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Jumping disturbances and large noises in input voltage sources to a power converter can cause substantial excursion of its output voltage even under a well-designed feedback controller. Predictive compensation can achieve improved disturbance rejection and tracking performance in such scenarios, resulting in a two-degree-of-freedom design. While the feedback controller has embedded robustness, designing feedforward controllers, which are open-loop compensators, is challenging due to the fact that converter internal parameters change from aging and variations in operating conditions, and loads themselves are part of the converter dynamics. When converter dynamics change, system performance deteriorates significantly, making adaptation mandatory. By integrating system identification with the feedforward compensator, an adaptive feedforward compensation design is proposed in this brief. Working on a boost dc–dc converter as a typical platform, combined feedback and adaptive feedforward design is explored. The results show that the two-degree-of-freedom adaptive design results in much improved performance in rejecting disturbances from input power sources. [ABSTRACT FROM AUTHOR]

The reliable operation of battery management systems depends critically on the accurate estimation of the state of charge (SOC) and characterizing parameters of a battery system. SOC estimation employs models that must be robust against variations in battery cell electrochemical features, aging, and operating conditions. This paper reveals that commonly used SOC estimation schemes are fundamentally flawed in providing the robustness of SOC estimation against model uncertainties. Parameter estimation methodologies and adaptive SOC estimation design are introduced in this paper to enhance SOC estimation accuracy and robustness. By a scrutiny of the impact of parameter variations on SOC estimation accuracy, the SOC–open-circuit-voltage mapping is identified to be the most critical function that must be accurately established. Identification algorithms are introduced, and their convergence properties are established. The integration of the identification algorithms and SOC estimation schemes lead to an adaptive SOC estimation framework that is superior over the existing methods in providing much improved accuracy and robustness. Experimental studies are conducted to validate the algorithms. [ABSTRACT FROM PUBLISHER]

This paper introduces identification algorithms for finite impulse response systems under quantized output observations and general quantized inputs. While asymptotically efficient algorithms for quantized identification under periodic inputs are available, their counterpart under general inputs has encountered technical difficulties and evaded satisfactory resolutions. Under quantized inputs, this paper resolves this issue with constructive solutions. A two-step algorithm is developed, which demonstrates desired convergence properties including strong convergence, mean-square convergence, convergence rates, asymptotic normality, and asymptotical efficiency in terms of the Cramér–Rao lower bound. Some essential conditions on input excitation are derived that ensure identifiability and convergence. It is shown that by a suitable selection of the algorithm’s weighting matrix, the estimates become asymptotically efficient. The strong and mean-square convergence rates are obtained. Optimal input design is given. Also the joint identification of noise distribution functions and system parameters is investigated. Numerical examples are included to illustrate the main results of this paper. [ABSTRACT FROM AUTHOR]

This paper studies identification of linear systems with binary-valued observations generated via fixed thresholds. In addition to stochastic measurement noises, the systems are also subject to structural uncertainties, including deterministic unmodelled dynamics, nonlinear model mismatch, and sensor observation bias. Since binary-valued observations can supply only limited information on the signals, truncated empirical measures are introduced to extract further information for system identification. An effective identification algorithm is constructed based on the proposed empirical measures. Optimal identification errors, time complexity, optimal input design, and impact of disturbances, unmodelled dynamics, observation bias, and nonlinear model mismatch are thoroughly investigated in a stochastic information framework. Asymptotic upper and lower bounds are established on identification errors. Numerical experiments are presented to demonstrate the effectiveness of the algorithms and the main results. [ABSTRACT FROM PUBLISHER]

Establishing real-time models for electric motors is important when capturing authentic dynamic behavior of the motors to improve control performance, enhance robustness, and support diagnosis. Quantized sensors are less expensive, and remotely controlled motors mandate signal quantization. Such limitations on observations introduce challenging issues in motor parameter estimation. This paper develops estimators for model parameters of permanent-magnet direct current (PMDC) motors using quantized speed measurements. A typical linearized model structure of PMDC motors is used as a benchmark platform to demonstrate the technology and its key properties and benefits. Convergence properties are established. Simulations and experimental studies are performed to illustrate potential applications of the technology. [ABSTRACT FROM AUTHOR]

Abstract: The traditional paradigm of system identification employs prior information on system structures and environments and input/output observation data to derive system models. Extensive research and development on its methodologies, theoretical foundation, algorithms, verifications, and applications over the past half century have established a mature field with a rich literature and substantial benchmark applications. However, rapid advancement in science, technology, engineering, and social medias has ushered in a new era of systems science and control in which challenges and opportunities are abundant for system identification. In this sense, system identification remains an exciting, young, viable, and critical field that mandates new paradigms to meet such challenges. This article points out some potentially important aspects of system identification in these new paradigms, suggests some worthy areas of research focus, and most importantly opens the forum for further discussions. [Copyright &y& Elsevier]

Accurate estimation of the state of charge in battery systems is of essential importance for battery system management. Due to nonlinearity, high sensitivity of the inverse mapping from external measurements, and measurement errors, SOC estimation has remained a challenging task. This is further compounded by the fact that battery characteristic model parameters change with time and operating conditions. This paper introduces an adaptive nonlinear observer design that compensates nonlinearity and achieves better estimation accuracy. A two-time-scale signal processing method is employed to attenuate the effects of measurement noises on SOC estimates. The results are further expanded to derive an integrated algorithm to identify model parameters and initial SOC jointly. Simulations were performed to illustrate the capability and utility of the algorithms. Experimental verifications are conducted on Li-ion battery packs of different capacities under different load profiles. [ABSTRACT FROM AUTHOR]

Renewable energy generation, vehicle electrification, and smart grids rely critically on energy storage devices for enhancement of operations, reliability, and efficiency. Battery systems consist of many battery cells, which have different characteristics even when they are new, and change with time and operating conditions due to a variety of factors such as aging, operational conditions, and chemical property variations. Their effective management requires high fidelity models. This paper aims to develop identification algorithms that capture individualized characteristics of each battery cell and produce updated models in real time. It is shown that typical battery models may not be identifiable, unique battery model features require modified input/output expressions, and standard least-squares methods will encounter identification bias. This paper devises modified model structures and identification algorithms to resolve these issues. System identifiability, algorithm convergence, identification bias, and bias correction mechanisms are rigorously established. A typical battery model structure is used to illustrate utilities of the methods. [ABSTRACT FROM PUBLISHER]

Abstract: This work is concerned with tracking and system identification for time-varying parameters. The parameters are Markov chains and the observations are binary valued with noise corruption. To overcome the difficulties due to the limited measurement information, Wonham-type filters are developed first. Then, based on the filters, two popular estimators, namely, mean squares estimator (MSQ) and maximum posterior (MAP) estimator are constructed. For the mean squares estimator, we derive asymptotic normality in the sense of weak convergence and in the sense of strong approximation. The asymptotic normality is then used to derive error bounds. When the Markov chain is infrequently switching, we derive error bounds for MAP estimators. When the Markovian parameters are fast varying, we show that the averaged behavior of the parameter process can be derived from the stationary measure of the Markov chain and that can be estimated using empirical measures. Upper and lower error bounds on estimation errors are also established. [Copyright &y& Elsevier]

Abstract: This work is concerned with system identification of plants using quantized output observations. We focus on relationships between identification space and time complexities. This problem is of importance for system identification in which data-flow rates are limited due to computer networking, communications, wireless channels, etc. Asymptotic efficiency of empirical measure based algorithms yields a tight lower bound on identification accuracy. This bound is employed to derive a separation principle of space and time complexities and to study sensor threshold selection. Insights gained from these understandings provide a feasible approach for optimal utility of communication bandwidth resources in enhancing identification accuracy. [Copyright &y& Elsevier]

Abstract: This paper studies identification of systems in which only quantized output observations are available. An identification algorithm for system gains is introduced that employs empirical measures from multiple sensor thresholds and optimizes their convex combinations. Strong convergence of the algorithm is first derived. The algorithm is then extended to a scenario of system identification with communication constraints, in which the sensor output is transmitted through a noisy communication channel and observed after transmission. The main results of this paper demonstrate that these algorithms achieve the Cramér–Rao lower bounds asymptotically, and hence are asymptotically efficient algorithms. Furthermore, under some mild regularity conditions, these optimal algorithms achieve error bounds that approach optimal error bounds of linear sensors when the number of thresholds becomes large. These results are further extended to finite impulse response and rational transfer function models when the inputs are designed to be periodic and full rank. [Copyright &y& Elsevier]

Abstract: System identification of plants with binary-valued output observations is of importance in understanding modeling capability and limitations for systems with limited sensor information, establishing relationships between communication resource limitations and identification complexity, and studying sensor networks. This paper resolves two issues arising in such system identification problems. First, regression structures for identifying a rational model contain non-smooth nonlinearities, leading to a difficult nonlinear filtering problem. By introducing a two-step identification procedure that employs periodic signals, empirical measures, and identifiability features, rational models can be identified without resorting to complicated nonlinear searching algorithms. Second, by formulating a joint identification problem, we are able to accommodate scenarios in which noise distribution functions are unknown. Convergence of parameter estimates is established. Recursive algorithms for joint identification and their key properties are further developed. [Copyright &y& Elsevier]

Abstract: This paper is concerned with persistent system identification for plants that are equipped with binary sensors whose unknown parameter is a random process represented by a Markov chain. We treat two classes of problems. In the first class, the parameter is a stochastic process modeled by an irreducible and aperiodic Markov chain with transition rates sufficiently faster than adaptation rates of identification algorithms. In this case, an averaged behavior of the parameter process can be derived from the stationary measure of the Markov chain and can be estimated with empirical measures. Upper and lower error bounds are established that explicitly show impact of unmodeled dynamics. In the second class of problems, the state switches values infrequently. A moving-window maximum a posterior (MAP) algorithm is introduced for tracking the time-varying parameters. Numerical results are presented to illustrate the tracking performance of the MAP algorithm and compare it with the widely used Viterbi algorithm. [Copyright &y& Elsevier]