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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., Comment: Presentation streamlined again, and made more accurate

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., Comment: 30 pages, 12 figures, 4 tables; submitted for publication

An enhanced state estimator (SE) is proposed to incorporate the real-time underfrequency load shedding (UFLS) information in this paper. During contingencies, the historical SCADA measurements are not accurate enough while the PMU measurements may be inadequate for the SE. Considering that load shedding strategy is a common practice to prevent the system from collapse during contingencies, we can use the shedding information as a viable option to update/correct the SCADA measurements by employing proper techniques. The auxiliary binary variables and scheduled load shedding amount are introduced in the traditional weighted least square formulation so that a more accurate SE can be obtained. The alternating optimization method is utilized to solve the SE problem. Case studies are conducted on the IEEE 30- and 118-Bus system to illustrate the proposed methodology and demonstrate the evident improvements on the reconstructed states.

In this paper, we propose a novel optimization strategy for energy-efficient day-ahead energy management and economic power reserve capacity allocation for frequency regulation, by jointly utilizing battery energy storage systems (BESSs) and demand-responsive (DR) fans at the distribution level. Specifically, we consider a smart building with renewable energy integration. The building owns a BESS and is willing to take part in frequency regulation service by allocating a proportion of its BESS capacity as power reserve capacity and receiving monetary reservation fee in return. Due to the high cost of BESS, its limited capacity, and significant degradation caused by excessive charging and discharging, we consider DR fans as a viable low-cost capability of the smart building for energy cost saving and frequency regulation. Particularly, we consider modern fans of the heating, ventilation, and air conditioning (HVAC) systems that can vary their speeds (and thus energy consumption), subject to satisfying user comfort constraints. Simulations results based on the real renewable energy and load data show that our proposed joint optimization strategy is effective in reducing the total energy cost of the building and at the same time providing a higher power reserve capacity to the grid.

Complex networks characterize the nature of internal/external interactions in real-world systems including social, economic, biological, ecological, and technological networks. Two issues keep as obstacles to fulfilling control of large-scale networks: structural controllability which describes the ability to guide a dynamical system from any initial state to any desired final state in finite time, with a suitable choice of inputs; and optimal control, which is a typical control approach to minimize the cost for driving the network to a predefined state with a given number of control inputs. For large complex networks without global information of network topology, both problems remain essentially open. Here we combine graph theory and control theory for tackling the two problems in one go, using only local network topology information. For the structural controllability problem, a distributed local-game matching method is proposed, where every node plays a simple Bayesian game with local information and local interactions with adjacent nodes, ensuring a suboptimal solution at a linear complexity. Starring from any structural controllability solution, a minimizing longest control path method can efficiently reach a good solution for the optimal control in large networks. Our results provide solutions for distributed complex network control and demonstrate a way to link the structural controllability and optimal control together. NRF (Natl Research Foundation, S’pore) MOE (Min. of Education, S’pore) Published version

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.

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., Comment: 30 pages, 8 figures, submitted to IEEE Journal of Selected Topics in Signal Processing

Recently the control methods based on disturbance rejection have largely renewed our understanding of automatic control, especially the role of feedforward control. With the renewed understanding, this work presents a feedforward and feedback approach for control of general dynamic systems under a state tracking framework. This is realized by introducing a fictitious system model and employing a proper state and disturbance estimator. The resultant control scheme embodies a close cooperation between feedforward and feedback controls: Feedforward control rejects the general disturbance and embeds a reference state trajectory, whilst feedback control cancels the fictitious dynamics and enforces desired tracking error dynamics. A notable advantage of the approach is that control designs of linear and nonlinear systems can follow a common routine and the closed-loop stability is guaranteed under standard conditions.

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., Comment: 4 pages, 3 figures, presented in IEEE TENCON 2016

We consider a multitask network where nodes 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 strategy, we propose a multitask diffusion strategy whose mean and mean-square stability can be achieved independent of the inter-cluster cooperation weights. We develop a distributed optimization algorithm that allows each node in the network to locally optimize its inter-cluster cooperation weights. Simulation results demonstrate that our approach leads to a lower average steady-state network MSD, compared with the multitask diffusion strategy using an averaging rule for the inter-cluster cooperation.

This paper presents a decentralized state estimation (SE) for the newly emerging hybrid ac/dc microgrid. The microgrid consists of an ac and a dc network which are connected via the interlinking ac/dc converter. The proposed SE is able to estimate the state variables of both networks in a decentralized way that the estimation is separately conducted while only limited information is exchanged during the process. The dual decomposition approach is adopted as the decentralized technique in this paper. Simulation results suggest that the proposed decentralized SE performs comparably to the centralized SE for the hybrid ac/dc microgrids in terms of accuracy, convergence, and robustness.

This paper considers energy management in a grid-connected microgrid which has multiple conventional generators (CGs), renewable generators and energy storage systems (ESSs). A robust two-stage optimization approach is presented to schedule the energy generation under uncertainties, aimed at minimizing the long-term average operating cost subject to realistic 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 a real-time scheduling. The combined solution meets the need of handling large uncertainties in the load demands and renewable generation, and provides an efficient solution under limited computational resource which approximately optimizes the long-term average operating cost while meeting the quality-of-service requirements. The performance of the proposed strategy is evaluated by simulations based on real load demand and renewable generation data. NRF (Natl Research Foundation, S’pore) EDB (Economic Devt. Board, S’pore)

In literature, self-optimizing control (SOC) method has been shown to be useful to select controlled variables (CVs) among available measurements for steady-state processes. This work introduces the concept of ‘dynamic SOC' (dSOC) to select CVs for processes, which do not reach steady state (e.g. batch processes) or for which the operational cost during transient response must be accounted for. The general problem formulation is presented and shown to be equivalent to an optimal control problem. An exact local method for quick prescreening of CV candidates is presented, where perturbation control approach is proposed for the solution of the resulting optimization problem. The proposed dSOC method is applied to a linear process to illustrate the theoretical results.

Recently, Shamsuzzoha and Skogestad proposed a proportional−integral (PI) tuning rule for a wide range of unidentified processes. [M. Shamsuzzoha and S. Skogestad, J. Process Control2010, 20, 1220....

The available methods for selection of controlled variables (CVs) using the concept of self-optimizing control have been developed under the restrictive assumption that the set of active constraints remains unchanged for all the allowable disturbances. To keep the input and output variables within their allowable bounds, the use of cascade controllers, and to track the optimal set of active input constraints, the use of split-range controllers is suggested in literature. In this paper, we propose a different strategy, where CVs are selected as linear combinations of measurements to minimize local average loss, while ensuring that all the constraints are satisfied over the allowable set of disturbances. This result is extended to select a few of the available measurements, whose combinations are used as CVs. The proposed approach offers simpler implementation of operational policy for processes with tight operational constraints. We use the case study of forced-circulation evaporator to illustrate the usefulness of the proposed method.

This work extends the concept of relative normalized gain array (RNGA) and proposes a systematic approach to designing decentralized proportional−integral−derivative (PID) control for multi-input−multi-output (MIMO) processes containing integrators and/or differentiators. By determining the input−output-pairing using the relative gain array (RGA)−Nederlinski index (NI)−RNGA criterion, equivalent transfer functions (ETFs) for the selected input−output pairs are derived by utilizing the RGA and RNGA information, which has incorporated the information on loop interactions. Based on the ETFs, decentralized PID controllers are tuned to stabilize the MIMO system independently. The proposed approach is simple, easy to understand, and easy to implement by field engineers. Three industrial MIMO processes with different dimensions and interaction modes are employed to demonstrate the efficiency of the proposed approach.

Correction to: Scientific Reports https://doi.org/10.1038/s41598-018-22655-5, published online 15 March 2018. The Acknowledgements section in this Article is incomplete.

In this paper, a general approach of partial control design for system control and synchronization is proposed. It turns control problems into simpler ones by reducing their control variables. This is realized by utilizing the dynamical relations between variables, which are described by the dynamical relation matrix and the dependence–influence matrix. By adopting partial control theory, the presented approach provides a simple and general way to stabilize systems to their partial or whole equilibriums, or to synchronize systems with their partial or whole states. Further, based on this approach, the controllers can be simplified. Two examples of synchronizing chaotic systems are given to illustrate its effectiveness.

We propose an energy-efficient diffusion strategy where each node performs the combination step in the diffusion LMS strategy only at every L time slots. This proposed strategy reduces the inter-node communication overhead by L-fold, but yields a steady-state network mean-square deviation (MSD) higher than the traditional diffusion LMS strategy. To improve the steady-state network MSD performance, we further propose a step-size switching mechanism that switches to a decaying step-size in the adaptation step of the diffusion LMS strategy as the MSD converges. To ensure adaptability of the new strategy, we also incorporate a parameter change detection method to switch the step-size back to a constant value if the underlying parameter varies. Simulation results suggest that the proposed method can effectively reduce the network energy consumption without sacrificing steady-state network MSD performance.

Ultra-wide bandwidth (UWB) systems allow for accurate localization to tackle and complement the GPS-aided solutions, which are impractical in weak signal environments. We consider the problem of fast link scheduling in the medium access control (MAC) layer for UWB localization. We present an optimization strategy to perform robust ranging scheduling with localization constraints. Given the complexity of the optimal strategy, two different MAC-aware link selection heuristics are also proposed. Our results show that significant MAC delay reductions are possible through the use of simple local heuristics.

Finding the solution for driving a complex network at the minimum energy cost with a given number of controllers, known as the minimum-cost control problem, is critically important but remains largely open. We propose a projected gradient method to tackle this problem, which works efficiently in both synthetic and real-life networks. The study is then extended to the case where each controller can only be connected to a single network node to have the lowest connection complexity. We obtain the interesting insight that such connections basically avoid high-degree nodes of the network, which is in resonance with recent observations on controllability of complex networks. Our results provide the first technical path to enabling minimum-cost control of complex networks, and contribute new insights to locating the key nodes from a minimum-cost control perspective. NRF (Natl Research Foundation, S’pore) MOE (Min. of Education, S’pore) Published version

This note proposes a new form of nonlinear state estimator, for which we can establish robust global asymptotic stability (RGAS) in the case of bounded disturbances. In this estimator, a max term is added to the usual sum of stage costs, and one additional assumption is made relating the initial state stage cost to the system’s detectability condition. A simulation example is presented to illustrate the estimator’s performance. Two open issues are presented: (i) the proof of estimator convergence for convergent disturbances and (ii) changing from full information estimation to moving horizon estimation (MHE), which has a smaller and more tractable online computational complexity.