Hasil untuk "River protective works. Regulation. Flood control"

Cédric Join, Michel Fliess

This communication on collision avoidance with unexpected obstacles is motivated by some critical appraisals on reinforcement learning (RL) which "requires ridiculously large numbers of trials to learn any new task" (Yann LeCun). We use the classic Dubins' car in order to replace RL with flatness-based control, combined with the HEOL feedback setting, and the latest model-free predictive control approach. The two approaches lead to convincing computer experiments where the results with the model-based one are only slightly better. They exhibit a satisfactory robustness with respect to randomly generated mismatches/disturbances, which become excellent in the model-free case. Those properties would have been perhaps difficult to obtain with today's popular machine learning techniques in AI. Finally, we should emphasize that our two methods require a low computational burden.

Jose A. Solano-Castellanos, Peter A. Fisher, Anuradha Annaswamy

This manuscript considers the problem of ensuring stability and safety during formation control with distributed multi-agent systems in the presence of parametric uncertainty in the dynamics and limited communication. We propose an integrative approach that combines Adaptive Control, Control Barrier Functions (CBFs), and connected graphs. The main elements employed in the integrative approach are an adaptive control design that ensures stability, a CBF-based safety filter that generates safe commands based on a reference model dynamics, and a reference model that ensures formation control with multi-agent systems when no uncertainties are present. The overall control design is shown to lead to a closed-loop adaptive system that is stable, avoids unsafe regions, and converges to a desired formation of the multi-agents. Numerical examples are provided to support the theoretical derivations.

Arthur N. Montanari, Chao Duan, Adilson E. Motter

The duality between controllability and observability enables methods developed for full-state control to be applied to full-state estimation, and vice versa. In applications in which control or estimation of all state variables is unfeasible, the generalized notions of output controllability and functional observability establish the minimal conditions for the control and estimation of a target subset of state variables, respectively. Given the seemly unrelated nature of these properties, thus far methods for target control and target estimation have been developed independently in the literature. Here, we characterize the graph-theoretic conditions for target controllability and target observability (which are, respectively, special cases of output controllability and functional observability for structured systems). This allow us to rigorously establish a weak and strong duality between these generalized properties. When both properties are equivalent (strongly dual), we show that efficient algorithms developed for target controllability can be used for target observability, and vice versa, for the optimal placement of sensors and drivers. These results are applicable to large-scale networks, in which control and monitoring are often sought for small subsets of nodes.

Piotr Morawiecki, Philippe H. Trinh

Many simple hydrologic models are based on parametric statistical relations between the river flow and catchment properties such as its area, precipitation rates, soil properties, etc., fitted to the available data. The main objective of this work is to explain how these statistical relations emerge from the physical laws governing surface and subsurface flow at a catchment scale. The main achievement of this work is the derivation of an analytic formula for predicting peak monthly and annual river flows. It does not require any parameter calibration, but requires a measurement or estimation of the mean flow at the given catchment's outlet. We found that this model 1) has a simple physical interpretation, 2) provides more precise estimates than the median maximum annual flow (QMED) estimation method from the Flood Estimation Handbook (FEH), commonly used to estimate flood risk in the ungauged catchments in the UK, and 3) is highly accurate for all types of catchments, including the small catchments, for which the standard FEH method is the least accurate.

Nishanth Venkatesh, Aditya Dave, Ioannis Faros et al.

In this paper, we investigate the control of a cyber-physical system (CPS) while accounting for its vulnerability to external attacks. We formulate a constrained stochastic problem with a robust constraint to ensure robust operation against potential attacks. We seek to minimize the expected cost subject to a constraint limiting the worst-case expected damage an attacker can impose on the CPS. We present a dynamic programming decomposition to compute the optimal control strategy in this robust-constrained formulation and prove its recursive feasibility. We also illustrate the utility of our results by applying them to a numerical simulation.

Po-han Li, Ufuk Topcu, Sandeep P. Chinchali

We propose a method to attack controllers that rely on external timeseries forecasts as task parameters. An adversary can manipulate the costs, states, and actions of the controllers by forging the timeseries, in this case perturbing the real timeseries. Since the controllers often encode safety requirements or energy limits in their costs and constraints, we refer to such manipulation as an adversarial attack. We show that different attacks on model-based controllers can increase control costs, activate constraints, or even make the control optimization problem infeasible. We use the linear quadratic regulator and convex model predictive controllers as examples of how adversarial attacks succeed and demonstrate the impact of adversarial attacks on a battery storage control task for power grid operators. As a result, our method increases control cost by $8500\%$ and energy constraints by $13\%$ on real electricity demand timeseries.

Alena Rodionova, Lars Lindemann, Manfred Morari et al.

We present a robust control framework for time-critical systems in which satisfying real-time constraints robustly is of utmost importance for the safety of the system. Signal Temporal Logic (STL) provides a formal means to express a large variety of real-time constraints over signals and is suited for planning and control purposes as it allows us to reason about the time robustness of such constraints. The time robustness of STL particularly quantifies the extent to which timing uncertainties can be tolerated without violating real-time specifications. In this paper, we first pose a control problem in which we aim to find an optimal input sequence to a control system that maximizes the time robustness of an STL constraint. We then propose a Mixed Integer Linear Program (MILP) encoding and provide correctness guarantees along with a complexity analysis of the encoding. We also show in two case studies that maximizing STL time robustness allows to account for timing uncertainties of the underlying control system.

Carmen Amo Alonso, Jing Shuang Li, Nikolai Matni et al.

We present a robust Distributed and Localized Model Predictive Control (rDLMPC) framework for large-scale structured linear systems. The proposed algorithm uses the System Level Synthesis to provide a distributed closed-loop model predictive control scheme that is robust to exogenous disturbances. The resulting controllers require only local information exchange for both synthesis and implementation. We exploit the fact that for polytopic disturbance constraints, SLS- based distributed control problems have been shown to have structure amenable for distributed optimization techniques. We show that similar to the disturbance-free DLMPC algorithm, the computational complexity of rDLMPC is independent of the size of the global system. To the best of our knowledge, robust DLMPC is the first MPC algorithm that allows for the scalable distributed computation of distributed closed-loop control policies in the presence of additive disturbances.

Zhenyi Yuan, Jorge Cortes

This paper develops a method to learn optimal controls from data for bilinear systems without a priori knowledge of the system dynamics. Given an unknown bilinear system, we first characterize when the available data is suitable to solve the optimal control problem. This characterization leads us to propose an online control experiment design procedure that guarantees that any input/state trajectory can be represented as a linear combination of collected input/state data matrices. Leveraging this data-based representation, we transform the original optimal control problem into an equivalent data-based optimization problem with bilinear constraints. We solve the latter by iteratively employing a convex-concave procedure to convexify it and find a locally optimal control sequence. Simulations show that the performance of the proposed data-based approach is comparable with model-based methods.

Alexandros Tanzanakis, John Lygeros

We study the problem of optimal state-feedback tracking control for unknown discrete-time deterministic systems with input constraints. To handle input constraints, state-of-art methods utilize a certain nonquadratic stage cost function, which is sometimes limiting real systems. Furthermore, it is well known that Policy Iteration (PI) and Value Iteration (VI), two widely used algorithms in data-driven control, offer complementary strengths and weaknesses. In this work, a two-step transformation is employed, which converts the constrained-input optimal tracking problem to an unconstrained augmented optimal regulation problem, and allows the consideration of general stage cost functions. Then, a novel multi-step VI algorithm based on Q-learning and linear programming is derived. The proposed algorithm improves the convergence speed of VI, avoids the requirement for an initial stabilizing control policy of PI, and computes a constrained optimal feedback controller without the knowledge of a system model and stage cost function. Simulation studies demonstrate the reliability and performance of the proposed approach.

Alexander Weber, Alexander Knoll

The present work deals with quantitative two-phase reach-avoid problems on nonlinear control systems. This class of optimal control problem requires the plant's state to visit two (rather than one) target sets in succession while minimizing a prescribed cost functional. As we illustrate, the naive approach, which subdivides the problem into the two evident classical reach-avoid tasks, usually does not result in an optimal solution. In contrast, we prove that an optimal controller is obtained by consecutively solving two special quantitative reach-avoid problems. In addition, we present a fully-automated method based on Symbolic Optimal Control to practically synthesize for the considered problem class approximately optimal controllers for sampled-data nonlinear plants. Experimental results on parcel delivery and on an aircraft routing mission confirm the practicality of our method.

Wenceslao Shaw Cortez, Dimos V. Dimarogonas

Safety-critical control is characterized as ensuring constraint satisfaction for a given dynamical system. Recent developments in zeroing control barrier functions (ZCBFs) have provided a framework for ensuring safety of a superlevel set of a single constraint function. Euler-Lagrange systems represent many real-world systems including robots and vehicles, which must abide by safety-regulations, especially for use in human-occupied environments. These safety regulations include state constraints (position and velocity) and input constraints that must be respected at all times. ZCBFs are valuable for satisfying system constraints for general nonlinear systems, however their construction to satisfy state and input constraints is not straightforward. Furthermore, the existing barrier function methods do not address the multiple state constraints that are required for safety of Euler-Lagrange systems. In this paper, we propose a methodology to construct multiple, non-conflicting control barrier functions for Euler-Lagrange systems subject to input constraints to satisfy safety regulations, while concurrently taking into account robustness margins and sampling-time effects. The proposed approach consists of a sampled-data controller and an algorithm for barrier function construction to enforce safety (i.e satisfy position and velocity constraints). The proposed method is validated in simulation on a 2-DOF planar manipulator.

Hassan Jafarzadeh, Cody Fleming

A Learning Model Predictive Controller (LMPC) is presented and tailored to platooning and Connected Autonomous Vehicles (CAVs) applications. The proposed controller builds on previous work on nonlinear LMPC, adapting its architecture and extending its capability to (a) handle dynamic environments and (b) account for data-driven decision variables that derive from an unknown or unknowable function. The paper presents the control design approach, and shows how to recursively construct an outer loop candidate trajectory and an inner iterative LMPC controller that converges to an optimal strategy over both model-driven and data-driven variables. Simulation results show the effectiveness of the proposed control logic.

E. Pasche, C. Zevenbergen, R. Ashley et al.

The flooding system in urban catchments needs special consideration and targeted strategies for the prevention and mitigation of flood risks in urban areas as illustrated in the Pitt Review of the widespread urban flooding in Eng-land in 2007 (http://archive.cabinetoffice.gov.uk/pittreview/ thepittreview.html). In the past two decades, much research effort has been devoted to the assessment and analysis of flood vulnerability. The European Commission has been supporting this research since the early 1990s through its successive Framework Programmes for research and tech-nological development. Much of this has focused on large river catchments, coastal erosion and inundation, with only limited research considering the highly complex and dynamic urban context. As at the moment, many countries are embarking on their own urban flood risk management strategy – with greater or lesser effectiveness – there is a need to exchange best practices for flood risk management for these urban catchments at a European level. With this inten-tion, the European Cooperation in Science and Technology (EU COST) office launched the new action C22 – Urban Flood Management (COST-UFM) in 2005. More than 50 scientists and practitioners from 13 European nations coop-erated in this action with the objective to build upon the previous and current European research (such as FLOOD-site) and practice in urban flood risk, to highlight weaknesses of today’s knowledge and practice in urban flood manage-ment, to provide examples of best practice and to help support the European Union in providing a framework for future. The thematic structure of COST-UFM reflected the need for urban flood risk management to deal with the flood problem in a holistic way using resilience as a guiding prin-ciple. Resilience, as applied to the flooding system, is defined here in a very broad sense as the capacity of the whole system to absorb flood waves in annual variability and to reorganize while undergoing change in flood probability or severity. The absorptive capacity has three critical aspects: threshold/ resistive capacity (avoiding flood losses), coping capacity (alleviating flood losses) and recovery capacity (recovering from flood losses). The reorganizing capacity refers to the capacity of the system management (i.e. the individuals and groups acting to manage the system) to influence the threshold/resistive, coping and recovery capacity. In 2009, COST-UFM ended with an international conference. On behalf of the International Hydrological Program of UNESCO and EU COST politicians, policymakers and decision-makers, researchers and practitioners from all over the world attended the closing event of the COST action, the final conference: Road Map Towards a Flood Resilient Urban Environment. This conference was organized jointly by UNESCO-International Hydrological Programme and COST-UFM in the headquarters of UNESCO in Paris on November 26th and 27th, 2009, with the intention to combine the forces of global and European communities. Participants from 23 countries witnessed an event in which a representative selection of the world’s knowledge on this emerging topic of flood risk management was presented and discussed. About 100 papers were selected for presentation. These highlighted the recent advances towards flood-resilient cities. The Editorial Board of the Journal of Flood Risk Management (JFRM) has taken this opportunity to release a special edition of the journal in which a selection of papers from the conference provides information related to the four main topics of the conference. In Section A, three papers address the topic of ‘policy, decision making and the role of institutions’ in the development of flood-resilient cities. They focus on appropriate policies, regulations, institutions and actors to respond to the pressures and needs to adapt cities to climate change and socio-economic drivers. In Section B, three papers deal with the ‘impact assessment of climate change and anthropogenic drivers’. These show new ideas of integrated modelling of local-scale climate change and the consequences for urban flood, inundation, vulnerability and damage, including probabilistic methods for the assessment of risk and hazard. Section C covers ‘Resilience Technology and nonstructural measures – source, pathway and receptor control’. The selected papers focus on options for: a) floodresilient-built environment; b) flood-resilient infrastructure; c) SUDS and conveyance systems for exceedance flows; d) management of pluvial, fluvial and coastal flooding; e) emergency responses during and post-flood. Section D addresses the ‘Strategy, communication and capacity building’ to support the transition process from traditional flood defence to flood risk management through empowering the public and others, sophisticated model-based DOI: 10.1111/jfr3.1145

Ł. Topczewski, J. Ciesla, P. Mikołajewski et al.

Scour of the riverbeds around bridge supports is the most frequent cause of their failures. Maintenance and repair costs of the bridges damaged by scour effects are significant, but it is estimated that the social costs are five times higher than the direct repair and replacement costs. The bridge supports are subjected to the scour effects due to the erosive action of the flowing water, involving soil loosening from the bottom and the banks of the watercourse. The condition for the proper monitoring of scour is to understand its nature. The knowledge of the phenomena occurring during the high water flow in the area of the bridge supports is crucial to properly assess the current condition and to develop proper maintenance actions. Scour may be the consequence of: narrowing the watercourse – a natural or man-made, including construction of a bridge, lateral movement or lowering of the stream bed, hydraulic works shortening the length of the meandering section of the watercourse, changes occurring in the catchment area of the watercourse, other changes in watercourse hydrology. Construction of a bridge in the certain area disturbs natural stream flow conditions, especially the flood water and may change the terms of the normal water flow. The presence of a bridge causes the stream flow cross-section reduction, which increases the speed and intensity of erosion of the streambed. River tends to stabilize its bed in order to restore the natural flow section. Bridge supports also change the laminar water flow and turbulent flow. Scour present around a bridge supports can be monitored by the mobile and fixed devices. Portable scour monitoring devices are mainly: different types of probes such as: sticks, tape or rope with weights and bars used by divers and sonar acoustic measuring devices. Stationary equipment is used for continuous or regular scour monitoring of the bridge supports of the bridge, for example once a day, once a week. They are stationary devices including various types of robotic probes and stationary hydroacoustic measurement systems. Stationary device can be installed on a support or near the bridge, usually at the head of the pillar, or in the ground near the bottom of the watercourse. It should be installed near the site of the anticipated greatest scour. The device interacts with the data logger, which can be read on the site or transmitted to a remote control unit. The article presents the principles of scour monitoring near the bridge supports, developed during the project “Monitoring system for bridge supports and their surrounding areas” co-founded by the Polish National Centre for Research and Development under the program Innotech. During the project monitoring system for bridge supports was developed with specialized software for online data visualization. The article presents selected measurement results from the sonar measurements.

C. Nerín, Filomena Silva, S. Manso et al.

Debasish Chatterjee, Masaaki Nagahara, Daniel Quevedo et al.

Maximum hands-off control aims to maximize the length of time over which zero actuator values are applied to a system when executing specified control tasks. To tackle such problems, recent literature has investigated optimal control problems which penalize the size of the support of the control function and thereby lead to desired sparsity properties. This article gives the exact set of necessary conditions for a maximum hands-off optimal control problem using an $L_0$-(semi)norm, and also provides sufficient conditions for the optimality of such controls. Numerical example illustrates that adopting an $L_0$ cost leads to a sparse control, whereas an $L_1$-relaxation in singular problems leads to a non-sparse solution.

Yoke Peng Leong, John C. Doyle

Robust control theory studies the effect of noise, disturbances, and other uncertainty on system performance. Despite growing recognition across science and engineering that robustness and efficiency tradeoffs dominate the evolution and design of complex systems, the use of robust control theory remains limited, partly because the mathematics involved is relatively inaccessible to nonexperts, and the important concepts have been inexplicable without a fairly rich mathematics background. This paper aims to begin changing that by presenting the most essential concepts in robust control using human stick balancing, a simple case study popular in both the sensorimotor control literature and extremely familiar to engineers. With minimal and familiar models and mathematics, we can explore the impact of unstable poles and zeros, delays, and noise, which can then be easily verified with simple experiments using a standard extensible pointer. Despite its simplicity, this case study has extremes of robustness and fragility that are initially counter-intuitive but for which simple mathematics and experiments are clear and compelling. The theory used here has been well-known for many decades, and the cart-pendulum example is a standard in undergrad controls courses, yet a careful reconsidering of both leads to striking new insights that we argue are of great pedagogical value.

Ehsan Shafieepoorfard, Maxim Raginsky, Sean P. Meyn

The article poses a general model for optimal control subject to information constraints, motivated in part by recent work of Sims and others on information-constrained decision-making by economic agents. In the average-cost optimal control framework, the general model introduced in this paper reduces to a variant of the linear-programming representation of the average-cost optimal control problem, subject to an additional mutual information constraint on the randomized stationary policy. The resulting optimization problem is convex and admits a decomposition based on the Bellman error, which is the object of study in approximate dynamic programming. The theory is illustrated through the example of information-constrained linear-quadratic-Gaussian (LQG) control problem. Some results on the infinite-horizon discounted-cost criterion are also presented.

Hui Liu, Junjian Qi, Jianhui Wang et al.

The Global Asymptotic Stability (GAS), Voltage Regulation (VR), and Power Regulation (PR) of the excitation and governor control system are of critical importance for power system security. However, simultaneously fulfilling GAS, VR, and PR has not yet been achieved. In order to solve this problem, in this paper, we propose a Lyapunov-based decentralized Control (LBC) for the excitation and governor system of multi-machine power system. A completely controllable linear system is actively constructed to design the time-derivative of the Lyapunov function and GAS is guaranteed by satisfying the condition of GAS in Lyapunov theorem. At the same time, VR and PR are performed by introducing both voltage and power to the feedback. The effectiveness of the proposed method is tested and validated on a six-machine power system.