scholarly journals Global Asymptotic Stabilization Control for a Class of Nonlinear Systems with Dynamic Uncertainties

2015 ◽  
Vol 2015 ◽  
pp. 1-12
Author(s):  
Jiangbo Yu ◽  
Jizhong Wang ◽  
Zhongcai Zhang
Keyword(s):  
Nonlinear Systems ◽  
Controller Design ◽  
Initial Conditions ◽  
Design Procedure ◽  
Control Law ◽  
Asymptotic Stabilization ◽  
Control Approach ◽  
Global Asymptotic Stabilization ◽  
Stabilization Control ◽  
Dynamic Uncertainties

This paper is concerned with the global asymptotic stabilization control problem for a class of nonlinear systems with input-to-state stable (ISS) dynamic uncertainties and uncertain time-varying control coefficients. Unlike the existing works, the ISS dynamic uncertainty is characterized by the uncertain supply rates. By using the backstepping control approach, a systematic controller design procedure is developed. The designed control law can guarantee that the system states are asymptotically regulated to the origin from any initial conditions and the other signals are bounded in closed-loop systems. Moreover, it is shown that, under some additional conditions, a linear control law can be designed by the proposed methodology. The simulation example demonstrates its effectiveness.

scholarly journals Adaptive Optimal Robust Control for Uncertain Nonlinear Systems Using Neural Network Approximation in Policy Iteration

2021 ◽  
Vol 11 (5) ◽  
pp. 2312
Author(s):  
Dengguo Xu ◽  
Qinglin Wang ◽  
Yuan Li
Keyword(s):  
Neural Network ◽  
Optimal Control ◽  
Robust Control ◽  
Nonlinear Systems ◽  
Policy Iteration ◽  
Control Law ◽  
Globally Asymptotically Stable ◽  
Control Approach ◽  
Input Uncertainties ◽  
Theoretical Results

In this study, based on the policy iteration (PI) in reinforcement learning (RL), an optimal adaptive control approach is established to solve robust control problems of nonlinear systems with internal and input uncertainties. First, the robust control is converted into solving an optimal control containing a nominal or auxiliary system with a predefined performance index. It is demonstrated that the optimal control law enables the considered system globally asymptotically stable for all admissible uncertainties. Second, based on the Bellman optimality principle, the online PI algorithms are proposed to calculate robust controllers for the matched and the mismatched uncertain systems. The approximate structure of the robust control law is obtained by approximating the optimal cost function with neural network in PI algorithms. Finally, in order to illustrate the availability of the proposed algorithm and theoretical results, some numerical examples are provided.

scholarly journals Robust Decentralized Adaptive Neural Control for a Class of Nonaffine Nonlinear Large-Scale Systems with Unknown Dead Zones

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Huanqing Wang ◽  
Qi Zhou ◽  
Xuebo Yang ◽  
Hamid Reza Karimi
Keyword(s):  
Neural Control ◽  
Large Scale ◽  
Controller Design ◽  
Dead Zone ◽  
Design Procedure ◽  
Rbf Neural Networks ◽  
Large Scale Systems ◽  
Dead Zones ◽  
Stabilization Control ◽  
Control Scheme

The problem of robust decentralized adaptive neural stabilization control is investigated for a class of nonaffine nonlinear interconnected large-scale systems with unknown dead zones. In the controller design procedure, radical basis function (RBF) neural networks are applied to approximate packaged unknown nonlinearities and then an adaptive neural decentralized controller is systematically derived without requiring any information on the boundedness of dead zone parameters (slopes and break points). It is proven that the developed control scheme can ensure that all the signals in the closed-loop system are semiglobally uniformly ultimately bounded in the sense of mean square. Simulation study is provided to further demonstrate the effectiveness of the developed control scheme.

scholarly journals State-Feedback Sampled-Data Control Design for Nonlinear Systems via Passive Theory

2013 ◽  
Vol 2013 ◽  
pp. 1-12 ◽  
Author(s):  
Chengming Yang ◽  
Qi Zhou ◽  
H. R. Karimi ◽  
Huanqing Wang
Keyword(s):  
Nonlinear Systems ◽  
Controller Design ◽  
Linear Matrix ◽  
Optimization Approach ◽  
Sampled Data ◽  
Control Approach ◽  
Synthesis Conditions ◽  
Data Control ◽  
Sampled Data Control ◽  
Data Controller

This paper investigates the problem of passive controller design for a class of nonlinear systems under variable sampling. The Takagi-Sugeno (T-S) fuzzy modeling method is utilized to represent the nonlinear systems. Attention is focused on the design of passive controller for the T-S fuzzy systems via sampled-data control approach. Under the concept of very-strict passivity, a novel time-dependent Lyapunov functional is constructed to develop passive analysis criteria and passive controller synthesis conditions. A new sampled-data controller is designed to guarantee that the resulting closed-loop system is very-strictly passive. These conditions are formulated in the form of linear matrix inequalities (LMIs), which can be solved by convex optimization approach. Finally, an application example is given to demonstrate the feasibility and effectiveness of the proposed results.

scholarly journals Adaptive Global Sliding Mode Controller Design for Perturbed DC-DC Buck Converters

Energies ◽  
2021 ◽  
Vol 14 (5) ◽  
pp. 1249
Author(s):  
Saleh Mobayen ◽  
Farhad Bayat ◽  
Chun-Chi Lai ◽  
Asghar Taheri ◽  
Afef Fekih
Keyword(s):  
Controller Design ◽  
Sliding Mode ◽  
Lyapunov Stability Theory ◽  
Buck Converter ◽  
Control Law ◽  
Control Input ◽  
Buck Converters ◽  
Tracking Accuracy ◽  
Control Approach ◽  
Efficient Suppression

This paper proposes a novel adaptive intelligent global sliding mode control for the tracking control of a DC-DC buck converter with time-varying uncertainties/disturbances. The proposed control law is formulated using a switching surface that eliminates the reaching phase and ensures the existence of the sliding action from the start. The control law is derived based on the Lyapunov stability theory. The effectiveness of the proposed approach is illustrated via high-fidelity simulations by means of Simscape simulation environment in MATLAB. Satisfactory tracking accuracy, efficient suppression of the chattering phenomenon in the control input, and high robustness against uncertainties/disturbances are among the attributes of the proposed control approach.

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