Observer-Based Asynchronous Fault Detection for Conic-Type Nonlinear Jumping Systems and its Application to Separately Excited DC Motor

Peng, Cheng; Wang, Jiancheng; He, Shuping; Luan, Xiaoli; Liu, Fei

doi:10.1109/tcsi.2019.2949368

Cited by 101 publications

(47 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Amir Benloucif et al established a vehicle dynamics model based on the T-S fuzzy model for vehicle obstacle avoidance control, and proposed LMI steering control method based on the T-S fuzzy model [137]. Reference [138][139][140] focused on the the finite-time asynchronous output feedback control for a class of Markov jump systems subject to external disturbances and nonlinearities, an asynchronous output feedback controller and an observer-based finite-time asynchronous H∞ control law are designed. The the feasibility and validity of the proposed methods are illustrated through a DC motor experiment.…”

Section: Other Robust Control Methodsmentioning

confidence: 99%

Control Strategies on Path Tracking for Autonomous Vehicle: State of the Art and Future Challenges

Yao

Tian

Wang³

et al. 2020

IEEE Access

123

View full text Add to dashboard Cite

Autonomous vehicle technology aims to improve driving safety, driving comfort, and its economy, as well as reduce traffic accident rate. As the basic part of autonomous vehicle motion control module, path tracking aims to follow the reference path accurately, ensure vehicle stability and satisfy the robust performance of the control system. This paper introduces the representative control strategies, robust control strategies and parameter observation-based control strategies on path tracking for autonomous vehicle. Furthermore, the implementations and disadvantages are summarized. Most importantly, the critical review in this paper provides a list and discussion of the remaining challenges and unsolved problems on path tracking control.

show abstract

Section: Other Robust Control Methodsmentioning

confidence: 99%

Control Strategies on Path Tracking for Autonomous Vehicle: State of the Art and Future Challenges

Yao

Tian

Wang³

et al. 2020

IEEE Access

123

View full text Add to dashboard Cite

show abstract

“…Finally, numerical examples are given to verify the effectiveness of the proposed methods. The results may be extended to the time-delay system [35][36][37][38] and nonlinear jumping system [39,40] in the future.…”

Section: Numerical Examplementioning

confidence: 98%

Finite-Time Non-Fragile Extended Dissipative Control of Periodic Piecewise Time-Varying Systems

Liu

Deng

et al. 2020

IEEE Access

View full text Add to dashboard Cite

This paper studies the problem of finite-time non-fragile extended dissipative control for the periodic piecewise time-varying systems. First, based on the time-varying Lyapunov matrix and the matrix polynomial condition, a sufficient condition of finite-time boundedness for periodic piecewise time-varying subsystem is derived, and the finite-time extended dissipative performance is then analyzed. Furthermore, considering two types of time-varying controller perturbations, the non-fragile controllers are developed, which can guarantee the finite-time boundedness of periodic piecewise time-varying systems and satisfy the extended dissipative performance at the same time. Finally, numerical examples demonstrate the effectiveness of the proposed methods.

show abstract

“…Meanwhile, MJSs have been applied extensively in communication network which includes a routing framework for single agent in single and multiple states decision and planning [10]. MJSs approach is nonlinear for an "optimal" control problem in which the aim is to select actions that maximize some measure of long-term reward [11]. For MJSs, there exist many results on Kalman filtering, H∞ filtering, passive filtering, and dissipative filtering [11].…”

Section: Complexitymentioning

confidence: 99%

Reinforcement Learning-Based Routing Protocol to Minimize Channel Switching and Interference for Cognitive Radio Networks

Malik

Hasan

2020

Complexity

View full text Add to dashboard Cite

In the existing network-layered architectural stack of Cognitive Radio Ad Hoc Network (CRAHN), channel selection is performed at the Medium Access Control (MAC) layer. However, routing is done on the network layer. Due to this limitation, the Secondary/Unlicensed Users (SUs) need to access the channel information from the MAC layer whenever the channel switching event occurred during the data transmission. This issue delayed the channel selection process during the immediate routing decision for the channel switching event to continue the transmission. In this paper, a protocol is proposed to implement the channel selection decisions at the network layer during the routing process. The decision is based on past and expected future routing decisions of Primary Users (PUs). A learning agent operating in the cross-layer mode of the network-layered architectural stack is implemented in the spectrum mobility manager to pass the channel information to the network layer. This information is originated at the MAC layer. The channel selection is performed on the basis of reinforcement learning algorithms such as No-External Regret Learning, Q-Learning, and Learning Automata. This leads to minimizing the channel switching events and user interferences in the Reinforcement Learning- (RL-) based routing protocol. Simulations are conducted using Cognitive Radio Cognitive Network simulator based on Network Simulator (NS-2). The simulation results showed that the proposed routing protocol performed better than all the other comparative routing protocols in terms of number of channel switching events, average data rate, packet collision, packet loss, and end-to-end delay. The proposed routing protocol implies the improved Quality of Service (QoS) of the delay sensitive and real-time networks such as Cellular and Tele Vision (TV) networks.

show abstract

Observer-Based Asynchronous Fault Detection for Conic-Type Nonlinear Jumping Systems and its Application to Separately Excited DC Motor

Cited by 101 publications

References 35 publications

Control Strategies on Path Tracking for Autonomous Vehicle: State of the Art and Future Challenges

Control Strategies on Path Tracking for Autonomous Vehicle: State of the Art and Future Challenges

Finite-Time Non-Fragile Extended Dissipative Control of Periodic Piecewise Time-Varying Systems

Reinforcement Learning-Based Routing Protocol to Minimize Channel Switching and Interference for Cognitive Radio Networks

Contact Info

Product

Resources

About