State Estimation Research Articles

Adaptive output feedback fault-tolerant control for a class of nonlinear systems based on a sensor fusion mechanism

This paper investigates an output feedback adaptive fault-tolerant tracking control for a class of nonlinear systems with system nonlinearities, sensor failures and external disturbances, in which sensor redundancy is employed to enhance measurement reliability. A sensor fusion mechanism, together with a novel history-based weighted average algorithm is first designed to fuse all sensor outputs. Then, an adaptive controller based on the sensor fusion output, a dynamic gain and a state observer is constructed to handle all the uncertainties caused by system nonlinearities, external disturbances, sensor failures and fusion mechanism. It is shown that by using the proposed scheme, the closed-loop system is stable, the sensor fusion mechanism can eliminate the effects of faulty sensors, and the real tracking error can be driven into a small compact set mainly affected by the fusion error. Experimental results are accomplished to validate the proposed scheme.

Distributed optimization for the trade-off between state-of-charge balancing and strategic battery usage in a networked BESS

Battery energy storage systems (BESSs) are critical components of microgrids. In this paper, we consider the dynamics of discharging batteries and propose a model for making a trade-off between balancing the state-of-charge (SoC) of battery units in a networked BESS and strategic usage of these battery units. To realize such a trade-off, we formulate an optimization problem that takes the total discharging power requirement and the limitations on individual battery units into account. The dynamics of SoC rely on the solution to the optimization problem. Furthermore, we reformulate the optimization problem in a distributed manner, employing estimators for certain global variables, and analyze the gap between the optimum with global information and the optimum with estimation in the steady state. Our simulation results verify that the trade-off between SoC balancing and strategic usage of battery units can be adjusted by tuning the weighting parameters in the optimization problems.

Cost and carbon-intensity reducing innovation in biofuels for road transportation

The transportation sector is the leading contributor to greenhouse gas emissions in the United States. Incentives for biofuel production in the form of direct subsidies and tradable performance standards have been substantial. Although research suggests such policies are less cost-effective at reducing emissions than an explicit carbon price, a dynamic assessment that accounts for innovation may differ substantially from static cost estimates. This study evaluates cost and carbon-intensity reducing innovation in the U.S. biofuel industry and estimates the value of social benefits for comparison with the observed level of policy support.Using multi-factor experience curves, this study finds that cost declines have been significant in ethanol production, with an estimated learning rate of 21.8%. However, learning in biodiesel and renewable diesel has been much less pronounced, at 3.23% and 1.33%, respectively. Reductions in carbon intensity are found to be largely related to feedstock choice rather than improvements in production processes. Based on the innovation rates identified in this study, current policy support for biofuels from stacked incentives is found to exceed the social benefits. This misalignment calls for a reassessment of biofuel policies to ensure they are economically and environmentally justified.

Convergence rate sliding mode control of permanent magnet synchronous motor based on expanded sliding mode observer

The aim is to address the limitations of conventional sliding mode control for PMSM, namely the problems of sliding mode oscillation, load disturbance, and poor control accuracy. An improved sliding mode control strategy for PMSM is proposed based on the novel convergence rate and extended state observer. Firstly, an integral sliding mode velocity loop controller is designed based on the novel convergence rate to solve the sliding mode vibration problem. The stability of the proposed control strategy is derived by the Lyapunov criterion. Secondly, an extended state observer capable of estimating the disturbance torque is designed for the purpose of disturbance compensation. The disturbance torque estimate is then used as a feed-forward signal for the sliding mode velocity loop controller. A fast second-order sliding mode controller is used for the current loop to avoid the potential introduction of noise and disturbances. The simulation results show that compared to alternative control strategies, the proposed control strategy enables the PMSM to respond effectively to start-up and load disturbances, significantly reduces the sliding mode jitter, and improves both the torque estimation accuracy and the system control accuracy.

Control Strategy for Disc Coreless Permanent Magnet Synchronous Motor with LC Filter

The disc coreless permanent magnet synchronous motor has the advantages of a short axial size, high power density, and small volume. Due to the coreless structure, its inductance is very small, which results in a serious current ripple and an unacceptable torque ripple if driven from a conventional inverter. This can be solved by installing an LC filter between the inverter and the motor. However, an undesirable resonance phenomenon is induced by the LC filter. In this paper, a new capacitive current feedback active damping (CCFAD) strategy is proposed. Instead of current sensors in the capacitor branch, a state observer is introduced to estimate the capacitance current. The observer is designed with double sliding mode surfaces, which reduces the order of the system. Compared to conventional capacitive current feedback, no additional current sensors are required, reducing the system cost. Besides the resonant harmonics, the phase current contains obvious fifth and seventh harmonics due to the special plane structure of the rotor. The proportional-integral-resonance (PIR) controller, instead of the traditional PI controller, is designed to suppress lower order harmonics. The experiment results show that current ripples due to resonance and rotor structure are suppressed significantly.

Observation of two-dimensional time-reversal broken non-Abelian topological states

Going beyond the conventional theory, non-Abelian band topology reveals the global quantum geometry of multiple Bloch bands and unveils a new paradigm for topological physics. However, to date, experimental studies on non-Abelian topological states beyond one dimension are still restricted to systems with time-reversal (T\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${{\\mathcal{T}}}$$\\end{document}) symmetry. Here, exploiting a designer gyromagnetic photonic crystal, we find rich T\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${{\\mathcal{T}}}$$\\end{document}-broken non-Abelian topological phases and their transitions with an unexpected connection to multigap antichiral edge states. By in-situ tuning the magnetic field in the gyromagnetic photonic crystal, we can create, braid, merge, and split the non-Abelian topological nodes in a unique way. Alongside this process, the multigap antichiral edge states can be tuned versatilely, giving rise to topological edge waveguiding with frequency-dependent directionality. These findings open a new avenue for non-Abelian topological physics and topological photonics.

Semimetallization induced Hall anomaly in doped polymers

Hall anomaly related to deviations in Hall carrier concentrations have always been recognized as signs of charge incoherence in organic semiconductors, which lack ordered lattices. In this paper, we show that the Hall carrier concentration anomaly in doped polymers might be a result of semimetallization. Based on the observation of semimetal-like states, a dual-channel model, in which electrons and holes compensate the Hall voltages of each other, is employed to account for the anomaly. Observations of possible nonlinear Hall effect and temperature dependent Hall signs further support the origin of Hall anomaly from semimetal states. Published by the American Physical Society 2024

Adaptive fuzzy inverse optimal output feedback decentralised prescribed performance control for interconnected nonlinear systems

This paper investigates an adaptive fuzzy inverse optimal output feedback decentralised prescribed performance control problem for interconnected systems. Firstly, the fuzzy logic systems (FLSs) are utilised to model unknown nonlinear functions in each subsystem and then an auxiliary system is constructed. Based on the auxiliary system, a fuzzy decentralised state observer is formulated to estimate unmeasurable states. Secondly, by applying the adaptive backstepping design technique and the inverse optimal control theory, an adaptive fuzzy inverse optimal output feedback decentralised controller is ultimately designed. It is proved that the proposed adaptive fuzzy inverse optimal decentralised controller not only minimises the cost function but also ensures that all variables of the control systems are semi-globally ultimately bounded and that the tracking errors converge to a small residual set with the prescribed performance bound. Finally, the effectiveness of the proposed control approach is verified by a nonlinear inverted pendulum large-scale system.

Pressure Control of Multi-Mode Variable Structure Electro-Hydraulic Load Simulation System.

During the loading process, significant external position disturbances occur in the electro-hydraulic load simulation system. To address these position disturbances and effectively mitigate the impact of uncertainty on system performance, this paper first treats model parameter uncertainty and external disturbances as lumped disturbances. The various states of the servo valve and the pressures within the hydraulic cylinder chambers are then examined. Building on this foundation, the paper proposes a nonlinear multi-mode variable structure independent load port electro-hydraulic load simulation system that is tailored for specific loading conditions. Secondly, in light of the significant motion disturbances present, this paper proposes an integral sliding mode active disturbance rejection composite control strategy that is based on fixed-time convergence. Based on the structure of the active disturbance rejection control framework, the fixed-time integral sliding mode and active disturbance rejection control algorithms are integrated. An extended state observer is designed to accurately estimate the lumped disturbance, effectively compensating for it to achieve precise loading of the independent load port electro-hydraulic load simulation system. The stability of the designed controller is also demonstrated. The results of the simulation research indicate that when the command input is a step signal, the pressure control accuracy under the composite control strategy is 99.94%, 99.86%, and 99.76% for disturbance frequencies of 1 Hz, 3 Hz, and 5 Hz, respectively. Conversely, when the command input is a sinusoidal signal, the pressure control accuracy remains high, measuring 99.94%, 99.8%, and 99.6% under the same disturbance frequencies. Furthermore, the simulation demonstrates that the influence of sensor random noise on the system remains within acceptable limits, highlighting the effective filtering capabilities of the extended state observer. This research establishes a solid foundation for the collaborative control of load ports and the engineering application of the independent load port electro-hydraulic load simulation system.

Modeling and Active Vibration Control of Flexible Robotic Arm and Its Application in Agricultural Water Conservancy Field

In order to improve the modeling and active vibration control effectiveness of flexible manipulators, this paper combines big data technology to study the modeling and active control of flexible manipulators. In addition, this article decouples the dynamic model of the flexible joint manipulator based on voltage control method, redefines the control input of the system as the voltage of the joint motor, and uses a nonlinear state observer to control the flexible link manipulator. The control strategy proposed in this article is based on a nonlinear state observer, combined with input-output control, to achieve tracking of joint angular displacement in a flexible linkage manipulator. Through experimental research, it can be seen that the big data based modeling and active vibration control research system for flexible robotic arms proposed in this paper can effectively improve the motion control effect of flexible robotic arms.

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.

Finite-time output consensus control of nonlinear uncertain multi-agent systems via extended state observer

This paper investigates the finite-time consensus problem for nonlinear uncertain multi-agent systems (MASs) affected by mismatched uncertainties and disturbances, as well as partial measurements. An active disturbance rejection control (ADRC) strategy that combines the backstepping technique with an extended state observer is proposed. Firstly, a finite-time state feedback controller is designed to ensure consensus when ignoring external disturbances. Secondly, a finite-time extended state observer (FTESO) is proposed for each agent to estimate the unavailable state and external disturbances, and a feedforward-feedback composite consensus controller is then developed. Furthermore, utilizing the homogeneous domination approach, we demonstrate that the proposed composite control strategy guarantees finite-time output consensus. Finally, the effectiveness of the proposed composite control strategy is illustrated using a multiple-manipulator system as an example.

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.

Trajectory Aware Deep Reinforcement Learning Navigation Using Multichannel Cost Maps

Deep reinforcement learning (DRL)-based navigation in an environment with dynamic obstacles is a challenging task due to the partially observable nature of the problem. While DRL algorithms are built around the Markov property (assumption that all the necessary information for making a decision is contained in a single observation of the current state) for structuring the learning process; the partially observable Markov property in the DRL navigation problem is significantly amplified when dealing with dynamic obstacles. A single observation or measurement of the environment is often insufficient for capturing the dynamic behavior of obstacles, thereby hindering the agent’s decision-making. This study addresses this challenge by using an environment-specific heuristic approach to augment the dynamic obstacles’ temporal information in observation to guide the agent’s decision-making. We proposed Multichannel Cost Map Observation for Spatial and Temporal Information (M-COST) to mitigate these limitations. Our results show that the M-COST approach more than doubles the convergence rate in concentrated tunnel situations, where successful navigation is only possible if the agent learns to avoid dynamic obstacles. Additionally, navigation efficiency improved by 35% in tunnel scenarios and by 12% in dense-environment navigation compared to standard methods that rely on raw sensor data or frame stacking.

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

This paper proposes an improved Active Disturbance Rejection Control (ADRC) scheme for nonlinear control-affine systems, achieving superior robustness/ performance trade-offs. By leveraging the flatness property within a closed-loop structure, Nonlinear Internal Model Control (NLIMC) is employed as a model-based controller utilises partial plant knowledge to enhance control performance and eliminate static tracking errors. Subsequently, an improved Extended State Observer (IESO) is incorporated with NLIMC to address unmodelled dynamics, disturbances, and parameter uncertainties in addition to the full state's estimation. The IESO's added hyperbolic function dynamic ensures higher sensitivity and accuracy compared to conventional linear ESOs, especially in case of large errors. Control performances are evaluated through a Remotely Operated Vehicle (ROV) application. Numerical simulations have been carried out and the results confirm its effectiveness and superiority compared to Ocean Disturbance Observer-based Double-loop Sliding Mode Controller (ODSMC) and the linear ADRC.

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.

Enhancing the motion performance of 3-DOF micro/nano-manipulators facing thermo-piezoelectric-mechanical coupling effects

Achieving ultra-high accuracy manipulation with piezoelectric micro/nano-manipulators is significantly hindered by thermal loads uncertainties in the digital control diagram. To address the problem, we propose a sampled-data fixed-time extended state observer based control strategy to enhance the manipulation performance. Initially, we delve into the dynamical model of the micro/nano-manipulator, examining the thermo-piezoelectric-mechanical coupling effects. Following the kinematics decoupling of the 3-DOF model, sampled-data fixed-time extended state observer based control strategy is designed to estimate the system states and total disturbances including thermal impacts and system uncertainties in the sampled-data control architecture, where an output predictor is integrated to forecast system output between consecutive sampling instances, while the observation gains are meticulously crafted to ensure a fixed-time convergence in estimation. Leveraging this estimation framework, a fixed-time feedback controller is designed to compensate the total disturbance and control the system, enabling the system to achieve a tracking convergence, irrespective of the inconsistent initial values stemming from thermal effects disparities. Finally, experimental validation on a piezoelectric thin-sheet-actuated 3-DOF flexible micro/nano-manipulator verifies the effectiveness of the proposed control strategy. Notably, we have observed a minimum enhancement of 26% in spatial trajectory positioning and scanning accuracy in the context of varying thermal loads.

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.