Robust Adaptive Control of Uncertain Stochastic Hamiltonian Systems with Time Varying Delay

2015 ◽  
Vol 18 (2) ◽  
pp. 642-651 ◽  
Author(s):  
Weiwei Sun ◽  
Lianghong Peng
2019 ◽  
Vol 17 (9) ◽  
pp. 2193-2202 ◽  
Author(s):  
Saim Ahmed ◽  
Haoping Wang ◽  
Muhammad Shamrooz Aslam ◽  
Imran Ghous ◽  
Irfan Qaisar

2013 ◽  
Vol 2013 ◽  
pp. 1-10 ◽  
Author(s):  
Weiwei Sun ◽  
Guochen Pang ◽  
Pan Wang ◽  
Lianghong Peng

This paper deals with the robust stabilizability andL2disturbance attenuation for a class of time-delay Hamiltonian control systems with uncertainties and external disturbances. Firstly, the robust stability of the given systems is studied, and delay-dependent criteria are established based on the dissipative structural properties of the Hamiltonian systems and the Lyapunov-Krasovskii (L-K) functional approach. Secondly, the problem ofL2disturbance attenuation is considered for the Hamiltonian systems subject to external disturbances. An adaptive control law is designed corresponding to the time-varying delay pattern involved in the systems. It is shown that the closed-loop systems under the feedback control law can guarantee theγ-dissipative inequalities be satisfied. Finally, two numerical examples are provided to illustrate the theoretical developments.


2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Ruliang Wang ◽  
Jie Li ◽  
Shanshan Zhang ◽  
Dongmei Gao ◽  
Huanlong Sun

We present adaptive neural control design for a class of perturbed nonlinear MIMO time-varying delay systems in a block-triangular form. Based on a neural controller, it is obtained by constructing a quadratic-type Lyapunov-Krasovskii functional, which efficiently avoids the controller singularity. The proposed control guarantees that all closed-loop signals remain bounded, while the output tracking error dynamics converge to a neighborhood of the desired trajectories. The simulation results demonstrate the effectiveness of the proposed control scheme.


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