scholarly journals Multiobjective Trajectory Optimization and Adaptive Backstepping Control for Rubber Unstacking Robot Based on RFWNN Method

2018 ◽  
Vol 2018 ◽  
pp. 1-19
Author(s):  
Le Liang ◽  
Yanjie Liu ◽  
Hao Xu

Multiobjective trajectory optimization and adaptive backstepping control method based on recursive fuzzy wavelet neural network (RFWNN) are proposed to solve the problem of dynamic modeling uncertainties and strong external disturbance of the rubber unstacking robot during recycling process. First, according to the rubber viscoelastic properties, the Hunt-Crossley nonlinear model is used to construct the robot dynamics model. Then, combined with the dynamic model and the recycling process characteristics, the multiobjective trajectory optimization of the rubber unstacking robot is carried out for the operational efficiency, the running trajectory smoothness, and the energy consumption. Based on the trajectory optimization results, the adaptive backstepping control method based on RFWNN is adopted. The RFWNN method is applied in the main controller to cope with time-varying uncertainties of the robot dynamic system. Simultaneously, an adaptive robust control law is developed to eliminate inevitable approximation errors and unknown disturbances and relax the requirement for prior knowledge of the controlled system. Finally, the validity of the proposed control strategy is verified by experiment.

Energies ◽  
2018 ◽  
Vol 11 (12) ◽  
pp. 3528 ◽  
Author(s):  
Chujia Guo ◽  
Aimin Zhang ◽  
Hang Zhang ◽  
Lei Zhang

This study aims to address the inherent uncertainty in plug loads and load extraction, distributed generation, and the inevitable circulating current in a parallel structure. Therefore, in this paper, an adaptive backstepping control scheme with an online parameter estimator (OPE) for a plug-and-play parallel converter system in a four-port power switcher is proposed. The adaptive backstepping control method was designed in the dq0 coordinate system to suppress the circulating current in the zero-component; the circulating current can be suppressed by using an embedded algorithm and omitting the extra controller. An adaptive update law was designed to weaken the influence of the arbitrary plug and extraction operations in the DC and AC buses to realize the plug-and-play function. The transient tracking performance is governed by the limitation of maximum total errors in the voltage and current. As a result, the settling times of the voltage, current, and power decreased. Additionally, to further improve the system robustness, an online inductance and resistance estimator was established using an optimal algorithm that solves the weighted least squares problem. In the estimator, there are no additional voltage and current sensors needed, and the mean squared error (MSE) of the estimation can be minimized. Simulation studies on a two-converter parallel system with a plug-and-play function were conducted using MATLAB/SIMULINK (R2018b, MathWorks, Natick, MA, USA) to verify the effectiveness of the proposed adaptive backstepping control strategy. The results show that this strategy improves system performance over that of a system with unbalanced parameters among a parallel structure with AC and DC system disturbances caused by arbitrary plug and extraction operations.


Processes ◽  
2021 ◽  
Vol 9 (12) ◽  
pp. 2209
Author(s):  
Gexin Chen ◽  
Huilong Liu ◽  
Pengshuo Jia ◽  
Gengting Qiu ◽  
Haohui Yu ◽  
...  

A nonlinear adaptive backstepping control method was proposed to address the system parameter uncertainty problem in the position control process of an electro-hydraulic servo closed-pump control system. This control strategy fully considers the parameter uncertainty of the nonlinear system and establishes the adaptive rate of the uncertain parameter to adjust the parameter disturbance online in real time, thereby improving the accuracy and robustness of the control system. A pump control system experiment platform was used to verify the feasibility of the controller. The experimental results showed that the proposed control strategy provided a good control effect. The pump control system can be controlled with high precision, with a steady-state control accuracy of ±0.02 mm, which serves as a good foundation for the engineering application and promotion of the pump control system.


2011 ◽  
Vol 403-408 ◽  
pp. 5082-5087
Author(s):  
Ping Jun Zhang ◽  
Xin Hua Jiang

For the nonliear and uncertainty parameters of the running driving component of Resonant cement-road breaking vehicle (RCRBV), the mathematic model of the speed Control is established, a adaptive backstepping control method based upon the dynamic recurrent fuzzy neural networks (DRFNN) is presented. The adaptive backstepping controlling arithmetic is designed firstly in transportational status without regard to the uncertain parameters. The convergence based on Lyapunov theory for the closed loop system is also analysised. secondly, the uncertain parameters of the Electro-hydraulic propotional system which affect the running speed controlling performances are defined as items to be estimated by DRFNN in breaking status to meet the high precision and stability requires, the parameter adjustment law is given based upon DRFNN. Finally, the results of the simulation show that the scheme is robust with respect to plant parameter variations and load disturbances.


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