PIDNN control for Vernier-gimballing magnetically suspended flywheel under nonlinear change of stiffness and disturbance

2020 ◽  
pp. 095965182097757
Author(s):  
Jiqiang Tang ◽  
Mengyue Ning ◽  
Xu Cui ◽  
Tongkun Wei ◽  
Xiaofeng Zhao
Keyword(s):  
Neural Network ◽  
Attitude Control ◽  
Magnetic Force ◽  
Magnetic Bearing ◽  
Network Control ◽  
Gradient Descent Method ◽  
Neural Network Control ◽  
Proportional Integral Derivative ◽  
Tracking Accuracy ◽  
Proportional Integral

Vernier-gimballing magnetically suspended flywheel is often used for attitude control and interference suppression of spacecrafts. Due to the special structure of the conical magnetic bearing, the radial component generated by the axial magnetic force and the change of the magnetic air gap will cause the nonlinearity of stiffness and disturbance. That will lead to not only poor stability of the suspension control system but also unsatisfactory tracking accuracy of the rotor position. To solve the nonlinear problem of the system, this article proposes a proportional–integral–derivative neural network control scheme. First, the rotor model considering the nonlinear variation of disturbance and stiffness parameters is established. Then, the weight of neural network is adjusted by the gradient descent method online to ensure the accurate output of magnetic force. Finally, the convergence analysis is carried out based on the Lyapunov stability theory. Compared with the general proportional–integral–derivative control and the radial basis function neural network control, the simulation results demonstrate that the proposed method has the highest tracking accuracy and excellent performance in improving stability. The experimental results prove the correctness of the theoretical analysis and the validity of the proposed method.

scholarly journals High Precision Adaptive Robust Neural Network Control of a Servo Pneumatic System

2019 ◽  
Vol 9 (17) ◽  
pp. 3472 ◽  
Author(s):  
Chen ◽  
Tao ◽  
Liu
Keyword(s):  
Neural Network ◽  
Adaptive Control ◽  
Robust Control ◽  
Network Control ◽  
Parametric Estimation ◽  
Pneumatic Cylinder ◽  
Unmodeled Dynamics ◽  
Neural Network Control ◽  
Tracking Accuracy ◽  
Neural Network Controller

In this paper, an adaptive robust neural network controller (ARNNC) is synthesized for a single-rod pneumatic actuator to achieve high tracking accuracy without knowing the bounds of the parameters and disturbances. The ARNNC control framework integrates adaptive control, robust control, and neural network control intelligently. Adaptive control improves the precision of dynamic compensation with parametric estimation, and robust control attenuates the effect of unmodeled dynamics and unknown disturbances. In reality, the unmodeled dynamics of the complicated pneumatic systems and unpredictable disturbances in working conditions affect the tracking precision. However, these cannot be expressed as an exact formula. Therefore, the real-time learning radial basis function (RBF) neural network component is considered for better compensation of unmodeled dynamics, random disturbances, and estimation errors of the adaptive control. Although the bounds of the parameters and disturbances for the pneumatic systems are unknown, the prescribed transient performance and final tracking accuracy of the proposed method can be still achieved with fictitious bounds. Asymptotic tracking performance can be acquired under the provided circumstance. The comparative experiments with a pneumatic cylinder driven by proportional direction valve illustrate the effectiveness of the proposed ARNNC as shown by a high tracking accuracy is achieved.

scholarly journals Neural Network Control for the Probe Landing Based on Proportional Integral Observer

2015 ◽  
Vol 2015 ◽  
pp. 1-10
Author(s):  
Yuanchun Li ◽  
Tianhao Ma ◽  
Bo Zhao
Keyword(s):  
Neural Network ◽  
Control Algorithm ◽  
Control Method ◽  
Landing Site ◽  
Network Control ◽  
Control Law ◽  
Neural Network Control ◽  
Control Approach ◽  
Proportional Integral ◽  
The Neural Network

For the probe descending and landing safely, a neural network control method based on proportional integral observer (PIO) is proposed. First, the dynamics equation of the probe under the landing site coordinate system is deduced and the nominal trajectory meeting the constraints in advance on three axes is preplanned. Then the PIO designed by using LMI technique is employed in the control law to compensate the effect of the disturbance. At last, the neural network control algorithm is used to guarantee the double zero control of the probe and ensure the probe can land safely. An illustrative design example is employed to demonstrate the effectiveness of the proposed control approach.

Research on PID Neural Network Control System of Temperature for Agricutural Film Unit

2013 ◽  
Vol 341-342 ◽  
pp. 694-699
Author(s):  
Yue Feng ◽  
Mei Xia Qiao ◽  
Shuai Zheng
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Pid Control ◽  
Control Algorithm ◽  
Melting Process ◽  
Network Control ◽  
Heating Process ◽  
Neural Network Control ◽  
Proportional Integral ◽  
Pid Neural Network

The temperature of agricultural film unit affects the plastic film directly. Since unit heating process has the characters of time delay, nonlinear, time-varying and strong coupling. It is difficult to create a mathematical model structure of plastic melting process. Thus, temperature control is very difficult. This paper presents decoupling control strategy and corresponding control algorithm based on PID (proportional-Integral-differential) neural network. Proportional, integral, differential neurons form a three-layer neural network. This design gives full play to respective advantages of PID control and neural network, and takes advantage of BP neural network to establish the dynamic model of system.

Neural network control of magnetic bearing

2003 ◽  
Author(s):  
Junru Wang ◽  
Benyong Chen ◽  
Zhengrong Sun ◽  
Qingxiang He
Keyword(s):  
Neural Network ◽  
Magnetic Bearing ◽  
Network Control ◽  
Neural Network Control

scholarly journals Robust Quadrotor Control through Reinforcement Learning with Disturbance Compensation

2021 ◽  
Vol 11 (7) ◽  
pp. 3257
Author(s):  
Chen-Huan Pi ◽  
Wei-Yuan Ye ◽  
Stone Cheng
Keyword(s):  
Neural Network ◽  
Reinforcement Learning ◽  
Control Strategy ◽  
External Disturbance ◽  
Control Agent ◽  
Network Control ◽  
Outdoor Environment ◽  
Disturbance Compensation ◽  
Tracking Accuracy ◽  
Control Scheme

In this paper, a novel control strategy is presented for reinforcement learning with disturbance compensation to solve the problem of quadrotor positioning under external disturbance. The proposed control scheme applies a trained neural-network-based reinforcement learning agent to control the quadrotor, and its output is directly mapped to four actuators in an end-to-end manner. The proposed control scheme constructs a disturbance observer to estimate the external forces exerted on the three axes of the quadrotor, such as wind gusts in an outdoor environment. By introducing an interference compensator into the neural network control agent, the tracking accuracy and robustness were significantly increased in indoor and outdoor experiments. The experimental results indicate that the proposed control strategy is highly robust to external disturbances. In the experiments, compensation improved control accuracy and reduced positioning error by 75%. To the best of our knowledge, this study is the first to achieve quadrotor positioning control through low-level reinforcement learning by using a global positioning system in an outdoor environment.

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