scholarly journals A novel adaptive control design method for stochastic nonlinear systems using neural network

2021 ◽  
Author(s):  
Mohammad Mahdi Aghajary ◽  
Arash Gharehbaghi
Keyword(s):  
Neural Network ◽  
Adaptive Control ◽  
Nonlinear Systems ◽  
Stochastic Systems ◽  
Control Method ◽  
Dead Zone ◽  
Stochastic Nonlinear Systems ◽  
Adaptation Period ◽  
Dynamic Surface ◽  
Actuator Nonlinearity

AbstractThis paper presents a novel method for designing an adaptive control system using radial basis function neural network. The method is capable of dealing with nonlinear stochastic systems in strict-feedback form with any unknown dynamics. The proposed neural network allows the method not only to approximate any unknown dynamic of stochastic nonlinear systems, but also to compensate actuator nonlinearity. By employing dynamic surface control method, a common problem that intrinsically exists in the back-stepping design, called “explosion of complexity”, is resolved. The proposed method is applied to the control systems comprising various types of the actuator nonlinearities such as Prandtl–Ishlinskii (PI) hysteresis, and dead-zone nonlinearity. The performance of the proposed method is compared to two different baseline methods: a direct form of backstepping method, and an adaptation of the proposed method, named APIC-DSC, in which the neural network is not contributed in compensating the actuator nonlinearity. It is observed that the proposed method improves the failure-free tracking performance in terms of the Integrated Mean Square Error (IMSE) by 25%/11% as compared to the backstepping/APIC-DSC method. This depression in IMSE is further improved by 76%/38% and 32%/49%, when it comes with the actuator nonlinearity of PI hysteresis and dead-zone, respectively. The proposed method also demands shorter adaptation period compared with the baseline methods.

Adaptive control based on Barrier Lyapunov function for a class of full-state constrained stochastic nonlinear systems with dead-zone and unmodeled dynamics

2021 ◽  
pp. 014233122098563
Author(s):  
Fei Shen ◽  
Xinjun Wang ◽  
Xinghui Yin
Keyword(s):  
Adaptive Control ◽  
Nonlinear Systems ◽  
Lyapunov Function ◽  
Dead Zone ◽  
Small Neighborhood ◽  
Unmodeled Dynamics ◽  
Stochastic Nonlinear Systems ◽  
Dynamic Surface ◽  
Barrier Lyapunov Function ◽  
Full State

This paper investigates the problem of adaptive control based on Barrier Lyapunov function for a class of full-state constrained stochastic nonlinear systems with dead-zone and unmodeled dynamics. To stabilize such a system, a dynamic signal is introduced to dominate unmodeled dynamics and an assistant signal is constructed to compensate for the effect of the dead zone. Dynamic surface control is used to solve the “complexity explosion” problem in traditional backstepping design. Two cases of symmetric and asymmetric Barrier Lyapunov functions are discussed respectively in this paper. The proposed Barrier Lyapunov function based on backstepping method can ensure that the output tracking error converges in the small neighborhood of the origin. This control scheme can ensure that semi-globally uniformly ultimately boundedness of all signals in the closed-loop system. Two simulation cases are proposed to verify the effectiveness of the theoretical method.

Event-triggered Neural Network-Based Adaptive Control for a Class of Uncertain Nonlinear Systems

2021 ◽  
pp. 2150275
Author(s):  
Hui Hu ◽  
Yang Li ◽  
Wei Yi ◽  
Yuebiao Wang ◽  
Fan Qu ◽  
...  
Keyword(s):  
Neural Network ◽  
Adaptive Control ◽  
Nonlinear Systems ◽  
Control Method ◽  
Uncertain Nonlinear Systems ◽  
Network Resource ◽  
Lyapunov Approach ◽  
Stability Method ◽  
The Stability ◽  
Event Triggered

In the paper, an event triggering adaptive control method based on neural network (NN) is proposed for a class of uncertain nonlinear systems with external disturbances. In order to reduce the network resource utilization, a novel event-triggered condition by the Lyapunov approach is proposed. In addition, the NN controller and adaptive parameters determined by the Lyapunov stability method are updated only at triggered instants to reduce the amount of calculation. Only one NN is used as the controller in the entire system. The stability analysis results of the closed-loop system are obtained by the Lyapunov approach, which shows that all the signals in the systems with bounded disturbance are semi-globally bounded. Zeno behavior is avoided. Finally, the analytical design is confirmed by the simulation results on a two-link robotic manipulator.

scholarly journals Actor Critic Neural Network-Based Adaptive Control for MEMS Gyroscopes Suffering from Multiresource Disturbances

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Chao Jing ◽  
Gangzhu Qiao
Keyword(s):  
Neural Network ◽  
Adaptive Control ◽  
Control Method ◽  
Control Policy ◽  
Dynamic Surface Control ◽  
Order System ◽  
Dynamic Surface ◽  
Control Approach ◽  
Mems Gyroscopes ◽  
Critic Neural Network

In this paper, an actor critic neural network-based adaptive control scheme for micro-electro-mechanical system (MEMS) gyroscopes suffering from multiresource disturbances is proposed. Faced with multiresource interferences consisting of parametric uncertainties, strong couplings between axes, Coriolis forces, and variable external disturbances, an actor critic neural network is introduced, where the actor neural network is employed to estimate the packaged disturbances and the critic neural network is utilized to supervise the system performance. Hence, strong robustness against uncertainties and better tracking properties can be derived for MEMS gyroscopes. Aiming at handling the nonlinearities inherent in gyroscopes without analytically differentiating the virtual control signals, dynamic surface control (DSC) rather than backstepping control method is employed to divide the 2nd order system into two 1st order systems and design the actual control policy. Moreover, theoretical analyses along with simulation experiments are conducted with a view to validate the effectiveness of the proposed control approach.

scholarly journals Adaptive Asymptotic Regulation for Uncertain Nonlinear Stochastic Systems with Time-Varying Delays

Symmetry ◽  
2021 ◽  
Vol 13 (12) ◽  
pp. 2284
Author(s):  
Xuemiao Chen ◽  
Ziwen Wu ◽  
Jing Li ◽  
Qianjin Zhao
Keyword(s):  
Stochastic Systems ◽  
Control Method ◽  
Dynamic Surface Control ◽  
Continuous Functions ◽  
Stochastic Nonlinear Systems ◽  
Approximation Technique ◽  
Time Varying ◽  
Dynamic Surface ◽  
Barbalat’S Lemma ◽  
Dsc Method

In this paper, for a class of uncertain stochastic nonlinear systems with input time-varying delays, an adaptive neural dynamic surface control (DSC) method is proposed. To approximate the unknown continuous functions online, the neural network approximation technique was applied, and based on the DSC scheme, the desired controller was constructed. A compensation system is presented to compensate for the effect of the input delay. The Lyapunov–Krasovskii functionals (LKFs) were employed to compensate for the effect of the state delay. Compared with the existing works, based on using the DSC scheme with the nonlinear filter and stochastic Barbalat’s lemma, the asymptotic regulation performance of this closed-loop system can be guaranteed under the developed controller. To certify the availability for the designed control method, some simulation results are presented.

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