Browsing by Author "Seo, Min-Guk"

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    Closed-loop analysis with incremental backstepping controller considering measurement bias
    (Elsevier, 2019-11-25) Jeon, Byoung-Ju; Seo, Min-Guk; Shin, Hyosang; Tsourdos, Antonios
    In this paper, closed loop system characteristics with an incremental backstepping controller are investigated through theoretical analysis when both measurement biases and model uncertainties exist. Incremental backstepping algorithm is proposed in previous studies to reduce model dependency of classical backstepping algorithm with additional measurements about state derivatives and control surface deflection angles. This research enables to have following critical understandings especially about the effects of biases on these additional measurements to system characteristics with incremental backstepping method. First, these biases do not affect a characteristic equation, so they do not have any influence about a condition for absolute stability. Second, these biases cause a steady state error, and model uncertainty in control effectiveness information starts to have an impact to it when these biases are additionally considered.
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    Decentralized task allocation for multiple UAVs with task execution uncertainties
    (IEEE, 2020-10-06) Liu, Ruifan; Seo, Min-Guk; Yan, Binbin; Tsourdos, Antonios
    This work builds on a robust decentralized task allocation algorithm to address the multiple unmanned aerial vehicle (UAV) surveillance problem under task duration uncertainties. Considering the existing robust task allocation algorithm is computationally intensive and also has no optimality guarantees, this paper proposes a new robust task assignment formulation that reduces the calculation of robust scores and provides a certain theoretical guarantee of optimality. In the proposed method, the Markov model is introduced to describe the impact of uncertain parameters on task rewards and the expected score function is reformulated as the utility function of the states in the Markov model. Through providing the high-precision expected marginal gain of tasks, the task assignment gains a better accumulative score than the state of arts robust algorithms do. Besides, this algorithm is proven to be convergent and could reach a prior optimality guarantee of at least 50%. Numerical Simulations demonstrate the performance improvement of the proposed method compared with basic CBBA, robust extension to CBBA and cost-benefit greedy algorithm.
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    Detect and avoid considerations for safe sUAS operations in urban environments
    (IEEE, 2021-11-15) Celdran Martinez, Victor; Ince, Bilkan; Kumar Selvam, Praveen; Petrunin, Ivan; Seo, Min-Guk; Anastassacos, Edward; Royall, Paul G.; Cole, Adrian; Tsourdos, Antonios; Knorr, Sebastian
    Operations involving small Unmanned Aerial Systems (sUAS) in urban environments are occurring ever more frequently as recognized applications gain acceptance, and new use cases emerge, such as urban air mobility, medical deliveries, and support of emergency services. Higher demands in these operations and the requirement to access urban airspace present new challenges in sUAS operational safety. The presence of Detect and Avoid (DAA) capability of sUAS is one of the major requirements to its safe operation in urban environments according to the current legislation, such as the CAP 722 in the United Kingdom (UK). The platform or its operator proves a full awareness of all potential obstacles within the mission, maintains a safe distance from other airspace users, and, ultimately, performs Collision Avoidance (CA) maneuvers to avoid imminent impacts. Different missions for the defined scenarios are designed and performed within the simulation model in Software Tool Kit (STK) software environment, covering a wide range of practical cases. The acquired data supports assessment of feasibility and requirements to real-time processing. Analysis of the findings and simulation results leads to a holistic approach to implementation of sUAS operations in urban environments, focusing on extracting critical DAA capability for safe mission completion. The proposed approach forms a valuable asset for safe operations validation, enabling better evaluation of risk mitigation for sUAS urban operations and safety-focused design of the sensor payload and algorithms.
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    New design methodology for impact angle control guidance for various missile and target motions
    (IEEE, 2017-09-26) Seo, Min-Guk; Lee, Chang-Hun; Tahk, Min-Jea
    This brief introduces a new design methodology for impact angle control guidance (IACG) laws. The proposed methodology can extend any proven homing guidance laws to their impact angle control versions if the expressions of the estimated terminal flight path angles under those guidance laws are given. The time derivatives of the estimated terminal flight path angles are obtained as functions of the guidance commands. The IACG versions of the homing guidance laws are derived from those functions and the desired error dynamics of the estimated terminal flight path angle. The guidance law of each IACG version has two terms: the first term maintains the characteristics and capturability of the original guidance law and the second term drives the estimated terminal flight path angle to converge to the specified flight path angle. When a well-understood homing guidance law for a certain combination of target and missile models is given, an IACG law for that combination is easily derived without reformulating the guidance problem again. The usefulness of the proposed method is demonstrated by several examples, deriving new IACG laws for various target and missile models.
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    New insights into guidance laws with terminal angle constraints
    (AIAA, 2018-04-19) Lee, Chang-Hun; Seo, Min-Guk
    Introduction : In the field of guidance technology, advanced guidance laws are being developed using various control theories such as optimal control [1,2], sliding mode control [3–6], H-infinity control [7], the state-dependent Riccati equation [8], the Lyapunov theory [9], the geometric control theory [10], predictive control [11,12], and feedback linearization control [13]. The general procedure of this approach is to first establish the guidance problem to be solved and to define the guidance geometry and kinematics equation corresponding to the guidance problem. A guidance law is then systematically designed in such a way that an appropriate control theory is applied to a predetermined guidance problem. The potential importance of this approach is that a number of advanced guidance laws can be newly developed, depending on the combinations of guidance problems and control theories. Therefore, recent trends in the design of guidance laws focus on finding a new combination of guidance problems and control theory to obtain a new guidance law that is superior to the conventional guidance law. Although previous research has focused more on the methodology and the design process itself, little effort has been made toward understanding the characteristics of the newly developed guidance laws.
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    Soil moisture retrieval from airborne multispectral and infrared images using convolutional neural network
    (Elsevier, 2020-11) Seo, Min-Guk; Shin, Hyosang; Tsourdos, Antonios
    This paper deals with the modeling of soil moisture retrieval from multispectral and infrared (IR) images using convolutional neural network (CNN). Since it is difficult to measure the soil moisture level of large fields, it is essential to retrieve soil moisture level from remotely sensed data. Quadrotor unmanned aerial vehicle (UAV) is considered as sensing platform in order to acquire data with high spatial resolution at anytime by non-experts. With considerations both on the availability of sensors for the platform and the information needed to overcome the effects of the canopies covering soil, IR and multispectral images are selected to be used for soil moisture retrieval. In order to prevent information loss by the calculation of parameters from measurements and enhance the applicabiliy for online operations, CNN is applied for the construction of soil moisture retrieval model to use the sensor measurement images directly as input data. Training and testing are conducted for the proposed CNN-based soil moisture retrieval model using the data from actual quadrotor flight over an agricultural field.
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    Soil moisture retrieval model design with multispectral and infrared images from unmanned aerial vehicles using convolutional neural network
    (MDPI, 2021-02-23) Seo, Min-Guk; Shin, Hyosang; Tsourdos, Antonios
    This paper deals with a soil moisture retrieval model design with airborne measurements for remote monitoring of soil moisture level in large crop fields. A small quadrotor unmanned aerial vehicle (UAV) is considered as a remote sensing platform for high spatial resolutions of airborne images and easy operations. A combination of multispectral and infrared (IR) sensors is applied to overcome the effects of canopies convering the field on the sensor measurements. Convolutional neural network (CNN) is utilized to take the measurement images directly as inputs for the soil moisture retrieval model without loss of information. The procedures to obtain an input image corresponding to a certain soil moisture level measurement point are addressed, and the overall structure of the proposed CNN-based model is suggested with descriptions. Training and testing of the proposed soil moisture retrieval model are conducted to verify and validate its performance and address the effects of input image sizes and errors on input images. The soil moisture level estimation performance decreases when the input image size increases as the ratio of the pixel corresponding to the point to estimate soil moisture level to the total number of pixels in the input image, whereas the input image size should be large enough to include this pixel under the errors in input images. The comparative study shows that the proposed CNN-based algorithm is advantageous on estimation performance by maintaining spatial information of pixels on the input images.
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    Understandings of classical and incremental backstepping controllers with model uncertainties
    (IEEE, 2019-11-11) Jeon, Byoung-Ju; Seo, Min-Guk; Shin, Hyosang; Tsourdos, Antonios
    This paper suggests closed-loop analysis results for both classical and incremental backstepping controllers considering model uncertainties. First, transfer functions with each control algorithm under the model uncertainties, are compared with the ones for the nominal case. The effects of the model uncertainties on the closed-loop systems are critically assessed via investigations on stability conditions and performance metrics. Second, closed-loop characteristics with classical and incremental backstepping controllers under the model uncertainties are directly compared using derived common metrics from their transfer functions. This comparative study clarifies how the effects of the model uncertainties to the closed-loop system become different depending on the applied control algorithm. It also enables understandings about the effects of additional measurements in the incremental algorithm. Third, case studies are conducted assuming that the uncertainty exists only in one aerodynamic derivative estimate while the other estimates have true values. This facilitates systematic interpretations on the impacts of the uncertainty on the specific aerodynamic derivative estimate to the closed-loop system.
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    Understandings of incremental backstepping controller considering measurement delay with model uncertainty
    (Elsevier, 2020-12-07) Jeon, Byoung-Ju; Seo, Min-Guk; Shin, Hyosang; Tsourdos, Antonios
    In this paper, closed loop characteristics with an incremental backstepping (IBKS) controller are investigated with consideration of measurement delays and model uncertainties. To judge absolute stability of the system, a systematic analysis framework is proposed which examines the existence of unstable poles from a derived characteristic equation with high nonlinearity due to the considered measurement delays. One of the key findings from the analysis results is that the system is stable only when a specific relationship between the measurement delays is satisfied and this stability condition is affected by the model uncertainty. Critical understandings about individual and integrated effects of the measurement delays and the model uncertainties to the system are suggested through a comparative study. Verification and validation of the obtained properties from the framework are performed through simulations.
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    Understandings of the incremental backstepping control through theoretical analysis under the model uncertainties
    (IEEE, 2018-10-29) Jeon, Byoung-Ju; Seo, Min-Guk; Shin, Hyosang; Tsourdos, Antonios
    In this paper, theoretical analysis on the incremental backstepping control is suggested especially under the existence of model uncertainties. This algorithm is proposed in the previous studies by modifying the backstepping method to reduce model dependency. Because this method is a type of nonlinear control and the model uncertainties are assumed to be considered, it is difficult to have theoretical analysis, which causes lack of understandings about this algorithm. Therefore, this paper suggests closed-loop analysis with simplified dynamics under the model uncertainty. Transfer function is derived and poles, stability condition, steady state error, and settling time are presented. In addition, the effects of model uncertainties and gains are identified through analysis. Proposed analysis is meaningful in terms of establishing critical understandings about the algorithm, even though the simplified dynamics is applied for analysis purpose.