系统与控制学术速递[1.10]
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eess.SY系统与控制，共计9篇
【1】 Voltage-Based State of Charge Correction at Charge-End
标题：基于电压的电荷端充电状态校正
链接：https://arxiv.org/abs/2201.02282
摘要：A voltage-based method is proposed to correct battery pack state of charge
(SOC) estimation at the charge-end. Two main characteristics make the
charge-end time span a good opportunity to correct SOC estimation: first, it is
easy to detect when the battery is at the last stage of charging because the
charging profile is known to the BMS designer and also during the charge-end
time span the amount of current is low, and the terminal voltage of the battery
cells are high; second, as the battery reaches the charge-end stage, we know
that the true SOC is approaching to 100%. This paper presents a method to
utilize these important features to correct the SOC estimation error. Using a
voltage threshold method, the algorithm detects when the battery is close to
the charge-end to activate the charge-end SOC correction strategy. Once
activated, the strategy corrects the SOC using the maximum cell voltage to
guarantee that SOC is 100% when charging is complete. The amount of correction
is a function of maximum cell voltage and the charge current C-rate.
【2】 Well-Conditioned Linear Minimum Mean Square Error Estimation
标题：良态线性最小均方误差估计
链接：https://arxiv.org/abs/2201.02275
摘要：Computing linear minimum mean square error (LMMSE) filters is often ill
conditioned, suggesting that unconstrained minimization of the mean square
error is an inadequate principle for filter design. To address this, we first
develop a unifying framework for studying constrained LMMSE estimation
problems. Using this framework, we expose an important structural property of
all constrained LMMSE filters and show that they all involve an inherent
preconditioning step. This parameterizes all such filters only by their
preconditioners. Moreover, each filters is invariant to invertible linear
transformations of its preconditioner. We then clarify that merely constraining
the rank of the filters, leading to the well-known low-rank Wiener filter, does
not suitably address the problem of ill conditioning. Instead, we use a
constraint that explicitly requires solutions to be well conditioned in a
certain specific sense. We introduce two well-conditioned estimators and
evaluate their mean-squared-error performance. We show these two estimators
converge to the standard LMMSE filter as their truncated-power ratio converges
to zero, but more slowly than the low-rank Wiener filter in terms of scaling
law. This exposes the price for being well conditioned. We also show
quantitative results with historical VIX data to illustrate the performance of
our two well-conditioned estimators.
【3】 An Input-to-State Safety Approach to Anomaly-Resilient Parabolic PDEs: Application to Cyber-Physical Battery Modules
标题：异常弹性抛物型偏微分方程的输入到状态安全方法：在网络物理电池模块中的应用
链接：https://arxiv.org/abs/2201.02239
摘要：Distributed Parameter Cyber-Physical Systems (DPCPSs), modelled by Partial
Differential Equations (PDEs), are increasingly vulnerable to anomalies such as
physical faults as well as cyber-attacks. This motivates the need for
strategies towards anomaly-resilient control of these systems. Although anomaly
detection and diagnostics in PDE systems have received considerable attention
in existing literature, fault-tolerant or anomaly-resilient control for PDEs
remains relatively under-explored. However, given the vulnerabilities of these
systems against anomalies, it is essential that the control systems possess
resilience against these disruptions. In this context, we explore a Practical
Input-to-Safety (pISSf) based control design approach for a class of DPCPSs
modelled by linear Parabolic PDEs. Specifically, we develop a design framework
for anomaly-resilient control for this class of system with both safety and
stability guarantees based on control Lyapunov functional and control barrier
functional. To illustrate our methodology, we apply our strategy to design a
thermal-anomaly resilient boundary coolant control system for a cyber-physical
battery module. Several simulation studies are done to show the efficacy of our
method under anomalies such as mechanical battery degradation and cyber-attack
mediated overdischarge.
【4】 Online 3-Axis Magnetometer Hard-Iron and Soft-Iron Bias and Angular Velocity Sensor Bias Estimation Using Angular Velocity Sensors for Improved Dynamic Heading Accuracy
标题：在线三轴磁强计硬铁和软铁偏差和角速度传感器偏差估计使用角速度传感器提高动态航向精度
链接：https://arxiv.org/abs/2201.02449
备注：Preprint of an article accepted for publication in Field Robotics, this https URL, Special Issue in Unmanned Marine Systems. Submitted January 16, 2021; Revised May 28, 2021; Accepted August 2, 2021
摘要：This article addresses the problem of dynamic on-line estimation and
compensation of hard-iron and soft-iron biases of 3-axis magnetometers under
dynamic motion in field robotics, utilizing only biased measurements from a
3-axis magnetometer and a 3-axis angular rate sensor. The proposed magnetometer
and angular velocity bias estimator (MAVBE) utilizes a 15-state process model
encoding the nonlinear process dynamics for the magnetometer signal subject to
angular velocity excursions, while simultaneously estimating 9 magnetometer
bias parameters and 3 angular rate sensor bias parameters, within an extended
Kalman filter framework. Bias parameter local observability is numerically
evaluated. The bias-compensated signals, together with 3-axis accelerometer
signals, are utilized to estimate bias compensated magnetic geodetic heading.
Performance of the proposed MAVBE method is evaluated in comparison to the
widely cited magnetometer-only TWOSTEP method in numerical simulations,
laboratory experiments, and full-scale field trials of an instrumented
autonomous underwater vehicle in the Chesapeake Bay, MD, USA. For the proposed
MAVBE, (i) instrument attitude is not required to estimate biases, and the
results show that (ii) the biases are locally observable, (iii) the bias
estimates converge rapidly to true bias parameters, (iv) only modest instrument
excitation is required for bias estimate convergence, and (v) compensation for
magnetometer hard-iron and soft-iron biases dramatically improves dynamic
heading estimation accuracy.
【5】 Model-Free Nonlinear Feedback Optimization
标题：无模型非线性反馈优化
链接：https://arxiv.org/abs/2201.02395
摘要：Feedback optimization is a control paradigm that enables physical systems to
autonomously reach efficient operating points. Its central idea is to
interconnect optimization iterations in closed-loop with the physical plant.
Since iterative gradient-based methods are extensively used to achieve
optimality, feedback optimization controllers typically require the knowledge
of the steady-state sensitivity of the plant, which may not be easily
accessible in some applications. In contrast, in this paper we develop a
model-free feedback controller for efficient steady-state operation of general
dynamical systems. The proposed design consists in updating control inputs via
gradient estimates constructed from evaluations of the nonconvex objective at
the current input and at the measured output. We study the dynamic
interconnection of the proposed iterative controller with a stable nonlinear
discrete-time plant. For this setup, we characterize the optimality and the
stability of the closed-loop behavior as functions of the problem dimension,
the number of iterations, and the rate of convergence of the physical plant. To
handle general constraints that affect multiple inputs, we enhance the
controller with Frank-Wolfe type updates.
【6】 Asymptotic Security using Bayesian Defense Mechanisms with Application to Cyber Deception
标题：基于贝叶斯防御机制的渐近安全性及其在网络欺骗中的应用
链接：https://arxiv.org/abs/2201.02351
备注：16 pages
摘要：This study addresses the question whether model knowledge can prevent a
defender from being deceived or not in cyber security. As a specific
model-based defense scheme, this study treats Bayesian defense mechanism, which
monitors the system's behavior, forms a belief on existence of the attacker,
and chooses appropriate reactions. Sophisticated attackers aim at achieving her
objective while avoiding being detected by deceiving the defender. In this
paper, their dynamic decision making is formulated as a stochastic signaling
game. It is revealed that the belief on the true scenario has a limit in a
stochastic sense at an equilibrium based on martingale analysis. This fact
implies that there are only two possible cases: the defender asymptotically
detects the attack with a firm belief or the attacker takes actions such that
the system's behavior becomes nominal after a certain finite time step.
Consequently, if the dynamics admits no stealthy attacks, the system is
guaranteed to be secure in an asymptotic manner provided that effective
countermeasures are implemented. The result concludes that model knowledge can
prevent deception in an asymptotic sense. As an application of the finding, a
defensive deception utilizing asymmetric recognition on vulnerabilities
exploited by the attacker is analyzed. It is shown that, the attacker possibly
stops the attack even if the defender is unaware of the vulnerabilities as long
as the defender's unawareness is concealed by the defensive deception. Those
results indicate the powerful defense capability achieved by model knowledge.
【7】 Stochastic Saddle Point Problems with Decision-Dependent Distributions
标题：决策相关分布的随机鞍点问题
链接：https://arxiv.org/abs/2201.02313
摘要：This paper focuses on stochastic saddle point problems with
decision-dependent distributions in both the static and time-varying settings.
These are problems whose objective is the expected value of a stochastic payoff
function, where random variables are drawn from a distribution induced by a
distributional map. For general distributional maps, the problem of finding
saddle points is in general computationally burdensome, even if the
distribution is known. To enable a tractable solution approach, we introduce
the notion of equilibrium points -- which are saddle points for the stationary
stochastic minimax problem that they induce -- and provide conditions for their
existence and uniqueness. We demonstrate that the distance between the two
classes of solutions is bounded provided that the objective has a
strongly-convex-strongly-concave payoff and Lipschitz continuous distributional
map. We develop deterministic and stochastic primal-dual algorithms and
demonstrate their convergence to the equilibrium point. In particular, by
modeling errors emerging from a stochastic gradient estimator as sub-Weibull
random variables, we provide error bounds in expectation and in high
probability that hold for each iteration; moreover, we show convergence to a
neighborhood in expectation and almost surely. Finally, we investigate a
condition on the distributional map -- which we call opposing mixture dominance
-- that ensures the objective is strongly-convex-strongly-concave. Under this
assumption, we show that primal-dual algorithms converge to the saddle points
in a similar fashion.
【8】 Electric Vehicle Routing Problem with Spatio-temporal Varying Electricity Price and Incentive-aware Customers
标题：具有时空变化电价和激励顾客的电动汽车路径问题
链接：https://arxiv.org/abs/2201.02311
备注：Submitted to IEEE TSG. arXiv admin note: substantial text overlap with arXiv:2110.06441
摘要：This paper investigates the optimization problem of a fleet of electric
vehicles (EVs) serving a set of time-specified customers, where the operator
needs to optimize routing and charging problem jointly for each EV. In
particular, regarding to the spatio-temporal varying electricity price, we
consider incentive-aware customers and propose that the operator offers
monetary incentives to exchange time flexibility of customers. In this manner,
a win-win situation is achievable since time flexibility enables the fleet
operator to obtain a routing and charging schedule with lower cost, whilst the
customers receives monetary compensation. Specifically, we first devise a
bi-level model whereby the fleet operator optimizes the routing and charging
schedule jointly with a monetary incentive to reimburse the delivery time
flexibility experienced by the customers. At the same time, the customers
choose the optimal time flexibility by minimizing its own cost. Second, we
tackle the complexity resulting from the bi-level and nonlinear problem with an
equivalent transformation method. Eventually, we reformulate the problem as a
single-level optimization problem, which later is solved by proposed Benders
dual decomposition method holding a faster convergence rate than the
generalized Benders decomposition method. To evaluate the effectiveness of our
framework and proposed Benders dual decomposition algorithm, we carry out
extensive numerical experiments using VRP-REP data from Belgium.
【9】 Strategic Storage Investment in Electricity Markets
标题：电力市场中的战略储能投资
链接：https://arxiv.org/abs/2201.02290
摘要：Arbitrage is one important revenue source for energy storage in electricity
markets. However, a large amount of storage in the market will impact the
energy price and reduce potential revenues. This can lead to strategic
behaviors of profit-seeking storage investors. To study the investors'
strategic storage investments, we formulate a non-cooperative game between
competing investors. Each investor decides the storage investment over a long
investment horizon, and operates the storage for arbitrage revenues in the
daily electricity market. Different investors can deploy storage with different
characteristics. Their decisions are coupled due to the market price that is
determined by all the investors' decisions. We use market data from California
ISO to characterize the storage impact on the market price, based on which we
establish a centralized optimization problem to compute the market equilibrium.
We show that an increasing number of investors will increase the market
competition, which reduces investors' profits but increases the total invested
storage capacity. Furthermore, we find that a slight increase in the storage
efficiency (e.g., increased charge and discharge efficiency) can significantly
improve an investor's profit share in the market.
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