Global output‐feedback stabilization for a class of stochastic nonlinear systems via sampled‐data control

Lan, Qixun; Li, Shihua

doi:10.1002/rnc.4021

Cited by 21 publications

(23 citation statements)

References 33 publications

(79 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Nonlinear Time-Varying Discrete Stochastic Systems. The stochastic control system has the characteristics of uncertainty [30], such as the autonomous control system of unmanned aerial vehicle (UAV) [31], which is affected by unstable wind [32], electromagnetic interference [33], noise signal [34], and so on in the process of operation, resulting in its control system with nonlinear, discrete, time-varying, stochastic, and other characteristics [35]. A stochastic system can be represented in the form of Equations (1) and (2) [36].…”

Section: Introductionmentioning

confidence: 99%

Control Optimization of Stochastic Systems Based on Adaptive Correction CKF Algorithm

Zhang

2020

International Journal of Aerospace Engineering

View full text Add to dashboard Cite

Standard cubature Kalman filter (CKF) algorithm has some disadvantages in stochastic system control, such as low control accuracy and poor robustness. This paper proposes a stochastic system control method based on adaptive correction CKF algorithm. Firstly, a nonlinear time-varying discrete stochastic system model with stochastic disturbances is constructed. The control model is established by using the CKF algorithm, the covariance matrix of standard CKF is optimized by square root filter, the adaptive correction of error covariance matrix is realized by adding memory factor to the filter, and the disturbance factors in nonlinear time-varying discrete stochastic systems are eliminated by multistep feedback predictive control strategy, so as to improve the robustness of the algorithm. Simulation results show that the state estimation accuracy of the proposed adaptive cubature Kalman filter algorithm is better than that of the standard cubature Kalman filter algorithm, and the proposed adaptive correction CKF algorithm has good control accuracy and robustness in the UAV control test.

show abstract

Section: Introductionmentioning

confidence: 99%

Control Optimization of Stochastic Systems Based on Adaptive Correction CKF Algorithm

Zhang

2020

International Journal of Aerospace Engineering

View full text Add to dashboard Cite

show abstract

“…As a matter of fact, systems are usually controlled using digital computers owing to the fast development of technology, which reduces the cost of implementation. 18,19 It is thus significant to study sampled-data control problem. In the work of Qian and Du, 20 global output feedback stabilization problem has been addressed for uncertain systems satisfying linear growth condition by using a linear sampled-data controller.…”

Section: Introductionmentioning

confidence: 99%

Global decentralized sampled‐data output feedback stabilization for a class of large‐scale nonlinear systems with sensor and actuator failures

Shi

Xiang

2019

Intl J Robust & Nonlinear

View full text Add to dashboard Cite

Summary In this paper, we investigate global decentralized sampled‐data output feedback stabilization problem for a class of large‐scale nonlinear systems with time‐varying sensor and actuator failures. The considered systems include unknown time‐varying control coefficients and inherently nonlinear terms. Firstly, coordinate transformations are introduced with suitable scaling gains. Next, a reduced‐order observer is designed to estimate unmeasured states. Then, a decentralized sampled‐data fault‐tolerant control scheme is developed with an allowable sampling period. By constructing an appropriate Lyapunov function, it can be shown that all states of the resulting closed‐loop system are globally uniformly ultimately bounded. Finally, the validity of the proposed control approach is verified by using two examples.

show abstract

“…23 Moreover, the last decade has witnessed an enormous interest in the study of networked and embedded control systems. [24][25][26][27][28][29][30][31] With the emergence of event-based, 32 self-triggered control techniques, 33 and some existing problems (sampling jitters, packet dropouts, and so on) in networked control systems, the study of aperiodic sampled-data systems constitutes nowadays a very popular research topic in control area. Many new techniques have been developed, eg, input delay method, 34,35 looped-functionals method, 36 discrete-time system approach, 37 integral quadratic constraint approach, 38 and impulsive-switched-system approach.…”

Section: Introductionmentioning

confidence: 99%

Mean square exponential stabilization of sampled‐data Markovian jump systems

Chen

Sun

Chen

2018

Intl J Robust & Nonlinear

View full text Add to dashboard Cite

Summary In this paper, the problem of mean square exponential stabilization for sampled‐data Markovin jump systems is studied. A time‐scheduled Lyapunov functional consisting of a exponential‐type looped function is constructed using segmentation technology and linear interpolation. Based on this new Lyapunov functional, a less conservative mean square exponential stability criterion is obtained such that a bigger maximum decay rate can be easily calculated. Meanwhile, the quantitative relationship among some system parameters, maximum sampling period, and decay rate is established. Moreover, a time‐dependent state feedback sample‐data controller is designed. Significant improvements of the proposed exponential‐type time‐scheduled Lyapunov functional method over some existing ones are verified by numerical examples.

show abstract

Global output‐feedback stabilization for a class of stochastic nonlinear systems via sampled‐data control

Cited by 21 publications

References 33 publications

Control Optimization of Stochastic Systems Based on Adaptive Correction CKF Algorithm

Control Optimization of Stochastic Systems Based on Adaptive Correction CKF Algorithm

Global decentralized sampled‐data output feedback stabilization for a class of large‐scale nonlinear systems with sensor and actuator failures

Mean square exponential stabilization of sampled‐data Markovian jump systems

Contact Info

Product

Resources

About