1,721,016 research outputs found
A Practical Distributed Mobile Network Protocol and Its Application to Drone Formation Control
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Resolver angle estimation using parameter and state estimation
No full textIn this paper, a new type of a resolver angle estimator that utilizes a combined parameter and state estimation scheme is proposed. A state-space model of a resolver is first developed with unknown parameters. Least square estimation is employed to obtain some unknown model parameters by using the measurements up to the current time. Based on the state-space model with estimated parameters, a constrained state estimator with finite memory is constructed to estimate the resolver angle. It is shown through simulation that the proposed scheme is very effective in suppressing noise and overcoming amplitude and phase imbalances compared with common angle tracking observers.111sciescopu
Novel data-efficient mechanism-agnostic capacity fade model for Li-ion batteries
No full textAccurate capacity fade prediction of Li-ion batteries is essential to reduce the time spent by manufacturers in performing quality assurance tests and to ensure the safety and durability of these batteries for end users. Various complicated aging mechanisms and the resulting capacity fade phenomena of Li-ion batteries make such predictions challenging; thus, mechanism-agnostic approaches using empirical and data-driven models are considered to be promising. This article proposes a mechanism-agnostic capacity fade empirical model called aging density function model (ADFM) for Li-ion batteries. Developed by innovating existing empirical models, the proposed ADFM predicts capacity fades for arbitrary battery input current trajectories, requires no additional experiments at the prediction phase, and reflects real batteries phenomena such as the varying amount of capacity fade for each cycle. As the proposed ADFM could generate a large amount of synthetic data, it was augmented with Bayesian neural networks (BNNs) to enhance its data efficiency. As a result, it can completely utilize the experimental data and achieve reasonable prediction accuracy regardless of the amount of experimental data. This BNN-augmented ADFM can also provide the reliability of the capacity fade prediction to ensure safety. Through charge/discharge cycle tests with an NCM/graphite Li-ion battery, the proposed BNN-augmented ADFM was shown to provide good performance in terms of the capacity fade prediction accuracy, with a mean absolute error of approximately 0.5% and maximum absolute error of approximately 2.5%.11Nsciescopu
A New Adaptive Sliding-Mode Control Scheme for Application to Robot Manipulators
No full textThis paper presents a new adaptive slidingmode control (ASMC) scheme that uses the time-delay estimation (TDE) technique, then applies the scheme to robot manipulators. The proposed ASMC uses a new adaptive law to achieve good tracking performance with small chattering effect. The new adaptive law considers an arbitrarily small vicinity of the sliding manifold, in which the derivatives of the adaptive gains are inversely proportional to the sliding variables. Such an adaptive law provides remarkably fast adaptation and chattering reduction near the sliding manifold. To yield the desirable closed-loop poles and simplify a complicated system model by adapting feedback compensation, the proposed ASMC scheme works together with a pole-placement control (PPC) and a TDE technique. It is shown that the tracking errors of the proposed ASMC scheme are guaranteed to be uniformly ultimately bounded (UUB) with arbitrarily small bound. The practical effectiveness and the fast adaptation of the proposed ASMC are illustrated in simulations and experiments with robot manipulators, and compared with those of an existing ASMC.115830sciescopu
Improved Receding Horizon Fourier Analysis for Quasi-periodic Signals
Get PDFIn this paper, an efficient short-time Fourier analysis method for the quasi-periodic signals is proposed via an optimal fixed-lag finite impulse response (FIR) smoother approach using a receding horizon scheme. In order to deal with time-varying Fourier coefficients (FCs) of quasi-periodic signals, a state space model including FCs as state variables is augmented with the variants of FCs. Through an optimal fixed-lag FIR smoother, FCs and their increments are estimated simultaneously and combined to produce final estimates. A lag size of the optimal fixed-lag FIR smoother is chosen to minimize the estimation error. Since the proposed estimation scheme carries out the correction process with the estimated variants of FCs, it is highly probable that the smaller estimation error is achieved compared with existing approaches not making use of such a process. It is shown through numerical simulation that the proposed scheme has better tracking ability for estimating time-varying FCs compared with existing ones.111Ysciescopuskc
A continuous finite-time convergence fixed-lag FIR smoother using multiple IIR filters
No full textIn this paper, we propose a continuous finite-time convergence finite impulse response (FIR) fixed-lag smoother using multiple, or more than two, computationally efficient IIR filters. We describe the optimal design to improve and further optimize an existing scheme based on two IIR filters. Multiple IIR filters are utilized to minimize the estimation error variance of the proposed smoother under the condition that its estimate converges to a real state in a finite time. As the number of adopted IIR filters increases, the proposed smoother improves and its performance approaches that of the heavy computational fixed-lag minimum variance FIR smoother. By choosing the appropriate number of IIR filters, we can balance the trade-off between improved accuracy and increased implementation costs. To realize the optimal design of IIR filters with the limited number of IIR filters, their gains are determined using a particle swarm optimization scheme. Numerical examples are used to show that with an increasing number of IIR filters, the estimation error variance decreases monotonically while guaranteeing finite-time convergence.(c) 2022 The Franklin Institute. Published by Elsevier Ltd. All rights reserved.11Nsciescopu
Event-triggered Actor Critic through Entropy Regularization for Highly Sparse Robot Controller
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Reliable Online Parameter Identification of Li-Ion Batteries in Battery Management Systems Using the Condition Number of the Error Covariance Matrix
No full textMonitoring the state of health (SOH) for Li-ion batteries is crucial in the battery management system (BMS), for their efficient and safe use. Due to time-varying battery parameters and insufficient computation capability of the BMSs, computationally efficient online parameter identification is practically required. So, a simple equivalent circuit model (ECM) based recursive least squares (RLS) parameter identification algorithm has been widely used. However, it has long been acknowledged that this algorithm suffers from wind-up problem when the input current doesnt provide sufficient excitation. It causes numerical instability and then induces large sensitivity of identified parameter values to the noise or truncation error of sensor data, leading to large parameter identification errors. In this work, a new reliable version of ECM based RLS, called a condition number based recursive least squares (CNRLS) algorithm, is proposed to avoid large errors due to insufficient excitation by monitoring the condition number of the error covariance matrix If the condition number is greater than a certain prescribed value, currently identified parameters are considered unreliable and hence the proposed algorithm uses stored internal variables previously computed with sufficiently exciting input current, leading to small condition number of the error covariance matrix. Accordingly, the forgetting factor is also adjusted to give a larger weight to such stored internal variables in order to overcome the insufficient excitation of the input current. It is shown with a1-RC equivalent circuit model that the proposed CNRLS algorithm is more noise-tolerant and accurate than two benchmarks including the standard RLS and adaptive forgetting factor RLS (AFFRLS) in terms of mean absolute errors, with almost the same computing cost.11Ysciescopu
An Uncertainty and Disturbance Estimator based on Model-free Time-delay Control to Robot manipulators
No full textAn Uncertainty and Disturbance Estimator based on Model-free Time-delay Control to Robot manipulators1
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