Robust Adaptive Control of PEMFC Air Supply System Based on Radical Basis Function Neural Network

2019 ◽  
Vol 141 (6) ◽  
Author(s):  
Yun-Long Wang ◽  
Yong-Fu Wang ◽  
Hua-Kai Zhang
Keyword(s):  
Neural Network ◽  
Adaptive Learning ◽  
Basis Function ◽  
Tracking Error ◽  
Approximation Error ◽  
Supply System ◽  
Robust Adaptive Control ◽  
Lyapunov Theory ◽  
Air Supply ◽  
Robust Adaptive

This technical brief emphasizes on the control of polymer electrolyte membrane fuel cell (PEMFC) air supply system. The control objective is to improve the net power output through adjusting the oxygen excess ratio within a reasonable range. In view of the problem that the PEMFC air supply system is difficult to achieve accurate modeling and stable control, a robust adaptive controller is proposed by utilizing exact linearization and radical basis function (RBF) neural network (RBFNN) system. This controller does not need the complete structure and parameters of PEMFC system. The unmodeled dynamics of PEMFC system can be approximated by RBFNN in which the adaptive learning law can be derived based on Lyapunov theory, and the external disturbance as well as the approximation error of RBFNN can be attenuated through robust control. The stability analysis shows that the system tracking error is uniformly ultimately bounded. Finally, the effectiveness and feasibility of controller are validated by hardware-in-loop (HIL) experiment.

High-accuracy robust adaptive motion control of a torque-controlled motor servo system with friction compensation based on neural network

2018 ◽  
Vol 233 (7) ◽  
pp. 2318-2328 ◽  
Author(s):  
Jian Hu ◽  
Yuangang Wang ◽  
Lei Liu ◽  
Zhiwei Xie
Keyword(s):  
Neural Network ◽  
Motion Control ◽  
Servo System ◽  
High Accuracy ◽  
Robust Adaptive Control ◽  
Lyapunov Theory ◽  
Parametric Estimation ◽  
Friction Compensation ◽  
Nonlinear Friction ◽  
Robust Adaptive

In this paper, a high-accuracy motion control of a torque-controlled motor servo system with nonlinear friction compensation is presented. Friction always exists in the servo system and reduces its tracking accuracy. Thus, it is necessary to compensate for the friction effect. In this paper, a novel controller that combines robust adaptive control with friction compensation based on neural network observer is proposed. An improved LuGre friction model is applied into the friction compensation as it is known as a good model to express the nonlinear friction. A single hidden-layer network is utilized to observe the immeasurable friction state. Then, the robust adaptive controller is used to handle the parametric uncertainty, the parametric estimation error, friction compensation error, and other uncertainties. Lyapunov theory is utilized to analyze the stability of the closed-loop system. The experimental results demonstrate the effectiveness of the proposed algorithm.

scholarly journals A Robust Adaptive Control using Fuzzy Neural Network for Robot Manipulators with Dead-Zone

2019 ◽  
Vol 14 (5) ◽  
pp. 692
Author(s):  
D. Ha Vu ◽  
Shoudao Huang ◽  
T. Diep Tran ◽  
T. Yen Vu ◽  
V. Cuong Pham
Keyword(s):  
Neural Network ◽  
Fuzzy Neural Network ◽  
Robot Manipulators ◽  
Industrial Robot ◽  
Dead Zone ◽  
Approximation Error ◽  
Robust Adaptive Control ◽  
Neural Network Controller ◽  
Fuzzy Neural ◽  
Robust Adaptive

In this paper, a robust-adaptive-fuzzy-neural-network controller (RAFNNs) bases on dead zone compensator for industrial robot manipulators (RM) is proposed to dead the unknown model and external disturbance. Here, the unknown dynamics of the robot system is deal by using fuzzy neural network to approximate the unknown dynamics. The online training laws and estimation of the dead-zone are determined by Lyapunov stability theory and the approximation theory. In this proposal, the robust sliding-mode-control (SMC) is constructed to optimize parameter vectors, solve the approximation error and higher order terms. Therefore, the stability, robustness, and desired tracking performance of RAFNNs for RM are guaranteed. The simulations and experiments performed on three-link RM are provided in comparison with neural-network (NNs) and proportional-integral-derivative (PID) to demonstrate the robustness and effectiveness of the RAFNNs.

scholarly journals DESIGNING A ROBUST ADAPTIVE TRACKING BACKTEPPING CONTROLLER CONSIDERING ACTUATOR SATURATION FOR A WHEELED MOBILE ROBOT TO COMPENSATE UNKNOWN SLIPPAGE

2020 ◽  
Vol 36 (2) ◽  
pp. 187-204
Author(s):  
Chung Le ◽  
Kiem Nguyen Tien ◽  
Linh Nguyen ◽  
Tinh Nguyen ◽  
Tung Hoang
Keyword(s):  
Neural Network ◽  
Mobile Robot ◽  
Basis Function ◽  
Actuator Saturation ◽  
Wheeled Mobile Robot ◽  
Technical Solution ◽  
Control Approach ◽  
Adaptive Tracking ◽  
Simulation Results ◽  
Robust Adaptive

This article highlights a robust adaptive tracking backstepping control approach for a nonholonomic wheeled mobile robot (WMR) by which the bad problems of both unknown slippage and uncertainties are dealt with. The radial basis function neural network (RBFNN) in this proposed controller assists unknown smooth nonlinear dynamic functions to be approximated. Furthermore, a technical solution is also carried out to avoid actuator saturation. The validity and efficiency of this novel controller, finally, are illustrated via comparative simulation results.

Robust Adaptive Control of Nonlinear Systems with Uncertainties

2014 ◽  
Vol 568-570 ◽  
pp. 1108-1112
Author(s):  
Ning Liu ◽  
Yu Sheng Liu ◽  
Qiang Yang
Keyword(s):  
Nonlinear Systems ◽  
Tracking Error ◽  
Robust Adaptive Control ◽  
Design Parameters ◽  
Unmodeled Dynamics ◽  
Time Varying ◽  
Mean Square ◽  
Robust Controller ◽  
The Mean ◽  
Robust Adaptive

This paper proposes a robust adaptive robust controller for nonlinear systems represented by input-output models with unmodeled dynamics. Under the circumstances that the output of the system is bounded, the proposed controller can guarantee that all the variables of the system are bounded in the presence of unmodeled dynamics and time-varying disturbances. The scheme does not need to generate an additional dynamic signal to dominate the effects of the unmodeled dynamics. It is shown that the mean-square tracking error can be made arbitrarily small by choosing some design parameters appropriately.

Adaptive control of a class of non-linear systems preceded by backlash-like hysteresis

2014 ◽  
Vol 24 (5) ◽  
Author(s):  
JIANPING CAI ◽  
LUJUAN SHEN ◽  
FUZHEN WU
Keyword(s):  
Differential Equation ◽  
Adaptive Control ◽  
Linear Systems ◽  
Tracking Error ◽  
Robust Adaptive Control ◽  
Unknown Parameters ◽  
Control Scheme ◽  
Non Linear ◽  
Non Linear Systems ◽  
Robust Adaptive

We consider a class of uncertain non-linear systems preceded by unknown backlash-like hysteresis, which is modelled by a differential equation. We propose a new state feedback robust adaptive control scheme using a backstepping technique and properties of the differential equation. In this control scheme, we construct a new continuous function to design an estimator to estimate the unknown constant parameters and the unknown bound of a ‘disturbance-like’ term. The transient performance of the output tracking error can be guaranteed by the introduction of pre-estimates of the unknown parameters in our controller together with update laws. We do not require bounds on the ‘disturbance-like’ term or unknown system parameters in this scheme. The global stability of the closed-loop system can be proved.

RBF NEURAL NETWORK ROBUST ADAPTIVE CONTROL FOR WIND GENERATOR SYSTEM

Mechanika ◽  
2011 ◽  
Vol 17 (5) ◽  
Author(s):  
Y. Zuo ◽  
Y. N. Wang ◽  
Y. Zhang ◽  
Z. L. Shen ◽  
Z. S. Chen ◽  
...  
Keyword(s):  
Neural Network ◽  
Adaptive Control ◽  
Rbf Neural Network ◽  
Robust Adaptive Control ◽  
Generator System ◽  
Wind Generator ◽  
Robust Adaptive
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