Parham Oveissi, Gohar T. Khokhar, Kyle Hanquist
et al.
This paper presents the application of a novel data-driven adaptive control technique, called dynamic mode adaptive control (DMAC), for regulating thrust in a solid fuel ramjet (SFRJ). A high-fidelity computational model incorporating compressible flow theory and equilibrium chemistry is used to simulate the combustion dynamics. An adaptive tracking controller is designed using the DMAC framework, which leverages dynamic mode decomposition to approximate the local system behavior, followed by a tracking controller synthesized around the identified model. Simulation results demonstrate that DMAC provides an effective and reliable approach for thrust regulation in SFRJs. In addition, a systematic hyperparameter sensitivity study is conducted by varying the tuning parameters over several orders of magnitude. The resulting responses show that the closed-loop performance and tracking error remain stable across wide parameter variations, indicating that DMAC exhibits strong robustness to hyper parameter tuning.
Ensuring resilient consensus in multi-robot systems with misbehaving agents remains a challenge, as many existing network resilience properties are inherently combinatorial and globally defined. While previous works have proposed control laws to enhance or preserve resilience in multi-robot networks, they often assume a fixed topology with known resilience properties, or require global state knowledge. These assumptions may be impractical in physically-constrained environments, where safety and resilience requirements are conflicting, or when misbehaving agents share inaccurate state information. In this work, we propose a distributed control law that enables each robot to guarantee resilient consensus and safety during its navigation without fixed topologies using only locally available information. To this end, we establish a sufficient condition for resilient consensus in time-varying networks based on the degree of non-misbehaving or normal agents. Using this condition, we design a Control Barrier Function (CBF)-based controller that guarantees resilient consensus and collision avoidance without requiring estimates of global state and/or control actions of all other robots. Finally, we validate our method through simulations.
This paper investigates extremum seeking control for a torque-controlled antenna pointing system without direct angular measurements. We consider a two-degree-of-freedom (2-DOF) antenna system that receives an unknown signal from its environment, where the signal strength varies with the antenna orientation. It is assumed that only real-time measurements of the signal are available. We develop an extremum seeking control strategy that enables the antenna to autonomously adjust its direction to maximize the received signal strength based on the symmetric product approximation. Under suitable assumptions on the signal function, we prove local practical uniform asymptotic stability for the closed-loop system.
Vladyslav Polushko, Damjan Hatic, Ronald Rösch
et al.
Floods cause serious problems around the world. Responding quickly and effectively requires accurate and timely information about the affected areas. The effective use of Remote Sensing images for accurate flood detection requires specific detection methods. Typically, Deep Neural Networks are employed, which are trained on specific datasets. For the purpose of river flood detection in RGB imagery, we use the BlessemFlood21 dataset. We here explore the use of different augmentation strategies, ranging from basic approaches to more complex techniques, including optical distortion. By identifying effective strategies, we aim to refine the training process of state-of-the-art Deep Learning segmentation networks.
This paper investigates the solvability and optimal control of a class of impulsive stochastic differential equations (SDEs) within a Hilbert space setting. First, we establish the existence and uniqueness of mild solutions for the proposed impulsive stochastic system, leveraging fixed-point theorems and appropriate analytical techniques. Next, we identify and derive the necessary conditions for the existence of optimal control pairs, ensuring the feasibility and effectiveness of the control solutions. Finally, to validate and demonstrate the practical applicability of our theoretical findings, we provide a detailed example showcasing the utility of the results in real-world scenarios.
In this paper, an extension of a linear control design for hyperbolic linear partial differential equations is presented for a first-order traffic flow model. Starting from the Lighthill-Whitham-Richards (LWR) model, variable speed limit control (VSL) is applied through a modification of Greenshield's equilibrium flow model. Then, an optimal linear quadratic (LQ) controller is designed on the linear LWR model. The LQ state feedback function is found via the solution of a Riccati differential equation. Unlike previous studies, the control input is the rate of change of the input, not the input itself. The proposed controller is then verified on both the linear and nonlinear models. In both cases, the controller is able to drive the system to a desired density profile. In the nonlinear application, a higher control gain is needed to achieve similar results as in the linear case.
We study a version of the Ornstein-Uhlenbeck bridge driven by a spectrally-positive subordinator. Our formulation is based on a Linear-Quadratic control subject to a singular terminal condition. The Ornstein-Uhlenbeck bridge, we develop, is written as a limit of the obtained optimally controlled processes, and is shown to admit an explicit expression. Its extension with self-excitement is also considered. The terminal condition is confirmed to be satisfied by the obtained process both analytically and numerically. The methods are also applied to a streamflow regulation problem using a real-life dataset.
Systematic attack design is essential to understanding the vulnerabilities of cyber-physical systems (CPSs), to better design for resiliency. In particular, false data injection attacks (FDIAs) are well-known and have been shown to be capable of bypassing bad data detection (BDD) while causing targeted biases in resulting state estimates. However, their effectiveness against moving horizon estimators (MHE) is not well understood. In fact, this paper shows that conventional FDIAs are generally ineffective against MHE. One of the main reasons is that the moving window renders the static FDIA recursively infeasible. This paper proposes a new attack methodology, moving-horizon FDIA (MH-FDIA), by considering both the performance of historical attacks and the current system's status. Theoretical guarantees for successful attack generation and recursive feasibility are given. Numerical simulations on the IEEE-14 bus system further validate the theoretical claims and show that the proposed MH-FDIA outperforms state-of-the-art counterparts in both stealthiness and effectiveness. In addition, \textcolor{blue}{an experiment on} a path-tracking control system of an autonomous vehicle shows the feasibility of the MH-FDIA in real-world nonlinear systems.
Stefano Spinelli, Marcello Farina, Andrea Ballarino
Optimal management of thermal and energy grids with fluctuating demand and prices requires to orchestrate the generation units (GU) among all their operating modes. A hierarchical approach is proposed to control coupled energy nonlinear systems. The high level hybrid optimization defines the unit commitment, with the optimal transition strategy, and best production profiles. The low level dynamic model predictive control (MPC), receiving the set-points from the upper layer, safely governs the systems considering process constraints. To enhance the overall efficiency of the system, a method to optimal start-up the GU is here presented: a linear parameter varying MPC computes the optimal trajectory in closed-loop by iteratively linearising the system along the previous optimal solution. The introduction of an intermediate equilibrium state as additional decision variable permits the reduction of the optimization horizon,while a terminal cost term steers the system to the target set-point. Simulation results show the effectiveness of the proposed approach.
Abstract The Biforcation Structure which is located at the branching of the Moilong and Mansahang Rivers, Banggai District functions as a weir at the Moilong Free Intake and distributes water proportionally to its downstream, so that it can function as a flood control structure and at the same time supply water to the Mansahang (Toili) Weir. The flood incident on June 18, 2019 resulted in the collapse of the Biforcation structure so that it submerged seven villages in Moilong District. The restructure of the Biforcation is very much needed by the community to provide a sense of security from the destructive power of water and irrigation water supply. To prepare for the development, a research was conducted with the aim of knowing the economic feasibility of the construction of the Biforcation and the flood conrol structure. Prior to construction, a study was conducted with the aim of determining the economic feasibility of the Biforcation construction and the flood control structure. The research method used analytical methods, quantitative approaches, and conducted interviews with the relevant agencies. The data obtained are reviewed and analyzed using several assumptions of loan interest rates, and loan times. Calculation of financial analysis using the calculation of Net Present Value (NPV) and Benefit Cost Ratio (BCR). The results of the study concluded that the construction ldingof Biforcation structures and flood control structures was feasible. The results of the Net Present Value (NV) are positive at 10% and 12% interest rates. Interest 10%, benefit Cost Ratio (BCR) 6,536 (12 years), BCR 5,675 (10 years), BCR 4,633 (8 years). Interest 12%, Benefit Cost Ratio (BCR) 5,798 (12 years), Benefit Cost Ratio (BCR) 5,097 (10 years) Benefit Cost Ratio (BCR) 4,219 (8 years).
Kanghong Shi, Nastaran Nikooienejad, Ian R. Petersen
et al.
In this paper, we show that a hybrid integrator-gain system (HIGS) is a nonlinear negative imaginary (NNI) system. We prove that the positive feedback interconnection of a linear negative imaginary (NI) system and a HIGS is asymptotically stable. We apply the HIGS to a MEMS nanopositioner, as an example of a linear NI system, in a single-input single-output framework. We analyze the stability and the performance of the closed-loop interconnection in both time and frequency domains through simulations and demonstrate the applicability of HIGS as an NNI controller to a linear NI system.
In these notes we collect some results from several of the authors' works in order to make available a single source and show how the approximate geometric methods for regulation have been developed, and how the control design strategy has evolved from the theoretical methods, involving the regulator equations, to what we now call the regularized controller. In between these two extremes we developed, in a series of works, a fairly rigorous analysis of the regularization scheme leading to the regularized dynamic regulator equations and an iterative scheme that produces very accurate tracking and disturbance rejection control laws. In our most recent work we have extended dynamic regulator equations to what we now refer to as the regularized controller. This new formulation has only recently being applied to examples including linear and nonlinear delay equations.
Recent advances in data sensing and processing technologies enable data-driven control of high-voltage direct-current (HVDC) systems for improving the operational stability of interfacing power grids. This paper proposes an optimal data-driven control strategy for an HVDC system with line-commutated converters (LCCs), wherein the dc-link voltage and current are optimally regulated at distinct HVDC terminals to improve frequency regulation (FR) in both rectifier- and inverter-side grids. Each HVDC converter is integrated with feedback loops for regulation of grid frequency and dc-link voltage in a localized manner. For optimal FR in both-side grids, a data-driven model of the HVDC-linked grids is then developed to design a data-driven linear quadratic Gaussian (LQG) regulator, which is incorporated with the converter feedback loops. Case studies on two different LCC HVDC systems are performed using the data-driven models, which are validated via comparisons with physics-based models and comprehensive MATLAB/SIMULINK models. The results of the case studies confirm that the optimal data-driven control strategy successfully exploits the fast dynamics of HVDC converters; moreover, cooperation of the HVDC system and synchronous generators in both-side grids is achieved, improving real-time FR under various HVDC system specifications, LQG parameters, and inertia emulation and droop control conditions.
Gunting River which is located in Jombang Regency of East Java Province, Indonesia encounters frequent flood event almost every year. It causes many problems in transportation, health, and economic activity. Thus, flood control which has been implemented in this area needs to evaluate. Design flood was analyzed using HEC-HMS 4.0 Software, while the hydraulic modeling used the unsteady flow simulation model by HEC-RAS 5.0.3 Software. The flood control simulation was conducted with 2 and 10-years return period. The simulation results with the normalization for 2-years (Q2) and 10-years return period (Q10) can effectively accommodate the exceed of flood discharge and lower the depth of runoff depth. The combination of normalization and embankment for can drain the maximum discharge up to 508.75 m3/s, and decrease run-off depth of 2.65 m. The land conservation of 17.8 km2 of the upper area in the watershed has lower the flood depth up to 0.16 m.
Anatoly Zlotnik, Kaarthik Sundar, Aleksandr M. Rudkevich
et al.
We formulate an economic optimal control problem for transport of natural gas over a large-scale transmission pipeline network under transient flow conditions. The objective is to maximize economic welfare for users of the pipeline system, who provide time-dependent price and quantity bids to purchase or supply gas at metered locations on a system with time-varying injections, withdrawals, and control actions of compressors and regulators. Our formulation ensures that pipeline hydraulic limitations, compressor station constraints, operational factors, and pre-existing contracts for gas transport are satisfied. A pipeline is modeled as a metric graph with gas dynamics partial differential equations on edges and coupling conditions at the nodes. These dynamic constraints are reduced using lumped elements to a sparse nonlinear differential algebraic equation system. A highly efficient temporal discretization scheme for time-periodic formulations is introduced, which we extend to develop a rolling-horizon model-predictive control scheme. We apply the computational methodology to a pipeline system test network case study. In addition to the physical flow and compressor control solution, the optimization yields dual functions that we interpret as the time-dependent economic values of gas at each location in the network.
This paper introduces a control strategy to simultaneously achieve asymptotic stabilization and transient frequency regulation of power networks. The control command is generated by iteratively solving an open-loop control cost minimization problem with stability and transient frequency constraints. To deal with the non-convexity of the stability constraint, we propose a convexification strategy that uses a reference trajectory based on the system's current state. We also detail how to employ network partitions to implement the proposed control strategy in a distributed way, where each region only requires system information from neighboring regions to execute its controller.
In this paper, a Multiple Models Adaptive Fuzzy Logic Controller (MM-AFLC) with Neural Network Identification is designed to control the unmanned vehicle in Intelligent Autonomous Parking System. The objective is to achieve robust control while maintaining a low implementation cost. The proposed controller design incorporates the following control theorems -- non-linear system identification using neural network, fuzzy logic control, adaptive control as well as multiple models adaptation. Such integration ensures superior performance compared to previous work. The generalized controller can be applied to different systems without prior knowledge of the actual plant model. In the intelligent autonomous parking system, the proposed controller can be used for both vehicle speed control and steering wheel turning. With a multiple model adaptive fuzzy logic controller, robustness can be also assured under various operating environments regardless of unpredictable disturbances. Last but not least, comparative experiments have also demonstrated that systems equipped with the new controller are able to achieve faster and smoother convergence.
This paper presents an explicit solution to decentralized control of a class of spatially invariant systems. The problem of optimal $H_2$ decentralized control for cone causal systems is formulated. Using Parseval's identity, the optimal $H_2$ decentralized control problem is transformed into an infinite number of model matching problems with a specific structure that can be solved efficiently. In addition, the closed-form expression (explicit formula) of the decentralized controller is derived for the first time. In particular, it is shown that the optimal decentralized controller is given by a specific positive feedback scheme. A constructive procedure to obtain the state-space representation of the decentralized controller is provided. A numerical example is given and compared with previous works which demonstrate the effectiveness of the proposed method.
With climate change, managing the variability in changing watershed conditions, from floods to droughts, is a challenge. Having the necessary information, in a timely manner, is critical for proper planning and implementing mitigating measures. South Nation Conservation's (SNC) recent project with IBM and Aquanty, the development of a data-assimilation and hydrological simulation platform, aims to improve short term (1 - 14 d) forecasting of flooding and droughts. The project will construct a real-time hydrologic model for South Nation Watershed. This fully integrated 3-dimensional groundwater - surface water model is dynamically coupled to state-of-the-art high resolution (4000 m) short-term weather forecasts (provided by IBM's Weather Company) and real-time field sensors throughout the Watershed. The model will enhance SNC's Flood Forecasting and Warning and Low Water Response programs with more accurate forecasts; utilizing over 200 local weather data points (compared to the previous 3 Environment Canada stations) to capture the diversity of weather patterns across the Watershed. The new real-time sensors to collect precipitation (rain and snow) and soil moisture will allow the model to better predict how forecasted weather events will impact watershed conditions. For example, is a significant rainfall event more likely to runoff into nearby watercourses or soak into the ground. Beyond its direct application to the South Nation Watershed, this project will provide a proof-of-concept demonstration of how the latest in weather forecasting and hydrologic modelling technologies can support water resources management under increasingly variable climate/weather conditions. It is anticipated that this project will provide far-reaching benefit to SNC, local producers, municipalities, and rural landowners throughout the region This project was made possible with funding support from Agriculture and Agri-Food Canada.