arXiv subjects

Wuhua Hu

Publications and source records attributed to Wuhua Hu.

13 recordsLinked to original sources

arXiv2022

Generic Stability Implication from Full Information Estimation to Moving-Horizon Estimation

Optimization-based state estimation is useful for handling of constrained linear or nonlinear dynamical systems. It has an ideal form, known as full information estimation (FIE) which uses all past measurements to perform state estimation, and also a practical counterpart, known as moving-horizon estimation (MHE) which uses most recent measurements of a limited length to perform the estimation. Due to the theoretical ideal, conditions for robust stability of FIE are relatively easier to establish than those for MHE, and various sufficient conditions have been developed in literature. This work reveals a generic link from robust stability of FIE to that of MHE, showing that the former implies at least a weaker robust stability of MHE which implements a long enough horizon. The implication strengthens to strict robust stability of MHE if the corresponding FIE satisfies a mild Lipschitz continuity condition. The revealed implications are then applied to derive new sufficient conditions for robust stability of MHE, which further reveal an intrinsic relation between the existence of a robustly stable FIE/MHE and the system being incrementally input/output-to-state stable.

math.OC↗

arXiv2018

An Event-based Diffusion LMS Strategy

We consider a wireless sensor network consists of cooperative nodes, each of them keep adapting to streaming data to perform a least-mean-squares estimation, and also maintain information exchange among neighboring nodes in order to improve performance. For the sake of reducing communication overhead, prolonging batter life while preserving the benefits of diffusion cooperation, we propose an energy-efficient diffusion strategy that adopts an event-based communication mechanism, which allow nodes to cooperate with neighbors only when necessary. We also study the performance of the proposed algorithm, and show that its network mean error and MSD are bounded in steady state. Numerical results demonstrate that the proposed method can effectively reduce the network energy consumption without sacrificing steady-state network MSD performance significantly.

eess.SP↗

arXiv2018

Output Feedback Control Based on State and Disturbance Estimation

Recently developed control methods with strong disturbance rejection capabilities provide a useful option for control design. The key lies in a general concept of disturbance and effective ways to estimate and compensate the disturbance. This work extends the concept of disturbance as the mismatch between a system model and the true dynamics, and estimates and compensates the disturbance for multi-input multi-output linear/nonlinear systems described in a general form. The results presented do not need to assume the disturbance to be independent of the control inputs or satisfy a certain matching condition, and do not require the system to be expressible in an integral canonical form as required by algorithms previously described in literature. The estimator and controller are designed under a state tracking framework, and sufficient conditions for the closed-loop stability are presented. The performance of the resulting controller relies on a co-design of the system model, the state and disturbance observer, and the controller. Numerical experiments on a first-order system and an inverted pendulum under uncertainties are used to illustrate the control design method and demonstrate its efficacy.

math.OC↗

arXiv2017

Robust Stability of Optimization-based State Estimation

Optimization-based state estimation is useful for nonlinear or constrained dynamic systems for which few general methods with established properties are available. The two fundamental forms are moving horizon estimation (MHE) which uses the nearest measurements within a moving time horizon, and its theoretical ideal, full information estimation (FIE) which uses all measurements up to the time of estimation. Despite extensive studies, the stability analyses of FIE and MHE for discrete-time nonlinear systems with bounded process and measurement disturbances, remain an open challenge. This work aims to provide a systematic solution for the challenge. First, we prove that FIE is robustly globally asymptotically stable (RGAS) if the cost function admits a property mimicking the incremental input/output-to-state stability (i-IOSS) of the system and has a sufficient sensitivity to the uncertainty in the initial state. Second, we establish an explicit link from the RGAS of FIE to that of MHE, and use it to show that MHE is RGAS under enhanced conditions if the moving horizon is long enough to suppress the propagation of uncertainties. The theoretical results imply flexible MHE designs with assured robust stability for a broad class of i-IOSS systems. Numerical experiments on linear and nonlinear systems are used to illustrate the designs and support the findings.

math.OC↗

arXiv2017

A Multitask Diffusion Strategy with Optimized Inter-Cluster Cooperation

We consider a multitask estimation problem where nodes in a network are divided into several connected clusters, with each cluster performing a least-mean-squares estimation of a different random parameter vector. Inspired by the adapt-then-combine diffusion strategy, we propose a multitask diffusion strategy whose mean stability can be ensured whenever individual nodes are stable in the mean, regardless of the inter-cluster cooperation weights. In addition, the proposed strategy is able to achieve an asymptotically unbiased estimation, when the parameters have same mean. We also develop an inter-cluster cooperation weights selection scheme that allows each node in the network to locally optimize its inter-cluster cooperation weights. Numerical results demonstrate that our approach leads to a lower average steady-state network mean-square deviation, compared with using weights selected by various other commonly adopted methods in the literature.

eess.SY↗

arXiv2017

Assessing the Economics of Customer-Sited Multi-Use Energy Storage

This paper presents an approach to assess the economics of customer-sited energy storage systems (ESSs) which are owned and operated by a customer. The ESSs can participate in frequency regulation and spinning reserve markets, and are used to help the customer consume available renewable energy and reduce electricity bill. A rolling-horizon approach is developed to optimize the service schedule, and the resulting costs and revenues are used to assess economics of the ESSs. The economic assessment approach is illustrated with case studies, from which we obtain some new observations on profitability of the customer- sited multi-use ESSs.

math.OC↗

arXiv2016

Towards Optimal Energy Management of Microgrids with a Realistic Model

This work considers energy management in a grid-connected microgrid which consists of multiple conventional generators (CGs), renewable generators (RGs) and energy storage systems (ESSs). A two-stage optimization approach is presented to schedule the power generation, aimed at minimizing the long-term average operating cost subject to operational and service constraints. The first stage of optimization determines hourly unit commitment of the CGs via a day-ahead scheduling, and the second stage performs economic dispatch of the CGs, ESSs and energy trading via an hour-ahead scheduling. The combined solution meets the need of handling large uncertainties in the load demand and renewable generation, and provides an efficient solution under limited computational resource which meets both short-term and long-term quality-of-service requirements. The performance of the proposed strategy is evaluated by simulations based on real load demand and renewable generation data.

math.OC↗

arXiv2015

Optimization-based State Estimation under Bounded Disturbances

This paper studies an optimization-based state estimation approach for discrete-time nonlinear systems under bounded process and measurement disturbances. We first introduce a full information estimator (FIE), which is given as a solution to minimize a cost function by using all the available measurements. Then, we prove that the FIE of an incrementally input/output-to-state stable system is robustly globally asymptotically stable under a certain class of cost functions. Moreover, the implications and relationships with related results in the literature are discussed. Finally, a simple example is included to illustrate the theoretical results.

math.DS↗

arXiv2015

Network infection source identification under the SIRI model

We study the problem of identifying a single infection source in a network under the susceptible-infected-recovered-infected (SIRI) model. We describe the infection model via a state-space model, and utilizing a state propagation approach, we derive an algorithm known as the heterogeneous infection spreading source (HISS) estimator, to infer the infection source. The HISS estimator uses the observations of node states at a particular time, where the elapsed time from the start of the infection is unknown. It is able to incorporate side information (if any) of the observed states of a subset of nodes at different times, and of the prior probability of each infected or recovered node to be the infection source. Simulation results suggest that the HISS estimator outperforms the dynamic message pass- ing and Jordan center estimators over a wide range of infection and reinfection rates.

physics.soc-ph↗

arXiv2015

Energy-efficient Rail Guided Vehicle Routing for Two-Sided Loading/Unloading Automated Freight Handling System

Rail-guided vehicles (RGVs) are widely employed in automated freight handling system (AFHS) to transport surging air cargo. Energy-efficient routing of such vehicles is of great interest for both financial and environmental sustainability. Given a multi-capacity RGV working on a linear track in AFHS, we consider its optimal routing under two-sided loading/unloading (TSLU) operations, in which energy consumption is minimized under conflict-avoidance and time window constraints. The energy consumption takes account of routing-dependent gross weight and dynamics of the RGV, and the conflict-avoidance constraints ensure conflict-free transport service under TSLU operations. The problem is formulated as a mixed-integer linear program, and solved by incorporating valid inequalities that exploit structural properties of the problem. The static problem model and solution approach are then integrated with a rolling-horizon approach to solve the dynamic routing problem where air cargo enters and departs from the system dynamically in time. Simulation results suggest that the proposed strategy is able to route an RGV to transport air cargo with an energy cost that is considerably lower than one of the most commonly used heuristic methods implemented in current practice.

math.OC↗

arXiv2014

Multi-hop Diffusion LMS for Energy-constrained Distributed Estimation

We propose a multi-hop diffusion strategy for a sensor network to perform distributed least mean-squares (LMS) estimation under local and network-wide energy constraints. At each iteration of the strategy, each node can combine intermediate parameter estimates from nodes other than its physical neighbors via a multi-hop relay path. We propose a rule to select combination weights for the multi-hop neighbors, which can balance between the transient and the steady-state network mean-square deviations (MSDs). We study two classes of networks: simple networks with a unique transmission path from one node to another, and arbitrary networks utilizing diffusion consultations over at most two hops. We propose a method to optimize each node's information neighborhood subject to local energy budgets and a network-wide energy budget for each diffusion iteration. This optimization requires the network topology, and the noise and data variance profiles of each node, and is performed offline before the diffusion process. In addition, we develop a fully distributed and adaptive algorithm that approximately optimizes the information neighborhood of each node with only local energy budget constraints in the case where diffusion consultations are performed over at most a predefined number of hops. Numerical results suggest that our proposed multi-hop diffusion strategy achieves the same steady-state MSD as the existing one-hop adapt-then-combine diffusion algorithm but with a lower energy budget.

math.OC↗

arXiv2008

A trick: why \hatγ< γ(=3) in [1=arXiv:cond-mat/0106096]?

In this paper, first a theorem on the partial sum of a particular series is given. Then, based on it, the origin of obvious simulation deviation from theory is explained: i) why the numerically estimated \hatγ(degree exponent) in [1=arXiv:cond-mat/0106096] is always smaller than γ(=3) that is predicted by theory; ii) and why \hatγrises monotonically as m (the links added at each step in Barabasi-Albert (BA) model [1]) increases. Strictly, it declares such errors are basically from the inconsistence of simulation with the theoretical model, which is caused by an additional incompatible condition used in simulation. In addition, noticing the evolving differences between the initial m_0 nodes and those after, we correct the derived BA model which unfairly omitted such differences.

cond-mat.stat-mech↗

arXiv2008

White-noise-aided Control

The issue of white-noise-aided control is considered and its availability is proved. And a noise-aiding way is developed to stabilize perturbed systems to be input-to-state stable (ISS) with respect to (w.r.t.) perturbations. To illustrate its effectiveness, the white-noise-aided control of a parameter perturbed chaotic Chen system is given as an example. And numerically, it shows that, comparing to the un-noise-aided case, noise-aided control can not only shorten the control's transient process but also save its cost. These are also demonstrated by various aiding noises such as common (symmetric) noise and non-common (independent or asymmetric) noise, where common noise is found to be the most efficient in enhancing the control.

math.OC↗