Article
Engineering, Marine
Wei Zhang, Qiang Wang, Wenhua Wu, Xue Du, Yu Zhang, Peiyu Han
Summary: In this paper, a novel Three-Dimensional trajectory tracking method for an Underwater Unmanned Vehicle (UUV) with external disturbances is proposed using Event-triggered Nonlinear Model Predictive Control (ENMPC). The 5-Degrees of Freedom (5-DoF) model of UUV is represented using both kinematics and dynamics. A Nonlinear Model Predictive Control (NMPC) algorithm is designed for 3-D trajectory tracking, and the feasibility and stability of the control method and closed-loop system are proved. The simulation results demonstrate the effectiveness of the proposed algorithm, showing that ENMPC strategy can effectively reduce computation iterations and improve tracking efficiency compared to Traditional Model Predictive Control (TMPC) and NMPC algorithm.
Article
Chemistry, Analytical
Yuxi Li, Gang Hao
Summary: This paper proposes an improved modified model predictive control algorithm by combining the Sage-Husa adaptive Kalman filter (SHAKF), the cubature Kalman filter (CKF), and the back-propagation neural network (BPNN) to mitigate the negative impacts of system noise on energy-optimal adaptive cruise control (EACC) and achieve further energy reduction.
Article
Engineering, Electrical & Electronic
Vishwas Vasuki Gautam, Renuka Loka, Alivelu M. Parimi
Summary: In order to address the issues in large power systems, a novel combined state estimation and optimal control method based on Cubature Kalman filter (CKF) and Linear Quadratic Regulator (LQR) is proposed. The proposed controller curbs frequency deviations by calculating optimal feedback gain values using LQR and estimates state variables using CKF algorithm. The efficacy of CKF-LQR control is demonstrated through simulation cases on small and large-scale power systems, comparing with existing EKF-LQR control, VSG-based control, and conventional PID control. The results show that the proposed control is robust and effective in regulating system frequency compared to other controllers.
ELECTRIC POWER SYSTEMS RESEARCH
(2023)
Article
Acoustics
Keyvan Karim Afshar, Ali Javadi
Summary: This paper presents an effective control method for stabilizing and tracking the trajectory of an electromagnetic levitation system using feedback linearization controller and Linear Quadratic Regulator (LQR), along with the utilization of a nonlinear observer and nonlinear Kalman filter to estimate the unmeasured states and system parameter. Simulation results demonstrate that the proposed method is efficient in stabilizing the levitated object and attenuating disturbance and uncertainty in the system.
JOURNAL OF SOUND AND VIBRATION
(2021)
Article
Chemistry, Analytical
Yuqing Ni, Xiaochen Liu, Chao Yang
Summary: This paper proposes a hybrid scheduling approach based on time and event triggers for remote state estimation, which improves the estimation performance with limited communication resources.
Article
Automation & Control Systems
Yamei Ju, Dan Liu, Derui Ding, Guoliang Wei
Summary: This paper deals with the distributed cubature Kalman filtering (DCKF) problem for a class of discrete time-varying nonlinear systems subject to stochastic communication protocol (SCP). The SCP is introduced to randomly schedule information transmission among sensor nodes to avoid data collisions. The design of a DCKF under the spherical-radial cubature rule is proposed to guarantee an upper bound of the filtering error covariance using fundamental inequality. The usefulness of the DCKF is verified through a simulation example on induction machines.
ASIAN JOURNAL OF CONTROL
(2022)
Article
Computer Science, Information Systems
Shitong Cui, Le Liu, Wei Xing, Xudong Zhao
Summary: This paper addresses the problem of remote state estimation in a linear discrete invariant system using a smart sensor for measurements and local estimates, with communication based on event scheduling in the smart sensor. The MMSE estimator is introduced and Gaussian preserving event-based sensor scheduling is used to strike a balance between communication cost and estimation quality. The variation range of communication probability is calculated to aid in designing the event-triggered estimation policy, and simulation results demonstrate the effectiveness of the proposed event-triggered estimator.
Article
Automation & Control Systems
Yuan Liang, Yinya Li, Ye Chen, Andong Sheng
Summary: This paper proposes a novel event-triggered diffusion estimation strategy for sensor networks, which reduces energy consumption without compromising performance. Two event-triggered mechanisms are provided to schedule data transmissions, and an event-triggered cubature Kalman filtering (ET-CKF) algorithm is developed for incremental update. Furthermore, an event-triggered covariance intersection (ET-CI) fusion algorithm is provided for diffusion update, ensuring the consistency of fusion estimates. Simulation results verify the effectiveness and advantage of the proposed method.
SYSTEMS & CONTROL LETTERS
(2023)
Article
Automation & Control Systems
Hong-Sen Yan, Guo-Biao Wang
Summary: This article presents a tractable adaptive control scheme for stochastic nonlinear systems with time-varying delays. An adaptive embedded Cubature Kalman Filter is developed to realize robust state estimation. The proposed method utilizes the Multidimensional Taylor Network to evaluate dynamic performance and approximate the optimal policy. The effectiveness of the method is confirmed through numerical simulation.
INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL
(2023)
Article
Automation & Control Systems
Yang Yang, Xuefeng Si, Dong Yue
Summary: This paper investigates an event-triggered observer-based output feedback control issue for uncertain nonlinear systems, utilizing an event-triggered extended state observer to estimate unmeasurable states and compensate uncertainties and disturbances in real time. The proposed scheme combines backstepping method and active disturbance rejection control, with an event-triggered input for reducing communication rate between the controller and actuator, showing effectiveness in simulations.
INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL
(2021)
Article
Automation & Control Systems
Xiaoxu Lv, Peihu Duan, Zhisheng Duan, Guanrong Chen, Ling Shi
Summary: This article proposes an event-triggered variational Bayesian filter for remote state estimation with unknown and time-varying noise covariances. It utilizes a variational Bayesian method and a fixed-point iteration method to jointly estimate the posterior state vector and the unknown noise covariances under a stochastic event-triggered mechanism. The algorithm ensures low communication loads and excellent estimation performances for a wide range of unknown noise covariances. The performance of the proposed algorithm is demonstrated by tracking simulations of a vehicle.
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
(2023)
Article
Engineering, Electrical & Electronic
Xingzhen Bai, Xinlei Zheng, Leijiao Ge, Wenlong Liao, Kody Powell, Jiaan Zhang
Summary: In this paper, a dynamic event-based forecasting-aided state estimation (FASE) method is proposed to solve the state estimation problem of active distribution systems (ADS) with communication constraints and non-linear measurements. The method constructs a state-space model of ADS to describe system state evolution and uses a dynamic event-triggered scheme (ETS) to optimize data transmission. To handle non-linear measurements, the Gaussian integral is approximated using the spherical cubature rule. The proposed method also minimizes the upper bound of estimation error covariance and develops the dynamic event-triggered cubature Kalman filter (DET-CKF) algorithm for state estimation in ADS.
ELECTRIC POWER SYSTEMS RESEARCH
(2023)
Article
Automation & Control Systems
Sen Li, Zhen Li, Jian Li, Tyrone Fernando, Herbert Ho-Ching Iu, Qinglin Wang, Xiangdong Liu
Summary: This article introduces a novel filter for nonlinear remote state estimation in wireless sensor networks, based on event-triggered scheduling and CKF. By utilizing event-triggered scheduling and spherical-radial cubature rules, the proposed filter effectively balances communication burden and estimation accuracy, providing accurate estimations in non-Gaussian environments.
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
(2021)
Article
Automation & Control Systems
Long Chen, Bin Hu, Zhi-Hong Guan
Summary: This article studies the dynamic transmission-scheduling problem of rate-limited networked control systems with multiple loops. It proposes an adaptive event-triggered stochastic scheduling policy and a multiagent reinforcement learning algorithm to search for optimal scheduling parameters. The article also introduces an edge-assisted learning architecture and obtains explicit performance index of the optimal scheduling. Numerical examples validate the superiority of the proposed scheduling policy.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
(2022)
Article
Engineering, Aerospace
Jiao Wu, Ming Liu, Yili Wang, Xibin Cao
Summary: This paper investigates the cluster coordination problem of spacecraft swarms with non-negative defined directed communication topology in the context of multi-mission parallel operations. A plasticity-based echo state network is used to approximate the nonlinear term of relative attitude dynamics, and an improved event-triggered transmission mechanism is proposed to reduce redundant communication. A novel cluster coordination control law is developed based on the echo state neural network and the event-triggered transmission mechanism, and an extended control law is proposed for switching topologies.
AEROSPACE SCIENCE AND TECHNOLOGY
(2023)