Research on adaptive control strategy of hydraulic mechanical continuously variable transmission of a cotton picker

2020 ◽  
Vol 234 (17) ◽  
pp. 3335-3345
Author(s):  
Xin Zhao ◽  
Xiangdong Ni ◽  
Qi Wang ◽  
Mingxi Bao ◽  
Sheng Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Adaptive Control ◽  
Control Strategy ◽  
Basis Function ◽  
Continuously Variable Transmission ◽  
Proportional Integral Derivative ◽  
Proportional Integral ◽  
Cotton Picker ◽  
Variable Transmission ◽  
Adaptive Control Strategy

Hydraulic mechanical continuously variable transmission has the advantages of good hydraulic stepless speed regulation performance and high efficiency of mechanical transmission, which can achieve the optimal matching of transmission system load and power source. In this article, an adaptive control strategy based on radial basis function neural network and proportional–integral–derivative control was proposed. The speed compound control method was used to solve the problems that the output speed of the hydraulic mechanical continuously variable transmission system was poor at the variable speed input and was difficult to control. The throttle opening and the engine speed were used as controller inputs. The pump–motor's displacement ratio and the output speed were used as controller outputs. Finally, the output speed of the cotton picker was stably controlled. Simulation and experimental results show that the transmission can quickly respond to the target speed and had little fluctuation based on different initial input speeds. The control strategy had good control precision and robustness. Compared with the traditional proportional–integral–derivative algorithm, the average steady-state error of the system output speed was controlled between ±0.0125%. The proposed algorithm based on radial basis function neural network proportional–integral–derivative adaptive control strategy had obvious control effect, and the stability of the speed output of the system was improved under the nonlinear input complex conditions. It provided research for the speed ratio adjustment and control of the hydraulic mechanical continuously variable transmission of the cotton picker.

scholarly journals Application of Nonlinear Adaptive Control in Temperature of Chinese Solar Greenhouses

Electronics ◽  
2021 ◽  
Vol 10 (13) ◽  
pp. 1582
Author(s):  
Yonggang Wang ◽  
Yujin Lu ◽  
Ruimin Xiao
Keyword(s):  
Neural Network ◽  
Adaptive Control ◽  
Radial Basis Function ◽  
Control Strategy ◽  
Basis Function ◽  
Adaptive Controller ◽  
Strong Nonlinearity ◽  
Nonlinear Adaptive Control ◽  
Radial Basis ◽  
Adaptive Control Strategy

The system of a greenhouse is required to ensure a suitable environment for crops growth. In China, the Chinese solar greenhouse plays a crucial role in maintaining a proper microclimate environment. However, the greenhouse system is described with complex dynamic characteristics, such as multi-disturbance, parameter uncertainty, and strong nonlinearity. It is difficult for the conventional control method to deal with the above problems. To address these problems, a dynamic model of Chinese solar greenhouses was developed based on energy conservation laws, and a nonlinear adaptive control strategy combined with a Radial Basis Function neural network was presented to deal with temperature control. In this approach, nonlinear adaptive controller parameters were determined through the generalized minimum variance laws, while unmodeled dynamics were estimated by a Radial Basis Function neural network. The control strategy consisted of a linear adaptive controller, a neural network nonlinear adaptive controller, and a switching mechanism. The research results show that the mean errors were 0.8460 and 0.2967, corresponding to a conventional PID method and the presented nonlinear adaptive scheme, respectively. The standard errors of the conventional PID method and the nonlinear adaptive control strategy were 1.8480 and 1.3342, respectively. The experimental results fully prove that the presented control scheme achieves better control performance, which meets the actual requirements.

Simulation and adaptive control of back propagation neural network proportional–integral–derivative for special launcher using new version of transfer matrix method for multibody systems

2019 ◽  
Vol 26 (9-10) ◽  
pp. 757-768 ◽  
Author(s):  
Yunfei Miao ◽  
Guoping Wang ◽  
Xiaoting Rui
Keyword(s):  
Neural Network ◽  
Adaptive Control ◽  
Control System ◽  
Transfer Matrix Method ◽  
Matrix Method ◽  
Multibody Systems ◽  
Proportional Integral Derivative ◽  
Backpropagation Neural Network ◽  
Proportional Integral ◽  
Proportional Integral Derivative Control

Rocket launcher system, as a special launcher placed on tactical vehicles, is a very complex mechanical system with characteristics of strong shock and vibration. In order to improve position accuracy, as well as reduce vibration, this paper creates a nonlinear dynamics model of the launcher system by using a new version of the transfer matrix method for multibody systems. The overall transfer equation of the nonlinear model is deduced. Combining with general kinematics equations of the rocket, the system launch dynamics are simulated and compared with experiments to verify the correctness of the model. On this basis, a backpropagation neural network proportional–integral–derivative adaptive control system is designed to improve servo control of the launcher. Then, the effectiveness of this method is verified by comparing with the traditional proportional–integral–derivative control method. Simulated results show that the backpropagation neural network proportional–integral–derivative control system makes the azimuth and elevation angles reach the target values smoothly and quickly, with higher accuracy. The results prove that the proposed method prominently reduces vibrations of the launcher, by adjusting the control parameters online according to the operation state of the system, presenting a better stability and robustness.

scholarly journals VSG damping adaptive adjustment based on BP neural network

2021 ◽  
Vol 2121 (1) ◽  
pp. 012036
Author(s):  
Mengzhao Zhang ◽  
Chunlin Guo
Keyword(s):  
Neural Network ◽  
Adaptive Control ◽  
Control Strategy ◽  
Bp Neural Network ◽  
Control Object ◽  
Synchronous Generator ◽  
Moment Of Inertia ◽  
Learning Ability ◽  
Adaptive Control Strategy ◽  
Virtual Synchronous Generator

Abstract The moment of inertia and damping of virtual synchronous generator (VSG) can be adjusted flexibly, which also has a significant impact on the transient performance of VSG. Constant damping or moment of inertia can not reduce frequency overshoot and fast response performance, so it is necessary to introduce adaptive damping control. Based on universal approximation theorem, BP neural network can fit continuous nonlinear function well. At the same time, it has the advantages of simple algorithm, powerful learning ability and fast learning speed. Based on the characteristics of the control object, the BP neural network is improved and a new adaptive control strategy is designed. The strategy uses improved BP neural network to adjust VSG virtual damping D online. Python-MATLAB-Simulink was used for co-simulation, BP neural network algorithm was integrated into the control object to establish an adaptive simulation model, and the proposed control strategy was simulated and verified. Simulation results show that the adaptive control strategy can eliminate overshoot and respond quickly when the frequency and active power of virtual synchronous generator change.

scholarly journals Multifunctional PID Neuro-Controller for Synchronous Generator

2016 ◽  
Vol 9 ◽  
pp. 1-4
Author(s):  
Hocine Tiliouine
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Adaptive Control ◽  
Control Strategy ◽  
Synchronous Generator ◽  
Generator System ◽  
Electromechanical Oscillations ◽  
Generator Voltage ◽  
Adaptive Control Strategy ◽  
Neuro Controller

This paper deals with a PID Neuro-Controller (PIDNC) for synchronous generator system. The controller is based on artificial neural network and adaptive control strategy. It ensures two functions: maintaining the generator voltage at its desired value and damping electromechanical oscillations. The performance of the proposed controller is evaluated on the basis of simulation tests. A comparative study of the results obtained with PIDNC and those with conventional PID was performed.

PIDNN Based Intelligent Control of Ignition Oven

2011 ◽  
Vol 396-398 ◽  
pp. 493-497
Author(s):  
Yu Qian Ying ◽  
Jian Gang Lu ◽  
Jin Shui Chen ◽  
You Xian Sun
Keyword(s):  
Neural Network ◽  
Intelligent Control ◽  
Control Strategy ◽  
Pid Control ◽  
Proportional Integral Derivative ◽  
Labor Costs ◽  
Neural Structure ◽  
Fuel Gas ◽  
Significant Saving ◽  
Proportional Integral

In a steel plant, fuel gas caloricity of ignition oven always changes rapidly and largely. Consequently, the temperature of ignition oven can’t keep steady. To overcome this problem we employ intelligent control of ignition oven based on PIDNN (Proportional-Integral-Derivative Neural Network). As we know, ignition oven is a nonlinear, large delay and slow time-varying process, so traditional PID control usually doesn’t work well. Artificial neural networks can perform adaptive control by learning, so we adopt Proportional-Integral-Derivative neural network to tackle the problem taking the advantages of both PID control and neural structure. In order to satisfy the restrictions of industrial instruments, we combine PIDNN control algorithm with expert system mechanism to fulfill the final intelligent control strategy. At a sintering plant in Hangzhou, we deploy the intelligent control strategy turning out a satisfactory result that the ignition oven temperature can be controlled steadily within a much smaller range with significant saving of labor costs and improving of energy efficiency.

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