measured directly. Since observers may suffer from model mismatches that lead to inaccurate OER estimation, a possible modification to the fuel cell system is proposed in this work by including an oxygen flow sensor at its exhaust. Yet the response of this sensor is slow, and to compensate for the slow response of the sensor, a novel two-degree-of-freedom PI controller and a state observer are designed to provide a robust, accurate, and quick estimation and control of the OER.

Resonant tunneling via the corner states in the double-barrier-modulated second-order topological insulator

In this work, we use the Green's function technique and investigate transport properties of the electronic states in the two-dimensional (2D) second-order topological insulator (TI). We calculated the bulk, edge, and corner states corresponding respectively to the infinite plane, one-dimensional nanoribbon, and the square flake geometries of the 2D second-order TI. Existence of the corner states in the square flake is confirmed. In this approach, we considered tunneling via the localized corner states in the double-barrier-modulated 2D second-order TI. Sharp peaks are found in the transmission probability and conductance incurred by the corner states lying within the surrounding energy gap. The relation between transmission peaks and the corner states is confirmed by their energy correspondence. The extremely discrete and sharp resonance might lend insight to experimental observation of the corner states in the 2D second-order TI.

Observation of a core-excited dipole-bound state ∼1eV above the electron detachment threshold in cryogenically cooled acetylacetonate.

Dipole-bound states in anions exist when a polar neutral core binds an electron in a diffuse orbital through charge-dipole interaction. Electronically excited polar neutral cores can also bind an electron in a diffuse orbital to form Core-Excited Dipole-Bound States (CE-DBSs), which are difficult to observe because they usually lie above the electron detachment threshold, leading to very short lifetimes and, thus, unstructured transitions. We report here the photodetachment spectroscopy of cryogenically cooled acetylacetonate anion (C5H7O2-) recorded by detecting the neutral radical produced upon photodetachment and the infrared spectroscopy in He-nanodroplets. Two DBSs were identified in this anion. One of them lies close to the electron detachment threshold (∼2.74eV) and is associated with the ground state of the radical (D0-DBS). Surprisingly, the other DBS appears as resonant transitions at 3.69eV and is assigned to the CE-DBS associated with the first excited state of the radical (D1-DBS). It is proposed that the resonant transitions of the D1-DBS are observed ∼1eV above the detachment threshold because its lifetime is determined by the internal conversion to the D0-DBS, after which the fast electron detachment takes place.

A Modified ADRC Scheme Based on Model Information for Maglev Train

During the operation of maglev trains, they are subjected to various disturbances. The presence of these disturbances presents a significant challenge for attaining high-performance control and even poses the risk of system instability. To further enhance the anti-disturbance capability of maglev trains, this paper proposes a model information-assisted modified active disturbance rejection control (MADRC) approach. A mathematical model of the single-point suspension system of maglev trains is constructed for the design of the extended state observer (ESO), which is a modified extended state observer (MESO), and a nonlinear mechanism is incorporated to boost the performance of the ESO. Owing to the introduction of model information, the estimated quantity of disturbances by MESO no longer considers the system model deviation as a disturbance. Hence, the linear feedback control law is modified accordingly. The MESO is regarded as an ESO with time-varying gain using the equivalent gain method, and its stability is proven using the Lyapunov method. The tracking and anti-disturbance performances of different controllers are compared via simulation experiments. Suspension and anti-disturbance experiments are conducted on the single-point suspension experimental platform, verifying that the proposed MADRC has a more potent suppression ability for load disturbances in the suspension system.

A robust design of time-varying internal model principle-based control for ultra-precision tracking in a direct-drive servo stage

This paper proposes a robust design of the time-varying internal model principle-based control (TV-IMPC) for tracking sophisticated references generated by linear time-varying (LTV) autonomous systems. The existing TV-IMPC design usually requires a complete knowledge of the plant I/O (input/output) model, leading to the lack of structural robustness. To tackle this issue, we, in this paper, design a gray-box extended state observer (ESO) to estimate and compensate unknown model uncertainties and external disturbances. By means of the ESO feedback, the plant model is kept as nominal, and hence the structural robustness is achieved for the time-varying internal model. It is shown that the proposed design has bounded ESO estimation errors, which can be further adjusted by modifying the corresponding control gains. To stabilize the ESO-based TV-IMPC, a time-varying stabilizer is developed by employing Linear Matrix Inequalities (LMIs). Extensive simulation and experimental studies are conducted on a direct-drive servo stage to validate the proposed robust TV-IMPC with ultra-precision tracking performance (∼60nm RMSE out of ±80mm stroke).

Optimization control of wastewater treatment based on neural network and multi-objective optimization algorithm

In wastewater treatment, energy consumption and effluent quality are conflicting objectives. Improving effluent quality while reducing energy consumption has become a pressing issue. This paper proposes a dynamic optimal control method for the wastewater treatment process, utilizing neural networks and multi-objective dragonfly algorithm. A regression prediction model, based on a Self-Attention mechanism of a multi-scale convolutional neural network-bidirectional gated recurrent unit, is used to establish a soft measurement model for effluent quality and energy consumption, serving as the optimization objective function. The multi-objective dragonfly algorithm then optimizes this objective to determine the optimal set values for control variables. A Linear Active Disturbance Rejection Control, based on a scalable bandwidth linear extended state observer, is designed to track these optimal set values. The proposed method is tested using Benchmark Simulation Model No. 1 (BSM1). Results demonstrate that the method effectively obtains and tracks optimal set values of control variables, ensuring effluent quality meets standards and significantly reducing energy consumption compared to four other methods.

An improved two‐degree‐of‐freedom ADRC for asynchronous motor vector system

AbstractThis paper proposes an improved two‐degree‐of‐freedom active disturbance rejection controller for the coupling problem of asynchronous motor vector system. To simplify the analysis process and accommodate observers of different types, a unified expression based on different controllers for the system output is developed. The closed‐loop transfer function generated by the reference input and load disturbance is given. For the coupling problem of motor output speed and immunity, the structure of a higher‐order extended state observer is reconstructed. The extended state observer estimates both the output speed and the total system disturbance, which serve as feedback and feed‐forward compensation quantities. Compared to the PI controller and traditional active disturbance rejection controller, the proposed controller achieves decoupling of output response speed and immunity, simplifies the process of parameter tuning. Finally, simulation and experiment results verify the feasibility and effectiveness of the algorithm in this paper.

Full-speed domain position sensorless control strategy for PMSM based on a novel phase-locked loop

This paper proposes a full-speed-domain position-sensor-less control strategy for precise control under forward and reverse rotation conditions to address the weak convex polarity of surface-mounted permanent magnet synchronous motor (SPMSM). The strategy comprises several key stages: pre-positioning of the rotor, constant current variable frequency (I/F) start-up, construct the Luenberger State Observer, and utilization of an improved phase-locked loop (PLL) for position estimation. In the pre-positioning stage, a constant amplitude current is applied to drag the rotor to a predetermined position. Subsequently, the I/F start-up stage accelerates the motor to a predetermined speed before transitioning to the Luenberger observer for closed-loop speed control, which is based on an extended back electromotive force (back-EMF) a two-phase rotating coordinate system. The improved PLL for position and speed estimation features three components: a phase discriminator (PD), a voltage-controlled oscillator (VCO), and loop filter (LF). Experimental results demonstrate the efficacy of the proposed strategy, showing quick start-up response, speed estimation error below 2 RPM, rotor position estimation error under 0.6 degrees post-loop closure, stable tracking during rapid speed changes, and consistent accuracy and stability even under reverse rotation conditions, thereby meeting the control strategy’s objectives.

Output-feedback formation tracking control of autonomous surface vessels with collision avoidance

In this paper, a formation control problem with collision avoidance is investigated for underactuated surface vessels with position–heading measurements. Each vessels is subject to actuator magnitude and rate saturations. At first, an anti-saturation nonlinear extended state observer is developed to recover the linear velocity and yaw rate as well as unknown uncertainty and disturbances. Then, observer-based formation control laws are designed based on an artificial potential functions, obstacle avoidance yaw references and a backstepping technique. The stability of closed-loop formation control system is proved. The salient features of this paper are as follows. First, formation control with the capability of collision avoidance is achieved by using artificial potential functions and obstacle avoidance yaw references, which is more aggressive. Second, the proposed controller is able to deal with the magnitude and rate constraints of the actuator. Third, the proposed anti-saturation nonlinear extended state observer can achieve better estimation results for rapidly changing signals. Simulation results are given to substantiate the proposed formation control laws.

Classification of adsorbates in scanning tunneling microscopy images of Fe3O4(111) surfaces exposed to water and carbon monoxide

Understanding the structure of catalyst surfaces with adsorbed molecules is key to improving catalyst design. Scanning tunneling microscopy (STM) allows the observation of adsorption states and sites and provides insights into diffusion and desorption processes; however, the presence of multiple types of molecules on the surface presents challenges such as the identification of species and verification of reaction progress, particularly at room temperature or higher. In this study, we develop a protocol for the height classification analysis of STM images using the Watershed algorithm. This method is applied to a system involving the co-adsorption of H2O and CO on the Fe3O4(111) surface, which represents the beginning of the water-gas shift reaction. Water molecules and dissociated OH species were identified in STM images of the Fe3O4(111) surface following the adsorption of water. Furthermore, gradual changes in the types of surface species were observed upon exposure of the surface to CO, indicating reaction progression. Our observations suggest that CO may react with molecular water rather than with dissociated OH on Fe sites. Despite its simplicity, the height classification analysis effectively identifies changes in the adsorbates on the catalyst surface. This method can be extended to other catalyst surfaces with adsorbed gasses.