State Estimation Research Articles

Sensorless Position Control in High-Speed Domain of PMSM Based on Improved Adaptive Sliding Mode Observer

To improve the speed buffering and position tracking accuracy of medium–high-speed permanent magnet synchronous motor (PMSM), a sensorless control method based on an improved sliding mode observer is proposed. By the mathematical model of the built-in PMSM, an improved adaptive super-twisting sliding mode observer is constructed. Based on the LSTA-SMO with a linear term of observation error, a sliding mode coefficient can be adjusted in real time according to the change in rotational speed. In view of the high harmonic content of the output back electromotive force, the adaptive adjustment strategy for the back electromotive force is adopted. In addition, in order to improve the estimation accuracy and resistance ability of the observer, the rotor position error was taken as the disturbance term, and the third-order extended state observer (ESO) was constructed to estimate the rotational speed and rotor position through the motor mechanical motion equation. The proposed method is validated in Matlab and compared with the conventional linear super twisted observer. The simulation results show that the proposed method enables the observer to operate stably in a wide velocity domain and reduces the velocity estimation error to 6.7 rpm and the position estimation accuracy error to 0.0005 rad at high speeds, which improves the anti-interference capability.

Adaptive neural control for a class of random nonlinear Markov jump multi-agent systems with full state constraints

This paper proposes a consensus control protocol based on the adaptive backstepping technique for a class of random Markov jump multi-agent systems (MASs) with full state constraints. Each agent is described by the high-order random nonlinear uncertain system driven by random differential equations, where the random noise is the second-order stationary stochastic process. First, a distributed tracking controller is designed for Markov jump MASs, effectively handling the interaction and coupling terms between agents. Second, an appropriate tan-type barrier Lyapunov function is selected to keep the agents’ states from the violation of constraint boundaries, and the unknown nonlinear terms with Markov jump parameters are approximated by neural networks (NNs) theory. Moreover, to address the differential explosion problem, the extended state observer (ESO) is first introduced instead of employing NNs approximation or command filtering techniques. Finally, the exponentially noise-to-state stability in mean square is proved rigorously through the Lyapunov method, which guarantees all signals of the closed-loop system are bounded in probability and the tracking error converges to a small neighborhood around zero. Simulations are given to demonstrate the feasibility of theoretical results.

Application of an Improved State Feedback Control in the Selective Catalytic Reduction Denitrification Systems of Coal Power Units Under Variable Load Conditions

Selective catalytic reduction (SCR) flue gas denitrification systems are inherently complex, typically embodying characteristics of non-linearity, significant time delays, and susceptibility to multiple disturbances. In the context of coal power units engaging in deep load cycling and rapid frequency adjustment, conventional proportional-integral-derivative (PID) control struggles to meet the demands of effective control. This study introduces a control strategy that incorporates a “state observer + Linear Quadratic Regulator (LQR) state feedback + Improved Quantum Genetic Algorithm (IQGA) optimized PID”. Initially, local linear mathematical models of an SCR denitrification system at 340 MW, 450 MW, and 540 MW loads were used to design state observer and LQR state feedback control parameters for each operational condition. At a single load point, the IQGA was employed to optimize the outer loop PID parameters, followed by simulation experiments of load increases and decreases between 340 MW and 540 MW. The results demonstrated that, compared to two other strategies, the proposed approach reduced the overshoot by a minimum of 1.5% and shortened the adjustment time by 31.7% under conditions of step disturbances and internal perturbations. Throughout variable operational conditions, the strategy consistently exhibited minimal output fluctuations, rapid adjustment capabilities, strong disturbance rejection, and robust stability. This algorithm proves to be an effective method for controlling NOx concentrations, offering insights for precise ammonia injection control in future applications.

Flatness-based nonlinear internal model control with disturbances compensation via improved extended state observer for remotely operated vehicle

Flatness-based nonlinear internal model control with disturbances compensation via improved extended state observer for remotely operated vehicle

Statistical and source characterization of the 2023 Kahramanmaraş Türkiye earthquake sequence

AbstractWe studied seismic features of the 2023 Kahramanmaraş Türkiye earthquake sequence by analyzing the spatiotemporal behavior of the b- and -p values and source characteristics of the mainshocks. We complemented our study by determining the regional stress field. The b-values in the region varied from 0.45 to 1.15. We observed a slight b-value decrease (Δb≈0.2) months before the two mainshocks. Our results exhibited complex b-value patterns on the fault planes and regular aftershock productivity rates (1.14 < p < 1.25). We compare static stress drop estimates derived from effective fault dimensions to those of finite-fault dimensions. Total effective stress drops (the sum of the stress drops of all fault segments) for the earthquake doublet were almost identical (~ 2.05 MPa), while those from finite-fault dimensions are somewhat lower (0.35 and 0.96 MPa). Based on a complete stress drop case, effective seismic efficiency was 0.65 and 0.43 for both mainshocks. The amount of partial stress drop was used to discriminate between different stress drop models. No clear model is discerned for the first mainshock, but a partial or even complete stress drop, assuming finite-fault dimensions, is supported by our measurements. Stress drop estimations derived from spectral analysis (25.46 and 34.39 MPa) agreed with global studies but larger than finite and effective fault estimates. Stress inversion results indicated that in the fault segments where the mainshocks occurred, the orientation of principal axes was consistent with a strike-slip regime. Conversely, the normal-faulting regime dominates adjacent areas of the main fault system.

Observer-Based Prescribed Performance Adaptive Neural Network Tracking Control for Fractional-Order Nonlinear Multiple-Input Multiple-Output Systems Under Asymmetric Full-State Constraints

In this work, the practical prescribed performance tracking issue for a class of fractional-order nonlinear multiple-input multiple-output (MIMO) systems with asymmetric full-state constraints and unmeasurable system states is investigated. A neural network (NN) nonlinear state observer is developed to estimate the unmeasurable states. Furthermore, the barrier Lyapunov functions with the settling time regulator are employed to deal with the asymmetric full-state constraint from the fractional-order MIMO system. On this ground, the prescribed performance adaptive tracking control approach is designed, assuring that all system states do not exceed the prescribed boundaries, and the tracking errors converge to the predetermined compact sets within a predefined time. Finally, two simulation examples are presented to show the effectiveness and practicability of the proposed control scheme.

Topology in a one-dimensional plasmonic crystal: the optical approach

Abstract In this paper we study the topology of the bands of a plasmonic crystal composed of graphene and of a metallic grating. Firstly, we derive a Kronig–Penney type of equation for the plasmonic bands as function of the Bloch wavevector and discuss the propagation of the surface plasmon polaritons on the polaritonic crystal using a transfer-matrix approach considering a finite relaxation time. Second, we reformulate the problem as a tight-binding model that resembles the Su–Schrieffer–Heeger (SSH) Hamiltonian, one difference being that the hopping amplitudes are, in this case, energy dependent. In possession of the tight-binding equations it is a simple task to determine the topology (value of the winding number) of the bands. This allows to determine the existense or absence of topological end modes in the system. Similarly to the SSH model, we show that there is a tunable parameter that induces topological phase transitions from trivial to non-trivial. In our case, it is the distance d between the graphene sheet and the metallic grating. We note that d is a parameter that can be easily tuned experimentally simply by controlling the thickness of the spacer between the grating and the graphene sheet. It is then experimentally feasible to engineer devices with the required topological properties. Finally, we suggest a scattering experiment allowing the observation of the topological states.

Integral Sliding Mode‐Composite Nonlinear Feedback Control Strategy for Microgrid Inverter Systems

To enhance the dynamic performance and robustness of the voltage control system of islanded microgrid inverters, a new control strategy combining integral sliding mode (ISM) control and composite nonlinear feedback (CNF) control is proposed. In ISM control, firstly, a new reaching law is designed to improve movement quality in the reaching phase by improving the power term and introducing the inverse tangent function. Then, to improve disturbance observation accuracy, a variable gain extended state observer is designed by improving the regulation mechanism of the state variables according to deviation control. Using the idea of variable damping, linear and nonlinear feedback are combined into CNF control. Specifically, linear feedback provides a small damping ratio for faster system response, while nonlinear feedback increases the damping ratio to improve steady‐state performance. Simulation results show that the integral sliding mode‐composite nonlinear feedback (ISM‐CNF) control strategy cam balances convergence speed and chattering better and achieves higher steady‐state accuracy than conventional strategies. Moreover, ISM‐CNF control has better robustness to reference voltage variations and disturbances caused by sudden load changes. Therefore, the ISM‐CNF control strategy can accomplish voltage control of islanded microgrid inverters quickly and steadily, effectively suppressing system disturbances and enhancing stability and power quality. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

Adaptive Generalized Extended State Observer for a Single Phase PV Grid-connected System Operating Under the Sudanese-Sahelian Climate of Cameroon

Knowing the health of a system allows to guarante its efficiency and sustainability. The state observer is one of several techniques used by authors to estimate system state. This paper focuses on the problem of simultaneous states estimation of DC (Direct Current) and AC (Alternating Current) sides of a single-phase Photovoltaic (PV) grid-connected operating under the Sudanese-Sahelian climate of Cameroon. A generalized extended state observer (GESO) has been designed to simultaneously estimate the three states and the three disturbances of the system. A good estimation of the state and disturbances is achieved by the appropriate choice of the observer gain and the disturbance compensation gain resulting from the correct pole placement. The GESO robustness has been tested by varying the PV voltage and grid voltage. When there are no input fluctuations, the estimation errors of nominal states and disturbances converge to zero. The fluctuation in PV voltage resulting from partial shading has a significant impact on the boost converter current. The boost converter current varies proportionally with the drop in voltage due to partial shading from 55% to 59%. Under the grid voltage fluctuation, the boost converter current remains stable while the DC bus voltage and inverter current are significantly affected. The proposed GESO prove its robustness to perturbations from the PV array and grid side into the Single-Phase PV Grid-connected System. This paper contributes to the study of observers applied to the PV system and points the way to future work on diagnosing faults in PV systems operating in Cameroon's Sudanese-Sahelian climate.

Optimized Energy Management Strategy for an Autonomous DC Microgrid Integrating PV/Wind/Battery/Diesel-Based Hybrid PSO-GA-LADRC Through SAPF

This study focuses on microgrid systems incorporating hybrid renewable energy sources (HRESs) with battery energy storage (BES), both essential for ensuring reliable and consistent operation in off-grid standalone systems. The proposed system includes solar energy, a wind energy source with a synchronous turbine, and BES. Hybrid particle swarm optimizer (PSO) and a genetic algorithm (GA) combined with active disturbance rejection control (ADRC) (PSO-GA-ADRC) are developed to regulate both the frequency and amplitude of the AC bus voltage via a load-side converter (LSC) under various operating conditions. This approach further enables efficient management of accessible generation and general consumption through a bidirectional battery-side converter (BSC). Additionally, the proposed method also enhances power quality across the AC link via mentoring the photovoltaic (PV) inverter to function as shunt active power filter (SAPF), providing the desired harmonic-current element to nonlinear local loads as well. Equipped with an extended state observer (ESO), the hybrid PSO-GA-ADRC provides efficient estimation of and compensation for disturbances such as modeling errors and parameter fluctuations, providing a stable control solution for interior voltage and current control loops. The positive results from hardware-in-the-loop (HIL) experimental results confirm the effectiveness and robustness of this control strategy in maintaining stable voltage and current in real-world scenarios.

Prescribed Performance Control for PEM Fuel Cell Air Supply System Based on Fully Actuated Approach With Fixed Regulation Time

ABSTRACTThe problem of air feed system control with guaranteed prescribed performance and fixed time control is investigated for proton exchange membrane fuel cell (PEMFC). First of all, the original model of PEMFC air feed system is converted to fully actuated system using higher order fully actuated system (HOFAS) approach. A fixed time extended state observer (ESO) is employed to approximate the cathode pressure. The parameter uncertainty is compensated by an adaptive radial basis function neural network (RBFNN). Then a prescribed performance sliding mode controller is designed with fixed convergence time. The proposed control law could guarantee the prescribed transient response for oxygen excess ratio (OER) error under stack current changing and bounded uncertainty. Lyapunov approach is utilized to proof fixed‐time stability of proposed controller. Simulation results of the study tests illustrate that the presented controller is efficient under constant OER tracking control, constant OER tracking control with parameter perturbation, and variable OER tracking control, respectively.

Adaptive variable gain linear active disturbance rejection control parameter optimization in magnetic levitation

The magnetic levitation ball system is characterized by strong nonlinearity, multiple disturbances, and intrinsic instability, which has long been a thorny problem in the field of control engineering. The magnetic levitation system can be continuously and stably levitated, even in a complex environment. In this thesis, the model of the magnetic levitation ball system is firstly constructed according to the kinetic theory and simplified according to the actual situation. After that, a linear active disturbance rejection control (LADRC) controller for the magnetic levitation ball system is designed. Considering that there are many parameters in the LADRC controller and it is difficult to obtain the optimal control effect by manual tuning, an adaptive variable gain LADRC algorithm is proposed to optimize the control strategy of LADRC parameters. An adaptive iterative algorithm is used to adaptively adjust the bandwidth of the linear extended state observer (LESO) in LADRC, and the convergence of the algorithm is demonstrated by using the Lyapunov function. To solve the problem that the proportional and differential gains of the LADRC controller can only be adjusted unidirectionally and simultaneously, this paper proposes an error variable gain algorithm. The aim is to realize the non-unidirectional and more detailed adjustment of the controller parameters, and it is rigorously analyzed and demonstrated through simulation and experiment. The results show that the proposed algorithm is better than proportional–integral–derivative (PID), sliding mode control (SMC), and LADRC in terms of dynamic performance, steady-state performance, and anti-interference.

Observer-Based Event-Triggered Fuzzy Control for Switched Multi-Agent Systems with State Constraints and Sensor Faults

In this paper, we consider the output feedback event-triggered tracking control problem for a class of switched nonlinear multi-agent systems (MASs) with sensor faults and state constraints. For unknown nonlinear functions in the system, fuzzy logic systems are used to approximate them. To address sensor faults, sensor error compensation coefficients are designed to compensate for the errors caused by sensor faults. Meanwhile, state observer is designed to estimate the unmeasurable states in the system, providing necessary information for control design. Based on directed switching networks containing a directed spanning tree, an event-triggered output feedback controller is designed to enable the MAS to track the leader agent. By constructing a suitable barrier Lyapunov function, the stability of the system is analyzed, and it is proved that the consensus tracking can be achieved without violating the state constraints, the uniform boundedness of all the closed-loop system signals is ensured and the Zeno behavior can be eliminated. Finally, the effectiveness of the proposed algorithm is verified through a simulation example.

ТЕХНОЛОГИИ И РЕЗУЛЬТАТЫ ИССЛЕДОВАНИЙ ОБЪЕКТОВ, ПРЕДСТАВЛЯЮЩИХ ЭКОЛОГИЧЕСКУЮ УГРОЗУ, В НОВОЗЕМЕЛЬСКОЙ ВПАДИНЕ

The article is devoted to the water area research at Novaya Zemlya depression of the Kara Sea, where, according to archival data, radioactive waste was flooded. The research concerned observations of the environment state and underwater nuclear and radiation hazardous facilities, such as the nuclear fleet flooded here reactors, vessels loaded with solid radioactive waste and numerous groups of containers. The research was carried out using the technology of route sonar and video shooting based on the use of towed uninhabited underwater vehicles (BNPA) developed at IO RAS. Objects condition monitoring was provided using underwater gammaray REM spectrometers developed at the National Research Center “Kurchatov Institute”. As a result of the research, individual objects have been identified and their location archival data has been clarified. Observations of benthos and the structure of the water column in the burial area were done.

Constrained solutions of generalized coupled discrete-time periodic matrix equations with application in state observer design for linear periodic systems

PurposeIt is desired to provide a diversified iterative scheme for solving the constrained solutions of the generalized coupled discrete-time periodic (GCDTP) matrix equations from the perspective of optimization.Design/methodology/approachThe paper considers generalized reflexive solutions of the GCDTP matrix equations by applying the Jacobi gradient-based iterative (JGI) algorithm, which is an extended variant of the gradient-based iterative (GI) algorithm.FindingsThrough numerical simulation, it is verified that the efficiency and accuracy of the JGI algorithm are better than some existing algorithms, such as the GI algorithm in Hajarian, the RGI algorithm in Sheng and the AGI algorithm in Xie and Ma.Originality/valueIt is the first instance in which the GCDTP matrix equations are solved applying the JGI algorithm.

Long-term variability of ice regime characteristics in river mouth areas of the western coast of the White Sea on the background of climatic changes

The paper presents results of the comprehensive study of the long-term variability of the main elements of the ice regime for the period 1955–2020 in mouth areas of the rivers Gridina, Kuzema, Pongoma, Kem, Shuya, Nizhny Vyg, Suma, Nyukhcha and Maloshuika flowing into the White Sea on its western coast (Republic of Karelia, Arkhangelsk region). The average daily air temperatureы in sites of the State observation network of Roshydromet – marine hydrometeorological coastal stations Gridino, Kem and Onega - were used as initial information for this work. Information about the main characteristics of the ice regime (times of coming of characteristic dates of ice phenomena) of the rivers was presented by data from nine hydrological posts. The mean values of characteristics of the ice regime (average statistical dates and durations of the ice regime phases) of the rivers under consideration (except those regulated by the cascade of hydroelectric power stations). were calculated. Statistical analysis of time series of the mean air temperatures obtained for the cold season on the western coast of the White Sea and the duration of periods with ice phenomena on the above rivers made possible to reveal two quasi-homogeneous periods with a turning point in 1990. This analysis shows that the temperature background in years 1991–2020 is significantly higher (by 1.4 °C) than the similar one in 1956–1990, and at the same time the average duration of ice phenomena decreased to almost two weeks (shorter by 11 days). The regression analysis allowed finding the presence of a statistically significant negative trend in the duration of ice phenomena for the whole period (1956–2020), which is −3.3 days/10 years. At the same time, the shortening of the duration of the periods with ice phenomena is due equally to both a shift in the time of the beginning of the stable ice formations towards later dates (1.6 days/10 years), and the earlier dates of the ice phenomena end (−1.7 days/10 years).

Nonlinear Extended State Observer and Prescribed Performance Fault-Tolerant Control of Quadrotor Unmanned Aerial Vehicles Against Compound Faults

Addressing trajectory and attitude control challenges in quadrotor UAVs amid compound faults and unknown external disturbances, this paper introduces a fault-tolerant control method predicated on nonlinear extended state observers. Initially, the UAV’s dynamic model is optimized and decoupled, forming a rapid non-singular terminal sliding mode surface that circumvents the singular phenomena typical in conventional terminal sliding mode controls. A nonlinear extended state observer is then deployed to estimate the unknown states triggered by compound faults and disturbances within the control system. Theoretical analysis shows that beyond accurately estimating faults and disturbances, the proposed controllers adaptively adjust the system’s dynamic and steady-state performances, ensuring rapid stabilization of all error responses. Numerical simulations indicate significant enhancements in control precision and robustness against compound faults and disturbances, with response times and energy consumption remaining within acceptable limits for practical applications.

Linear Disturbance Observer-Enhanced Continuous-Time Predictive Control for Straight-Line Path-Following Control of Small Unmanned Aerial Vehicles

This paper studies the straight-line path-following problem on the lateral plane for fixed-wing unmanned aerial vehicles (FWUAVs) which are susceptible to uncertainties. Firstly, based on the natural frame’s location on the prescribed reference paths, the command yaw angle (which is the basis for yaw angle control system design) is solved analytically by combining it with the errors of path following, attack angle, sideslip angle, attitude angles, and geometric parameters of the prescribed reference paths. Secondly, by considering complicated dynamic characteristics, a linear extended state observer is designed to estimate uncertainties such as nonlinearities, couplings, and unmodeled dynamics whose estimated values are incorporated into the continuous-time predictive controllers for feedback compensation. Finally, numerical simulations are conducted to demonstrate the advantages of the proposed method, including reduced tracking errors and enhanced robustness in the closed-loop system, as compared to the conventional nonlinear dynamic inversion and sliding mode control approaches.

A D* orthogonal matching pursuit algorithm for time-varying channel estimation.

Orthogonal matching pursuit (OMP) combined with the A* search algorithm (A*OMP) exhibits robust reconstruction capabilities for synthesizing sparse data and signals, achieving relatively low reconstruction errors and a higher exact recovery ability than conventional OMP. However, A*OMP is only suitable for static channel estimation and cannot be applied to dynamic scenarios. This is because the channel delays for several consecutive orthogonal frequency-division multiplexing blocks per frame are similar and the path gains exhibit temporal correlation. This paper introduces a dynamic OMP approach (D*OMP) that employs a heuristic function of A*OMP and a unique reverse process, enabling sparse solutions to be identified in unknown and changing environments. The proposed method is highly practical for joint channel estimation across multiple blocks. Simulation and sea trial results indicate that D*OMP not only possesses superior channel recovery accuracy, but also has a more efficient channel reconstruction process, outperforming both A*OMP and conventional OMP.

A feedback linearization sliding mode decoupling and fuzzy anti-surge compensation based coordinated control approach for PEMFC air supply system

With the application of proton exchange membrane fuel cell (PEMFC) in the field of transportation and energy, the requirements for air flow and pressure tracking performance, efficiency and stability of PEMFC air supply system become higher and higher. To improve PEMFC cathode air flow and pressure control performance under load variation and avoid surge of air compressor, a novel feedback linearization sliding mode decoupling and fuzzy anti-surge compensation based coordinated control approach is proposed. Firstly, optimal references of oxygen excess ratio (OER) and cathode air pressure under different load are calculated based on net output power of PEMFC system analysis. Then, an extended state observer is designed to estimate the cathode air pressure and flow in real time, a feedback linearization sliding mode based decoupling controller is designed to enhance OER and air pressure tracking control performance and robustness. Considering on the operating trajectory and surge line of air compressor, a fuzzy logic based anti-surge compensator is designed to prevent air compressor from surge by compensating both air flow and pressure simultaneously. The proposed control approach is implemented in PEMFC air supply control experiments and the results demonstrates its effectiveness.