State Estimation Research Articles

Event-Triggered Mechanism-Based Discrete-Time Nash Equilibrium Seeking for Graphic Game With Outlier-Resistant ESO.

In this article, we consider a discrete-time Nash equilibrium (NE) seeking problem for graphic game subject to disturbances. For the first-order dynamics, the discrete-time outlier-resistant extended state observer (ESO)-based game strategy is proposed to enable the players to estimate the disturbances under effect of anomaly measurements and then compensate them. An event-triggered mechanism is applied between adjacent players to reduce the frequency of communication. The convergence of the outlier-resistant ESO and control strategy is presented. Moreover, the upper bound of ϵ -NE solution deviating from the unique point of nominal system is given analytically. Then, the addressed issues are extended to high-order game systems. The NE seeking-based control strategy for each player is designed such that the equilibrium point converges to the ϵ -NE which is also analytically calculated. Finally, in order to verify the effectiveness of the proposed game strategy, an example of satellite system is given.

Sensorless Control of Variable Flux Memory Machines Based on Improved Extended EMF Model and Adaptive Extended State Observer

Sensorless Control of Variable Flux Memory Machines Based on Improved Extended EMF Model and Adaptive Extended State Observer

Rapid attitude stabilization of ultra-low orbit satellites using movable masses and reaction wheels

This paper proposes a combination of movable masses and reaction wheels for the attitude control of ultra-low-orbit satellites. During the attitude control of ultra-low-orbit satellites, the satellite’s attitude is significantly influenced by the aerodynamic torque. Therefore, this paper proposes a combined control method using movable masses and reaction wheels. By adjusting the position of the movable masses to modify the relative distance between the center of mass (CM) and the center of pressure (CP), the aerodynamic torque is effectively reduced. Additionally, a finite-time terminal sliding mode extended state observer (TSMESO) is incorporated to estimate the reduced aerodynamic torque. Then, a finite-time non-singular terminal sliding mode control (NTSMC) method is introduced to achieve rapid attitude stabilization. Simulations demonstrate that under the proposed control system, the aerodynamic torque acting on the ultra-low-orbit spacecraft can be significantly reduced in a short time, and the response and high precision attitude stability can be effectively achieved.

Linear Model Predictive Control and Back-Propagation Controller for Single-Point Magnetic Levitation with Different Gap Levitation and Back-Propagation Offline Iteration

Magnetic suspension balance systems (MSBSs) need to allow vehicle models to levitate stably in different attitudes, so it is difficult to ensure the stable performance of the system under different levitation gaps using a controller designed with single balance point linearization. In this paper, a levitation controller based on linear model predictive control and a back-propagation neural network (LMPC-BP) is proposed and simulated for single-point magnetic levitation. The deviation of the BP network is observed and compensated by an expansion state observer (ESO). The iterative BP neural network model is further updated using current data and feedback data from the ESO, and then the performance of the LMPC-BP controller is evaluated before and after the update. The simulation results show that the LMPC-BP controller can achieve stable levitation at different gaps of the single-point magnetic levitation system. With further updating and iteration of the BP network, the controller anti-jamming performance is improved.

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

Multiple harmonics suppressing methods based on generalized resonant extended state observer: methodology and analysis

Multiple harmonics suppressing methods based on generalized resonant extended state observer: methodology and analysis

Multiple Observer Adaptive Fusion for Uncertainty Estimation and Its Application to Wheel Velocity Systems.

Uncertainty estimation in real-world scenarios is challenged by complexities arising from peaking phenomena and measurement noises. This article introduces a novel scheme for practical uncertainty estimation to mitigate peaking dynamics and enhance overall dynamic behavior. A fusion estimation framework for lumped uncertainties using multiple extended state observers (ESOs) is constructed, and the low-frequency adaptive parameter learning technique is employed to approximate the optimal fusion. The adaptive fusion estimation not only attenuates transient peaks in uncertainty estimation but also attains fast convergence and high accuracy under the high-gain scheduling of ESOs. Furthermore, the robustness of uncertainty estimation against measurement noises is enhanced by cascading filters in the proposed adaptive fusion framework for multiple ESOs. Extensive theoretical analyses are executed to verify practical applicability in peak and noise rejection. Finally, simulations and experiments on the wheel velocity system of a mobile robot are conducted to test the validity and feasibility.

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.

Generalized proportional–integral extended state observer-based controller design for fully actuated systems

In this paper, a feedback controller based on the extended state observer is proposed for fully actuated systems. First, a generalized proportional–integral observer is designed to estimate states and disturbances simultaneously. Using the linear parameter varying approach and the convexity principle, a linear matrix inequality condition is given to obtain the observer gains. Second, on the basis of the full-actuation property and the estimated states, a feedback controller, utilizing estimated disturbances to compensate for system disturbances, is designed to make all the states of the closed-loop system uniformly ultimately bounded. In addition, if disturbances are constant or slow time-varying, the observation errors and the states of closed-loop system are all exponentially convergent. Two illustrations are provided to show the validity and practicality of the proposed approach. Simulation results show that the estimated disturbances can follow the true values with relatively small errors, so compensating the system disturbances with estimated values can effectively reduce the ultimate bounds of states of the closed-loop system.

Prescribed performance dynamic surface control based on dual extended state observer for 2-dof hydraulic cutting arm

In tunnel section forming operations, the boom-type roadheader tracking target trajectory with high precision is greatly significant in avoiding over and under excavation and improving excavation efficiency. However, there exist complex cutting loads, measurement noise, and model uncertainties, seriously degrading the tracking performance of traditional nominal model-based controllers. Hence, this study first fully analyzes the kinematics of all members of the cutting mechanism and establishes its complete multi-body dynamic model using the Lagrange method. Furthermore, a dual extended state observer is designed to estimate the mechanical system’s angular velocity and unmodeled disturbances and actuators’ uncertain nonlinearities. In particular, introducing a nonlinear filter replaces the traditional first-order filter in dynamic surface technology, overcoming the “explosion of complexity” while attenuating the conservatism of gains tuning. Then, a dual extended state observer-based prescribed performance dynamic surface controller is developed for roadheaders for the first time. Simultaneously, integrating an improved error transformation function into controller design effectively avoids the online computational burden caused by traditional logarithmic operations. Utilizing Lyapunov theory, the cutting system’s prescribed transient response and steady-state performance are guaranteed. Finally, the proposed controller’s effectiveness is verified by comparative experiments on the roadheader.

Automated computer vision based individual salmon (Salmo salar) breathing rate estimation (SaBRE) for improved state observability

Salmon farming plays an important role in the Norwegian food industry, supplying a large portion of the world's salmon. Despite its economic importance, salmon farming faces unique biological challenges impacting the health and welfare of the fish. These factors limit the growth of salmon and the full economic potential of salmon farms, highlighting the need for advanced techniques in aquaculture to enhance productivity and sustainability. A key technical challenge is the unavailability of effective monitoring tools, leading to greater difficulties assessing the condition of salmon in water compared to the assessment of the animal stock on land-based farms. To improve the observability of salmon farms, we designed and created a computer-vision based approach for salmon breathing rate estimation (SaBRE), which allows the automatic monitoring of the respiration frequency of each individual salmon in a group of fish, seamlessly covering the entire workflow from video-stream input to the final data output (end-to-end). We thoroughly evaluated the capabilities of our method in two ways. Firstly, we performed a quantitative analysis of the constituent modules of SaBRE, revealing that all modules were highly accurate, including a salmon re-identification module that achieved an accuracy of 99.51 %. Secondly, we analyzed data from a salmon experiment with SaBRE, demonstrating that our algorithm provides high-quality respiration frequency information that can be compared with other types of experimental data to infer biological relationships. For the fish in our experiment, we observed that the ranking of the respiration frequencies in individual salmon remains relatively unchanged both in the short term and over longer periods (i.e., a salmon with a high breathing rate consistently remains among those with the highest rates, regardless of changes in the environment). Furthermore, a significant negative correlation (Pearson correlation coefficients with r values between −0.61 and −0.90 and p values below 0.01) was observed between our algorithm's estimated respiration frequency and the dissolved oxygen (dO2) content in the water. In addition, the average breathing rate of the salmon was observed to increase in response to incidents potentially causing stress.

An approach to mismatched disturbance rejection control for uncontrollable systems

This study focuses on the problem of optimal mismatched disturbance rejection control for uncontrollable linear discrete-time systems. In contrast to previous studies, by introducing a quadratic performance index such that the regulated state can track a reference trajectory and minimize the effects of disturbances, mismatched disturbance rejection control is transformed into a linear quadratic tracking problem. The necessary and sufficient conditions for the solvability of this problem over a finite horizon and a disturbance rejection controller are derived by solving a forward–backward difference equation. In the case of an infinite horizon, a sufficient condition for the stabilization of the system is obtained under the detectable condition. Additionally, in combination with the generalized extended state observer, a controller design method is proposed, and the stability analysis of the system under this controller is presented. This paper details our novel approach to disturbance rejection. Finally, four examples are provided to demonstrate the effectiveness of the proposed method.

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.

Safety-critical anti-disturbance control of tugs for collaborative berthing

It is a challenging task for large ships themselves berthing in port areas even for an experienced captain. This paper addresses the collaborative berthing by utilizing tugs maneuvering at low speeds for operating a ship. A safety-critical anti-disturbance control method is proposed for collaborative berthing subject to the contact force constraint, velocity constraints, ship constraints referring to constraining the tugs velocities in the body frame of the ship and the angular rate of the ship, as well as ocean disturbances. First, a finite-time kinematic control law is developed for each tug to track the desired position and heading prescribed by using the relative distance between two tugs and the heading of port shoreline. Next, an extended state observer (ESO) based on robust exact differentiator (RED) is used for each tug to estimate the model uncertainties and environment disturbances. Then, a safety-critical anti-disturbance control law is designed for each tug by employing an RED-based ESO. Finally, through converting the contact force constraint and ship constraints to velocity constraints on tugs, a quadratic programming problem is solved to obtain the collaborative velocity signals for achieving the collaborative operation of the connected tugs subject to velocity constraints. It is proven that the closed-loop control system is finite-time input-to-state stable and the safety during collaborative berthing can be guaranteed in the presence of environmental disturbances. Simulation results are given to substantiate the efficacy of the proposed safety-critical anti-disturbance control method of tugs for collaborative berthing.

Adaptive complementary sliding mode control of ship course under environmental disturbance

In this paper, an adaptive complementary sliding mode control strategy based on nonlinear extended state observer (NESO) is proposed for the ship course tracking control subject to environmental disturbances. By introducing the adaptive law, the system uncertainty is estimated and adjusted, so that the boundary layer of the complementary sliding mode control can realize dynamic changes and ensure the system robustness. At the same time, the auxiliary control system is introduced to compensate the complementary sliding mode control law to guarantee the system stability under finite actuation. For the environmental disturbances of the heading process, a NESO is designed for real-time estimation and compensated to the adaptive complementary sliding mode control law to improve the robustness of the system. The closed-loop stability is proved, and simulation results show that the control strategy effectively improves the tracking accuracy and robustness of the system.

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.