scholarly journals Finite Time Inverse Optimal Stabilization for Stochastic Nonlinear Systems

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Xiushan Cai ◽  
Yuhang Lin ◽  
Wei Zhang
Keyword(s):  
Nonlinear Systems ◽  
Finite Time ◽  
Closed Loop ◽  
Loop System ◽  
Stochastic Nonlinear Systems ◽  
Control Law ◽  
Closed Loop System ◽  
Control Lyapunov Function ◽  
Optimal Stabilization ◽  
Time Control

This paper deals with finite time inverse optimal stabilization for stochastic nonlinear systems. A concept of the stochastic finite time control Lyapunov function (SFT-CLF) is presented, and a control law for finite time stabilization for the closed-loop system is obtained. Furthermore, a sufficient condition is developed for finite time inverse optimal stabilization in probability, and a control law is designed to ensure that the equilibrium of the closed-loop system is finite time inverse optimal stable. Finally, an example is given to illustrate the applications of theorems established in this paper.

scholarly journals Global Adaptive Control of Stochastic Nonlinear Systems with Linearly Bounded Unmeasurable States by Output Feedback

2012 ◽  
Vol 2012 ◽  
pp. 1-15
Author(s):  
Qiangde Wang ◽  
Chunling Wei
Keyword(s):  
Nonlinear Systems ◽  
Output Feedback ◽  
Closed Loop ◽  
Inverse Dynamics ◽  
Growth Conditions ◽  
Loop System ◽  
Stochastic Nonlinear Systems ◽  
Nonlinear Functions ◽  
Closed Loop System ◽  
Stochastic Stabilization

The problem of the output feedback stochastic stabilization is investigated for a class of stochastic nonlinear systems with linearly bounded unmeasurable states. Under the condition that the inverse dynamics is stochastic input-to-state stable and the nonlinear functions satisfy the linear growth conditions with unknown growth rate, an adaptive output feedback controller is proposed to make the closed-loop system globally stable in probability and the states of the closed-loop system converge to zero almost surely. A simulation example is provided to show the effectiveness of the theoretical results.

Adaptive finite-time control for stochastic nonlinear systems using multi-dimensional Taylor network

2021 ◽  
pp. 014233122110396
Author(s):  
Shan-Liang Zhu ◽  
Ming-Xin Wang ◽  
Yu-Qun Han
Keyword(s):  
Nonlinear Systems ◽  
Finite Time ◽  
Tracking Error ◽  
Small Neighborhood ◽  
Lyapunov Stability Theory ◽  
Loop System ◽  
Stochastic Nonlinear Systems ◽  
Time Control ◽  
Control Scheme ◽  
The Stability

In this paper, the problem of adaptive finite-time multi-dimensional Taylor network (MTN) control for a class of stochastic nonlinear systems is investigated. By combining the MTN-based approximate method and adaptive backstepping technique, a novel adaptive finite-time MTN control scheme is proposed. In this scheme, the MTNs are used to approximate the unknown nonlinear functions of the systems. The finite-time Lyapunov stability theory is utilized to prove the stability of the close-loop system. The proposed scheme can ensure that all signals in the closed-loop system are bounded in probability and the tracking error converges to a small neighborhood of the origin in a finite time. Three simulation examples are presented to show the effectiveness of the control scheme. It should be pointed that the adaptive MTN controller proposed in this paper has the advantages of fast computational speed and good real-time performance thanks to the simple structure of the MTN.

scholarly journals The Adaptive Neural Control for a Class of High-Order Uncertain Stochastic Nonlinear Systems

2018 ◽  
Vol 2018 ◽  
pp. 1-10
Author(s):  
Xiaoyan Qin
Keyword(s):  
Nonlinear Systems ◽  
Neural Control ◽  
Closed Loop ◽  
Control Method ◽  
High Order ◽  
Stochastic Nonlinear Systems ◽  
Closed Loop System ◽  
Adaptive Neural Control ◽  
Control Scheme ◽  
Approximation Capability

This paper studies the problem of the adaptive neural control for a class of high-order uncertain stochastic nonlinear systems. By using some techniques such as the backstepping recursive technique, Young’s inequality, and approximation capability, a novel adaptive neural control scheme is constructed. The proposed control method can guarantee that the signals of the closed-loop system are bounded in probability, and only one parameter needs to be updated online. One example is given to show the effectiveness of the proposed control method.

scholarly journals Adaptive Stabilization Control for a Class of Complex Nonlinear Systems Based on T-S Fuzzy Bilinear Model

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Jinsheng Xing ◽  
Naizheng Shi
Keyword(s):  
Adaptive Control ◽  
Nonlinear Systems ◽  
Complex Systems ◽  
Closed Loop ◽  
Loop System ◽  
Asymptotically Stable ◽  
Bilinear Model ◽  
Closed Loop System ◽  
Fuzzy Modelling ◽  
Control Scheme

This paper proposes a stable adaptive fuzzy control scheme for a class of nonlinear systems with multiple inputs. The multiple inputs T-S fuzzy bilinear model is established to represent the unknown complex systems. A parallel distributed compensation (PDC) method is utilized to design the fuzzy controller without considering the error due to fuzzy modelling and the sufficient conditions of the closed-loop system stability with respect to decay rateαare derived by linear matrix inequalities (LMIs). Then the errors caused by fuzzy modelling are considered and the method of adaptive control is used to reduce the effect of the modelling errors, and dynamic performance of the closed-loop system is improved. By Lyapunov stability criterion, the resulting closed-loop system is proved to be asymptotically stable. The main contribution is to deal with the differences between the T-S fuzzy bilinear model and the real system; a global asymptotically stable adaptive control scheme is presented for real complex systems. Finally, illustrative examples are provided to demonstrate the effectiveness of the results proposed in this paper.

Neural network-based finite-time control of quantized stochastic nonlinear systems

2019 ◽  
Vol 362 ◽  
pp. 195-202 ◽  
Author(s):  
Fang Wang ◽  
LiLi Zhang ◽  
Shaowei Zhou ◽  
Yuanyuan Huang
Keyword(s):  
Neural Network ◽  
Nonlinear Systems ◽  
Finite Time ◽  
Stochastic Nonlinear Systems ◽  
Time Control

Extended Finite Time Inverse Optimal Control of Nonlinear Systems

2011 ◽  
Vol 403-408 ◽  
pp. 1499-1502
Author(s):  
Xin Jun Ren ◽  
Yan Jun Shen
Keyword(s):  
Optimal Control ◽  
Nonlinear Systems ◽  
Finite Time ◽  
Lyapunov Functions ◽  
Control Law ◽  
Inverse Optimal Control ◽  
Closed Loop System ◽  
Affine Nonlinear Systems ◽  
Definition Of ◽  
The Cost

In this paper, we use the definition of control Lyapunov functions to study finite time inverse optimal control for affine nonlinear systems. Based on control Lyapunov functions, a finite time universal control formula is presented, which can ensure the closed-loop system is finite time stable. From this, less conservative conditions for the finite time inverse optimal control are derived. We design a finite time inverse optimal control law, which minimizes the cost functional. A numerical example verifies the validity of the proposed method.

Semiglobal Stabilization for Nonholonomic Mobile Robots Based on Dynamic Feedback With Inputs Saturation

2012 ◽  
Vol 134 (4) ◽  
Author(s):  
Hua Chen ◽  
Chaoli Wang ◽  
Liu Yang ◽  
Dongkai Zhang
Keyword(s):  
Mobile Robots ◽  
Finite Time ◽  
Closed Loop ◽  
Kinematic Model ◽  
Loop System ◽  
Control Technique ◽  
Dynamic Feedback ◽  
Closed Loop System ◽  
Nonholonomic Mobile Robots ◽  
Semiglobal Stabilization

This paper investigates the semiglobal stabilization problem for nonholonomic mobile robots based on dynamic feedback with inputs saturation. A bounded, continuous, time-varying controller is presented such that the closed-loop system is semiglobally asymptotically stable. The systematic strategy combines finite-time control technique with the virtual-controller-tracked method, which is similar to the back-stepping procedure. First, the bound-constrained smooth controller is presented for the kinematic model. Second, the dynamic feedback controller is designed to make the generalized velocity converge to the prespecified kinematic (virtual) controller in a finite time. Furthermore, the rigorous proof is given for the stability analysis of the closed-loop system. In the mean time, the position and torque inputs of robots are proved to be bounded at any time. Finally, the simulation results show the effectiveness of the proposed control approach.

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