Input Estimation Algorithm Research Articles

Model‐free adaptive control for ultracapacitor based three‐phase interleaved bidirectional DC–DC converter

AbstractThe lack of an accurate mathematical model of non‐linear systems, such as bidirectional DC–DC converters, brings certain limits to design a formidable controller for such systems. This article proposes a model‐free adaptive control (MFAC) approach for an ultracapacitor based three‐phase interleaved bidirectional DC–DC converter (BDC) to ameliorate the controller design limitations due to the demand for system model information. This article designs a pseudo gradient (PG) estimation algorithm based on the input and output (I/O) data of a three‐phase interleaved BDC and designs a control input estimation algorithm through the PG estimation value, then, the design of a model‐free adaptive controller is completed. Moreover, the Black‐box technique is applied to the system, which does not require a precise mathematical model resulting in a lower computational burden, faster dynamics, and easier implementation. Furthermore, the feasibility and effectiveness of the proposed MFAC scheme are experimentally tested and verified on an energy storage system microgrid platform.

A Key-Agreement Scheme for Cyber–Physical Systems

Traditional cryptographic approaches may not always be suitable for cyber–physical systems (CPSs). In this correspondence, we present a control-theoretic approach allowing a networked controller and a smart actuator of a stochastic CPS to agree on a common secret key without resorting to classical cryptographic approaches. More precisely, by utilizing the asymmetry in the system model knowledge available to the control system defender and to the eavesdropper, we propose a key-agreement scheme utilizing a simultaneous input and state estimation algorithm. The validity of the proposed solution is shown through a numerical example.

An Online Prediction of Capacity and Remaining Useful Life of Lithium-Ion Batteries Based on Simultaneous Input and State Estimation Algorithm

For lithium-ion batteries used in the electric vehicles, accurate prediction of capacity and remaining useful life online is extremely important. However, most of the research works focus on the prediction accuracy but neglect the complexity of the test environment, which makes many methods show poor robustness in application. To solve the problem, in this article, we first introduce the simultaneous input and state estimation algorithm into the online prediction of state-of-charge and capacity, and combine the Gauss–Hermite extended particle filter to predict the remaining useful life. By setting different gradients of state noises in experiments, the proposed algorithm demonstrates the best accuracy and robustness in comparison with other algorithms. Through the two-factor authentication in simulations, the maximum error of capacity estimation is 35 mAh. For the prediction of remaining useful life, the minimum relative error of the proposed method is 0.4%. Therefore, the high accuracy and strong robustness of the proposed algorithm are verified.

Shape-constrained Input Estimation for Efficient Multi-shaker Vibration Testing

Multi-shaker vibration testing is gaining interest from structural dynamics test engineers as it can provide a much more accurate match to complicated field vibration responses than traditional single-axis shaker tests. However, the force capabilities of the small modal shakers typically used in multi-shaker vibration tests has limited the achievable response levels. To date, most multi-shaker vibration tests have been performed using a variety of standard, commercially-available control systems. While these control systems are adequate for a wide range of multiple-input/multiple-output tests, their control algorithms have not been tailored for the specific problem of multi-shaker vibration tests: efficiently coordinating the various shakers to work together to achieve a desired response. Here, a new input estimation algorithm is developed and demonstrated using simulations and actual test data. This algorithm, dubbed shape-constrained input estimation, is shown to effectively coordinate multiple shakers using a set of constraint vectors based on the deflection shapes of the test structure. This is accomplished by using the singular vector shapes of the system frequency response matrix, which allows the constraint vectors to automatically change as a function of frequency. Simulation and test results indicate a significant reduction in the input forces required to achieve a desired response. The results indicate that shape-constrained input estimation is an effective method to achieve higher response levels from limited shaker forces which will enable higher level multi-shaker vibration tests to be performed.

Iterative Approximate Nonlinear Inference via Gaussian Message Passing on Factor Graphs

Factor graphs are graphical models able to represent the factorization of probability density functions. By visualizing conditional independence statements, they provide an intuitive and versatile interface to sparsity exploiting message passing algorithms as a unified framework for constructing algorithms in signal processing, estimation, and control in a mix-and-match style. Especially, when assuming Gaussian distributed variables, tabulated message passing rules allow for easy automated derivations of algorithms. This letter’s contribution consists in the combination of statistical or Jacobian-based linearization approaches to handling nonlinear factors with efficient message parametrizations in a Gaussian message passing setting. Tabulated message passing rules for a multivariate nonlinear factor node are presented that implement a re-linearization about the most current belief (marginal) of each adjacent variable. When utilized in a nonlinear Kalman smoothing setting, the iterated nonlinear modified Bryson–Frazier smoother is recovered, while retaining the flexibility of the factor graph framework. This application is illustrated by deriving an input estimation algorithm for a nonlinear system.

Optimal Position and Velocity Estimation for Multi-USV Positioning Systems with Range Measurements

This paper investigates the problem on simultaneously estimating the velocity and position of the target for range-based multi-USV positioning systems. According to the range measurement and kinematics model of the target, we formulate this problem in a mixed linear/nonlinear discrete-time system. In this system, the input and state represent the velocity and position of the target, respectively. We divide the system into two components and propose a three-step minimum variance unbiased simultaneous input and state estimation (SISE) algorithm. First, we estimate the velocity in the local level plane and predict the corresponding position. Then, we estimate the velocity in the heave direction. Finally, we estimate the 3-dimensional (3D) velocity and position. We establish the unbiased conditions of the input and state estimation for the MLBL system. Simulation results illustrate the effectiveness of the problem formulation and demonstrate the performance of the proposed algorithm.

Adaptive Strong Tracking Square-Root Cubature Kalman Filter for Maneuvering Aircraft Tracking

A novel strong tracking square-root cubature Kalman filter (SCKF) based on the adaptive current statistical (CS) model is proposed aiming at the maneuvering aircraft tracking problem. The Jerk input estimation is introduced on the basis of the modified input estimation algorithm in order to make the connection with the state process noise and the state error covariance matrix. Thus, the online-adaptive adjustment of the CS model is achieved. Additionally, the introduced position of the fading factor is re-deduced and a novel calculation method is designed in order to overcome the invalidity problem of the traditional fading factor. Two simulation scenarios are conducted to verify the effectiveness of the proposed algorithm. The simulation results show that the proposed algorithm possesses better adaptability and tracking precision than the two state-of-the-art single model filters. Moreover, the proposed algorithm decreases the runtime by 40% while maintaining the comparable performance compared with the interacting-multiple-model SCKF.

Effects of wall thickness and material property on inverse heat conduction analysis of a hollow cylindrical tube

In this study, we examined the effects of a hollow cylindrical tube’s thickness and material properties on estimated time delay and waveform distortion in a one-dimensional inverse heat transfer analysis model using the thermal resistance method and an input estimation algorithm. Results indicated a persistent time delay for various heat flux amounts applied to different tube thicknesses. As the tube thickness increased, the numerically determined temperature data also experienced a time delay, which affected the inverse heat transfer response curve. Results also indicated that the transient heat flux waveform estimated for different material properties showed higher levels of distortion for materials having relatively low thermal conductivity. These materials also exhibited greater time delays. To address these issues, we applied a Fourier number (a dimensionless number representing the tube’s thickness and material properties) and proposed an equation to calculate sharpness, which can subsequently be used to predict probable time delays and heat flux waveform distortion. In conclusion, a correction is required when a low Fourier number is used in inverse heat transfer analysis.

Three-dimensional inverse heat conduction modeling of a multi-layered hollow cylindrical tube using input estimation algorithm and thermal resistance network

High-temperature gas flow can cause melt, crack, erosion, and wear of tubes. In order to reduce these side effects, the inner wall of a hollow cylindrical tube is generally coated with a resistive material, for example, a steel pipe coated with a chrome layer. In addition, it is important to know heat flux applied to the tube wall and temperature distribution in the tube wall for appropriate design of the hollow cylindrical tube. In this study, three-dimensional inverse heat conduction modeling of a multi-layered hollow cylindrical tube was conducted. The thermal resistance network (TRN) scheme was employed to solve the heat conduction in the tube. The temperature distribution in the tube was estimated from a measured temperature on the outer wall of the tube by Kalman filter. At the same time, unknown heat flux on the inner wall of the tube was calculated by the recursive least squares algorithm.

Prediction of time-varying heat flux along a hollow cylindrical tube wall using recursive input estimation algorithm and thermal resistance network method

In this study, a model using the thermal resistance network method and the recursive input estimation algorithm was developed to analyze two-dimensional inverse heat conduction problems. The recursive input estimation algorithm consisted of the Kalman filter and the real-time least squares. A two-layered hollow cylindrical tube was assumed with the convection heat transfer boundary condition at the outer wall of the tube. Using the model developed in this study, an unknown time-varying heat flux at the inner wall of the tube was predicted from the known temperatures at some positions on the outer wall of the tube in the longitudinal direction. In the process of predicting the unknown heat flux, the temperature distribution in the two-layered hollow cylindrical tube wall was also obtained. The present model was able to accurately predict the unknown time-varying heat flux profiles of square waves or sinusoidal-like waves with and without spatial variations in the longitudinal direction.

Joint State and Input Estimation for One-Dimensional Heat Conduction

This paper presents a joint state and input estimation algorithm for the one-dimensional heat-conduction problem. A computationally efficient method is proposed in this work to solve the inverse heat-conduction problem (IHCP) using orthogonal collocation method (OCM). A Kalman filter (KF) algorithm is used in conjunction with a recursive-weighted least-square (RWLS)-based method to simultaneously estimate the input boundary condition and the temperature field over the heat-conducting element. A comparison study of the algorithm is shown with explicit finite-difference method (FDM) of approximation and analytical solution of the forward problem, which clearly reveals the high accuracy with lower-dimensional modeling. The estimation results show that the performance of the estimator is robust to noise sensitivity up to a certain level, which is practically acceptable.

Inverse heat transfer analysis of multi-layered tube using thermal resistance network and Kalman filter

In this study, a hollow cylindrical tube with a coating layer on its inner wall was considered. The thermal resistance network method was employed to solve the heat conduction in the tube. Unknown heat flux on the inner wall of the tube was estimated from measured temperature on the outer wall of the tube by the recursive input estimation algorithm consisting of the Kalman filter and real-time least squares. Assuming various operating conditions, the performance of the model developed in this study was evaluated.

Design of sensor networks for instantaneous inversion of modally reduced order models in structural dynamics

In structural dynamics, the forces acting on a structure are often not well known. System inversion techniques may be used to estimate these forces from the measured response of the structure. This paper first derives conditions for the invertibility of linear system models that apply to any instantaneous input estimation or joint input-state estimation algorithm. The conditions ensure the identifiability of the dynamic forces and system states, their stability and uniqueness. The present paper considers the specific case of modally reduced order models, which are generally obtained from a physical, finite element model, or from experimental data. It is shown how in this case the conditions can be directly expressed in terms of the modal properties of the structure. A distinction is made between input estimation and joint input-state estimation. Each of the conditions is illustrated by a conceptual example. The practical implementation is discussed for a case study where a sensor network for a footbridge is designed.

Compensation and Estimation of Friction by Using On-Line Input Estimation Algorithm

In this paper, a compensation method of nonlinear friction using on-line input estimation (IE) method is developed. To illustrate the validity and performance of the proposed algorithm applied to positioning system, comparisons with the results using the Gomonwattanapanich method and robustness analysis are performed. The simulation result shows that the estimated friction torque does not need any assumption in the pattern of friction model in advance, the proposed algorithm has consistent robustness to diverse friction characteristics, and the method can significantly improve the performance of a control system.

Nonlinear structural system wind load input estimation using the extended inverse method

This study develops an extended inverse input estimation algorithm with intelligent adaptive fuzzy weighting to effectively estimate the unknown input wind load of nonlinear structural systems. This algorithm combines the extended Kalman filter and recursive least squares estimator with intelligent adaptive fuzzy weighting. This study investigated the unknown input wind load applied on a tower structural system. Nonlinear characteristics will exist in various structural systems. The nonlinear characteristics are particularly more obvious when applying larger input wind load. Numerical simulation cases involving different input wind load types are studied in this paper. The simulation results verify the nonlinear characteristics of the structural system. This algorithm is effective in estimating unknown input wind loads.

A Modified Online Input Estimation Algorithm for Inverse Modeling of Steel Quenching

The surface temperatures and surface heat flux of a 1080 steel cylinder during the quenching process are estimated by the application of inverse heat transfer analysis. The conventional online input estimation algorithm is modified and used for the first time to handle this coupled nonlinear problem. The nonlinearity of the problem is treated explicitly, which results in a noniterative algorithm suitable for real-time control of the steel quenching process. The results obtained are validated using experimental data and numerical results obtained by solving the direct problem. Results show that the algorithm can estimate the convective heat transfer coefficient efficiently.

Reentry vehicle tracking in a cluttered environment

Recently, reentry vehicle (RV) tracking has become an important issue because of its high speed and high-elevation angle. The extended Kalman filter (EKF) with input estimation (IE) was developed for an RV tracking in a clear environment. However, radar tracking suffers from clutters in the form of unwanted, unavoidable and unpredictable signal echoes from sea, land and weather. The Hough transform is a well-known technique for identifying a straight line in noisy environments. This technique can be utilised to design a clutter elimination algorithm for clutters censoring. This study presents a novel tracking algorithm by integrating the EKF, IE and clutter elimination algorithm to track the RVs in cluttered environments. To ensure an RV to be tracked continuously, the estimation errors between the predicted and the real trajectories should be within a range cell. Simulation results reveal that the proposed algorithm can provide an acceptable accuracy to maintain the track. Although clutters cannot be totally censored, the effects of resting clutters can be reduced to an acceptable range. The proposed algorithm may thus help radars in achieving continuous tracking. In conclusion, this algorithm deserves further study and application.

Numerical study of the modeling error in the online input estimation algorithm used for inverse heat conduction problems (IHCPs)

A numerical investigation has been conducted to study the effect of modeling error in the state equation on the performance of the online input estimation algorithm in its application to the inverse heat conduction problems. This modeling error is used as a tuning parameter known as the stabilizing parameter in the online input estimation algorithm of the inverse heat conduction problems. Three different cases which cover most forms of the boundary heat flux functions have been considered. These cases are: square wave, triangular wave and mixed wave heat fluxes. The investigation has been carried for a one dimensional inverse heat conduction problem. Temperature measurements required for the inverse algorithm was generated by using a numerical solution of the direct heat conduction problem employing the three boundary heat flux functions. The most important finding of this investigation is that a robust range of the stabilizing parameter has been found which achieves the desired trade-off between the filter tracking ability and its sensitivity to measurement errors. For all three considered cases, it has been found that there is a common optimal value of the stabilizing parameter at which the estimate bias is minimal. This finding is very important for practical applications since this parameter is unknown practically and this study provides a needed guidance for assuming this parameter. The effect of changing other important parameters in the online input estimation algorithm on its performance has also been studied in this investigation.

Load torque estimation problem for electric machinery servo systems by an inverse method

This study analyzes a load torque estimation problem for an electric machinery servo system using the recursive input estimation (RIE) algorithm. This study also presents four novel estimation models, which have different numbers of sensors, considers the process and the measurement noise to estimate load torque without a torque sensor. The proposed algorithm is a novel estimation technique for solving force feedback and state estimation problems for an electric machinery servo control system. In this work, the DC servomotor system is utilized as an electric machinery servo system, and four important RIE characteristics were verified by numerical simulation results: (i) Adaptive forgetting factor in the RIE algorithm can estimate load torque more effectively than constant forgetting factor; (ii) The significant variation of control input and/or load torque impact affects the estimation precision; (iii) The high‐performance load torque estimation model can be established based only on the given control input and techogenerator sensor; (iv) Using different degrees of model errors, the estimation of the performance tendency toward good or bad for control input and load torque are the same. This characteristic shows that good estimated performance for an unknown load torque can be confirmed based on the strength of good control input estimated performance. In other words, model error can be identified via the deterministic control input and its estimation results. The proposed models may have practical applications for solving disturbance compensation problems in electric machinery servo systems.

Inverse active wind load inputs estimation of the multilayer shearing stress structure

This research investigates the adaptive input estimation method applied to the multilayer shearing stress structure. This method is to estimate the values of wind load inputs by analyzing the active reaction of the system. The Kalman filter without the input term and the adaptive weighted recursive least square estimator are two main portions of this method. The innovation vector can be produced by the Kalman filter, and be applied to the adaptive weighted recursive least square estimator to estimate the wind load input over time. This combined method can effectively estimate the wind loads to the structure system to enhance the reliability of the system active performance analysis. The forms of the simulated inputs (loads) in this paper include the periodic sinusoidal wave, the decaying exponent, the random combination of the sinusoidal wave and the decaying exponent, etc. The active reaction computed plus the simulation error is regard as the simulated measurement and is applied to the input estimation algorithm to implement the numerical simulation of the inverse input estimation process. The availability and the precision of the input estimation method proposed in this research can be verified by comparing the actual value and the one obtained by numerical simulation.