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检索条件"任意字段=10th IEEE Data Driven Control and Learning Systems Conference, DDCLS 2021"

共 359 条记录，以下是241-250 订阅

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On initial value problem for fractional-order Kalman filters of linear continuous-time fractional-order systems 10

On initial value problem for fractional-order Kalman filters...

引用

10th ieee data driven control and learning systems conference, ddcls 2021

作者： Yang, Chuang Gao, Zhe Ma, Xiaoou Miao, Yue Shool of Mathematics Liaoning University Shenyang110036 China College of Light Industry Liaoning University Shenyang110036 China Department of Control Science and Engineering Jilin University Changchun130025 China Liaoning Science and Technology Museum Shenyang110067 China

ISBN: (纸本)9781665424233

To realize the state estimation of linear continuous-time fractional-order systems, the fractional-order Kalman filter (FOKF) is designed to solve problem on the initial value influence. By using the model transformation, an equivalent equation is obtained such that the state estimation of the transformed model is independent of the initial value. the dimension of the equivalent equation is the same as that of the original system, and the proposed FOKF algorithm based on equivalent equation can effectively reduce the initial value influence on the state estimation. Finally, the effectiveness of the solutions for initial value problem for FOKF is validated by the given simulation example. © 2021 ieee.

关键词： State estimation

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RMPC Fault-Tolerant control for Flexible HSV with Polytopic LPV model 10

RMPC Fault-Tolerant Control for Flexible HSV with Polytopic ...

引用

10th ieee data driven control and learning systems conference, ddcls 2021

作者： Yang, Xiaohe Lv, Weijie Hu, Chaofang Hu, Yongtai School of Electrical and Information Engineering Tianjin University Tianjin300072 China Aviation Key Laboratory of Science and Technology on Aircraft Control Facri Xi'an710065 China

ISBN: (纸本)9781665424233

In this paper, a robust fault-tolerant controller is designed for flexible air-breathing hypersonic vehicle (HSV) with actuator fault and external disturbance. Firstly, the polytopic linear parameter variable (LPV) model of flexible HSV is established through Jacobian linearization and tensor product (TP) method. the integral terms of height error and velocity error are introduced to eliminate the steady state error, and the extended system is constructed. the actuator loss of effectiveness fault is separated from the control input and lumped into disturbance term together with the external disturbance. And the extended polytopic LPV faulty system is obtained. Secondly, the fault-tolerant controller is designed based on robust model predictive control (RMPC). the state feedback control law is obtained by solving the linear matrix inequality (LMI) optimization problem. Norm bounded theory is introduced to suppress the lumped disturbance term of the real system. the disturbed and undisturbed state are guaranteed to be in the same invariant set. Finally, numerical simulations have demonstrated the fault-tolerant performance of the developed controller. © 2021 ieee.

关键词： Hypersonic vehicles

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Adaptive optimal setpoint tracking control of unknown linear continuous-time systems 10

Adaptive optimal setpoint tracking control of unknown linear...

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10th ieee data driven control and learning systems conference, ddcls 2021

作者： Zhao, Jianguo Zhang, Hengjin Yang, Chunyu School of Information and Control Engineering China University of Mining and Technology Xuzhou221116 China School of Electrical and Power Engineering China University of Mining and Technology Xuzhou221116 China

ISBN: (纸本)9781665424233

this paper presents a novel adaptive optimal setpoint tracking control method for unknown linear continuous-time systems with constant disturbances using adaptive dynamic programming (ADP). Compared with the existing works, the proposed ADP-based tracking control and disturbance rejection framework has the following advantages: 1) By defining a new performance index, its boundedness is guaranteed without additional discount factor and thus the influence of discount factor on the stability of the closed-loop system is eliminated;2) According to Lyapunov theory, we prove that the tracking error converges to zero rather than the bounded tracking error of the existing results;3) It is not to assume that the knowledge of the output matrix is known and a complete model-free tracking control scheme is achieved;4) the external disturbance input is not required to be measurable, so the proposed method has more practical engineering application. Finally, the feasibility and effectiveness of the proposed tracking controller are verified by the rougher flotation operational processes simulation. © 2021 ieee.

关键词： Closed loop systems

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A Novel Electric Vehicle Braking Energy Recovery Method Based on Model Free Adaptive control Algorithm with Input and Output Constraints 10

A Novel Electric Vehicle Braking Energy Recovery Method Base...

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10th ieee data driven control and learning systems conference, ddcls 2021

作者： Liu, Shida Li, Zhen Ji, Honghai Hou, Zhongsheng Fan, Lingling School of Electrical and Control Engineering North China University of Technology Beijing100000 China School of Automation Qingdao University Qinhuangdao266071 China School of Automation Beijing Information Science and Technology University Beijing100192 China

ISBN: (纸本)9781665424233

this study focus on the problem of pure electric vehicle's braking energy recovery with the uncertain dynamic external factors. For this problem, a novel model free adaptive control with input and output constraints (IOC-MFAC) method is introduced. the dynamic process can be considered as a nonlinear two inputs and two outputs system with hydraulic braking torque and motor braking torque as inputs and braking energy and braking deceleration as outputs. By using IOC-MFAC, the constraints of limitation of current and voltage on the maximum motor braking torque and the constraints of the vehicle's comfort on braking deceleration are considered. Consequently, the recovered energy is controlled in a stable range while guaranteeing the energy recovery to prolong the storage battery's operating life. the major advantages of IOC-MFAC are that not only the controller is designed only with input and output data of the regenerative brake control system, but also the constraints of the system inputs and outputs are considered. Further, the efficiency of IOC-MFAC is verified with a series of numerical simulations. © 2021 ieee.

关键词： Regenerative braking

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Extended-State-Observer Based Finite-Time control for Spacecraft Attitude Tracking on SO(3) 10

Extended-State-Observer Based Finite-Time Control for Spacec...

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10th ieee data driven control and learning systems conference, ddcls 2021

作者： Shi, Zhongjun Shao, Changbao Zhang, Jianqiao Zhao, Yanbin Shanghai Academy of Spaceflight Technology Shanghai201109 China Shanghai Institute of Satellite Engineering Shanghai201109 China Shanghai Academy of Spaceflight Technology Beijing Rd Center Beijing100081 China

ISBN: (纸本)9781665424233

this paper addresses the spacecraft attitude tracking control problem in the presence of model uncertainties, external disturbances, and actuator saturation by proposing a finite-time controller integrated with an extended-state-observer (ESO). More specifically, in view of the aforementioned drawbacks, the attitude dynamics are developed on the Special Orthogonal group SO(3) directly, and the system total disturbance is reconstructed by using anti-windup technique. It should be noted that singularities and ambiguities associated with other attitude representations are avoided in the developed model. then, a finite-time ESO is designed to observe the disturbance. Based on the estimated information, a coordinate-free fast terminal sliding mode controller (FTSMC) is developed to ensure the finite-time stability of the closed-loop system. the stability of the closed-loop system is proved using Lyapunov stability theory. Finally, numerical simulations are conducted to demonstrate fine performance of the finite-time observer and the controller for the attitude tracking control system. © 2021 ieee.

关键词： controllers

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Lognormal and Gamma mixed negative binomial model for defects prediction in steel products 10

Lognormal and Gamma mixed negative binomial model for defect...

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10th ieee data driven control and learning systems conference, ddcls 2021

作者： He, Bocun Wei, Chihang Zhu, Zheren Zhang, Xinmin Song, Zhihuan State Key Laboratory of Industrial Control Technology Institute of Industrial Process Control College of Control Science and Engineering Zhejiang University Zhejiang Hangzhou310027 China

ISBN: (纸本)9781665424233

In steel industry, variations in the manufacturing process cause various defects in products. thus, it is important to forecast the instance of defects in real time. However, ordinary probability models are not adequate enough to describe the observed defect count data due to the nonnegative integers characteristics, high imbalanced distribution, and overdispersion. To explicitly account for these distinctive characteristics, the present work proposes an online quality forecast system developed on the lognormal and Gamma mixed negative binomial (LGNB) model. LGNB incorporates the lognormal and Gamma distributions to account for the random effect and dispersion in the data. In addition, the drawbacks of the ordinary negative binomial (NB) model such as low robustness and biased parameter estimation against data with large dispersion are conquered by the LGNB model. the efficacy of the LGNB model was validated via its application to the defect data of an ironmaking plant. the results show that the LGNB model improves the forecast accuracy as compared to the PLS, Poisson, and NB models, respectively. © 2021 ieee.

关键词： Defects

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New Stochastic Gradient Descent Algorithm via Lagrange-type 1-step-ahead Numerical Differentiation 10

New Stochastic Gradient Descent Algorithm via Lagrange-type ...

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10th ieee data driven control and learning systems conference, ddcls 2021

作者： Luo, Wendi Lin, Mei Chen, Liangming Peng, Bo Department of Information Science and Engineering Lanzhou University Lanzhou730000 China Chongqing Institute of Green and Intelligent Technology Chinese Academy of Sciences Chongqing400714 China

ISBN: (纸本)9781665424233

the method that stochastic gradient descent (SGD) algorithm uses to update the undetermined parameter in the iterative process can be viewed as a rudimentary forward Euler method in the perspective of numerical differentiation. In order to overcome the connatural imperfection of the forward differentiation rule and computation error of the SGD algorithm, a new algorithm is obtained by substituting the original updating rule in SGD with the Lagrange-step l-step-ahead differentiation rule. In addition, extensive experiments between the original SGD algorithm and the modified algorithm are conducted to analyze the convergence. Empirical results demonstrate that Lagrange-step 1-step-ahead can not be used to the SGD algorithm and the new algorithm does not converge. theoretical analysis is given to explain this result. © 2021 ieee.

关键词： Stochastic systems

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A Novel YOLOv5-based Anomalous Object Detection Algorithm in Buses 11

A Novel YOLOv5-based Anomalous Object Detection Algorithm in...

引用

11th ieee data driven control and learning systems conference, ddcls 2022

作者： Liu, Shida Li, Qingyi Ji, Honghai Wang, Li Zhang, Xiaoping He, Zhonghe School of Electrical and Control Engineering North China University of Technology Beijing100000 China

ISBN: (数字)9781665496759

ISBN: (纸本)9781665496759

In this work, a novel abnormal objects analyzing and detecting (AOAD) algorithm inside the bus is proposed, and the AOAD algorithm is further applied to the practical bus by designing an embedded video analysis system. the proposed algorithm is based on the deep learning YOLOv5 (You Only Look Once) algorithm, which has better timeliness and accuracy in detecting anomalous objects. the anomalous objects with larger boxes are detected by building an anomalous object dataset for training. the effectiveness and applicability of the proposed algorithm is verified by extensive experiments on video data based on real buses. © 2022 ieee.

关键词： Buses

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Reinforcement learning Tracking control for Unknown Continuous Dynamic systems 10

Reinforcement Learning Tracking Control for Unknown Continuo...

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10th ieee data driven control and learning systems conference, ddcls 2021

作者： Ye, Linqi Li, Jiayi Wang, Changliang Liu, Houde Liang, Bin Center of Intelligent Control and Telescience Tsinghua Shenzhen International Graduate School Tsinghua University Shenzhen518055 China School of Mechanical Engineering and Automation Harbin Institute of Technology Shenzhen Shenzhen518055 China Shanghai Academy of Spaceflight Technology Shanghai201109 China Navigation and Control Research Center Tsinghua University Department of Automation Beijing100084 China

ISBN: (纸本)9781665424233

Reinforcement learning tracking control (ILC) is proposed to solve the traditional iterative learning control (ILC) problem. For unknown continuous dynamic systems, precision output tracking is achieved for a given trajectory after several times of trials. the controller is composed of a feedback part and a feedforward part, where the feedback part uses linear states feedback to stabilize the system and the feedforward part is a time-dependent signal to ensure precision tracking. the desired trajectory is defined in a finite time interval and the controller is applied repeatedly to the system with the feedforward signal updated by reinforcement learning in each trial. the tracking problem is treated as a black-box optimization problem where the integral quadratic tracking error is a cost to be minimized under input constraints. In order to apply reinforcement learning, the feedforward signal is approximated by spline interpolation of a few representative points distributed uniformly along the tracking time interval. A search strategy based on squeeze theorem is adopted to update the value of the representative points to achieve minimum tracking error cost. the cart pole example illustrates the effectiveness of the proposed method. © 2021 ieee.

关键词： Reinforcement learning

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Model-Free Adaptive Sliding Mode control for Discrete-Time Nonlinear systems With Sensor Fault and Prescribed Performance 11

Model-Free Adaptive Sliding Mode Control for Discrete-Time N...

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11th ieee data driven control and learning systems conference, ddcls 2022

作者： Hao, Li-Ying Yang, Sen Liu, Dong College of Marine Electrical Engineering Dalian Maritime University Dalian China School of Automation Shenyang Aerospace Universtiy Shenyang China

ISBN: (数字)9781665496759

ISBN: (纸本)9781665496759

this paper concentrates on the issue of a model-free adaptive sliding mode control approach for a family of discrete-time single-input and single-output (SISO) nonlinear systems with sensor fault and prescribed performance. First of all, we set out to build an approximator that is based on radial basis function neural network to estimate the sensor fault offline, and for the purpose of restricting the tracking error in a prescribed region when the sensor fault occurs, the prescribed performance control (PPC) method is adopted. then, we consider sliding mode control strategy to achieve the stability and ensure the convergence of tracking error within PPC scheme. We have to point out that all of these works above are carried in model-free adaptive control (MFAC) framework. Finally, a simulation example result is given to validate the effectiveness of the algorithm proposed in this article. © 2022 ieee.

关键词： Sliding mode control

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