Adaptive control of missile attitude based on BP–ADRC

2020 ◽  
Vol 92 (10) ◽  
pp. 1475-1481
Author(s):  
Haiyan Qiao ◽  
Hao Meng ◽  
Wei Ke ◽  
Quanxi Gao ◽  
Shaobo Wang
Keyword(s):  
Neural Network ◽  
Adaptive Control ◽  
Control System ◽  
Bp Neural Network ◽  
Closed Loop ◽  
Control Method ◽  
Extended State ◽  
Dynamics Model ◽  
Closed Loop System ◽  
Content Type

Purpose To improve the robustness of missile control system and reduce the error, a missile attitude adaptive control method based on active disturbance rejection control technology (ADRC) and BP neural network is innovatively proposed. Design/methodology/approach ADRC improves the performance of the missile control system by estimating and eliminating the total disturbance of the system. BP neural network adjusts the parameters of ADRC controller according to the state of the system to realize adaptive control. Based on the control system and missile dynamics model, the convergence analysis of the extended state observer and the stability analysis of the closed-loop system after embedding BP neural network are given. Findings The simulation results show that the adaptive control method can adjust the coefficient of error feedback rate according to the system input, output and error change rate, which accelerates the response speed of missile attitude angle and reduces the attitude angle error. Practical implications BP–ADRC further improves the robustness and environmental adaptability of the missile control system. The BP–ADRC control method proposed in this paper is proved feasible. Originality/value Different from the traditional ADRC, the BP–ADRC feedback signal proposed in this paper uses the output signal and its rate of the closed-loop system instead of the system state quantity estimated by extended state observer (ESO). This innovative method combined with BP neural network can make the system output meet the requirements when ESO has errors in the estimation of missile dynamics model.

Comparative Analysis of DC Motor Control System

2016 ◽  
Vol 817 ◽  
pp. 111-121 ◽  
Author(s):  
Wojciech Mitkowski ◽  
Marta Zagórowska ◽  
Waldemar Bauer
Keyword(s):  
Control System ◽  
Comparative Analysis ◽  
Pid Controller ◽  
Closed Loop ◽  
Control Method ◽  
Closed Loop System ◽  
Motor Shaft ◽  
Dc System ◽  
Dc Motor Control ◽  
Motor Control System

In this work we will present a control method for DC system – the so-called practical PID controller, where the inertia of both the derivative and the actuator is included. The original element in this paper consists of a comparative analysis of various controller stabilizing the position of motor shaft. In a system with ideal gain, K>0 ensures asymptotic stability of the closed-loop system. Taking into account this inertia along with the inertia of the derivative, we obtain limited values 0<Kp<Kgr. A similar restrictions apply to a system with delay.

Paste-filling weighing control system optimization based on neural network

2017 ◽  
Vol 14 (2) ◽  
pp. 155-158 ◽  
Author(s):  
Guimei Wang ◽  
Yong Shuo Zhang ◽  
Lijie Yang ◽  
Shuai Zhang
Keyword(s):  
Neural Network ◽  
Control System ◽  
Coal Mine ◽  
Bp Neural Network ◽  
Back Propagation ◽  
System Optimization ◽  
Filling Material ◽  
Content Type ◽  
Control Precision ◽  
Filling System

Purpose This paper aims to optimize the weighing control system and compensate weighing error for weighing control system of coal mine paste-filling weighing control system. Design/methodology/approach The process of the paste-filling weighing control system is analyzed and the mathematical model of the paste-filling material weight is established. Then, the back-propagation (BP) neural network is used to optimize the control system and compensate the weighing error. Findings Without the BP neural network, the weighing error of the paste-filling control system is more than 3 per cent, whereas after optimization with the BP neural network, the weighing error is less than 1 per cent. With the simulation results, it is seen that the weighing error of the paste-filling control system decreases and the accuracy of the weighing control system improves and optimizes. Originality/value The method can be further used to improve the control precision of the coal mine paste-filling system.

Research on Pressurizer Pressure Control System Based on BP Neural Network Control of Self-Adjusted PID Parameters

2013 ◽  
Vol 291-294 ◽  
pp. 2416-2423 ◽  
Author(s):  
Guo Duo Zhang ◽  
Xu Hong Yang ◽  
Dong Qing Lu ◽  
Yong Xiao Liu
Keyword(s):  
Neural Network ◽  
Control System ◽  
Bp Neural Network ◽  
Pid Controller ◽  
Nuclear Reactor ◽  
Control Method ◽  
Setting Time ◽  
Pressure Control ◽  
Network Control ◽  
Pressure System

The pressurizer is an important device in nuclear reactor system, and the traditional PID regulator is usually used to control pressure system of pressurizer in modern reactors. However, it is difficult to get precise parameters of traditional PID controller, and the PID control method is relied on the precise mathematical model badly. And the response of PID controller is often shown by the large amount of overshoot and long setting time which are not the desired results. For such a large inertia and complex time-varying control system, the tradition PID controller can not obtain the satisfy control results. A controller based on BP neural network in this paper has a simple structure, and the parameters of PID controller can be tuned on-line by the neural network self-learning characteristics. The computer simulation experiment demonstrates that the BP neural network PID controller performs very well when compared with the tradition PID regulator in minimal overshoot and more quick response.

Application of Model Predictive Control Based on BPNN to Heating Boiler

2011 ◽  
Vol 383-390 ◽  
pp. 2242-2248
Author(s):  
Yan Ping Wang
Keyword(s):  
Neural Network ◽  
Model Predictive Control ◽  
Predictive Control ◽  
Bp Neural Network ◽  
Control Algorithm ◽  
Closed Loop ◽  
Control Method ◽  
Feedback Information ◽  
Heating Boiler ◽  
Controlled Object

This paper presents the algorithm of model predictive control (MPC) based on BP neural network to the burden system of the heating boiler. Because the burden system of the heating boiler is complex, the proposed approach uses steady, effective way to control the boiler. There is a closed-loop, repeating online optimization, model-based control algorithm which deals with the feedback information and the quantity of the fuel entering the boiler by the way of multi-step future predicting and compensating based on BP neural network. By simulation, it is demonstrated that the burden system of the heating boiler using MPC as control method is better in performance than the traditional PID. Besides, it is compliant to the model of the controlled object, especially to those which parameters of the model are variable.

A Study on the Closed-Loop Error Feedback Method of the Rapid Prototyping and Manufacturing Integrated System

2011 ◽  
Vol 55-57 ◽  
pp. 2188-2191
Author(s):  
Hong Ling Yuan
Keyword(s):  
Neural Network ◽  
Rapid Prototyping ◽  
Bp Neural Network ◽  
Error Control ◽  
Closed Loop ◽  
Control Method ◽  
Integrated System ◽  
Error Feedback ◽  
Feedback Method ◽  
Rapid Prototyping And Manufacturing

In order to improve the precision of the Rapid Prototyping and Manufacturing Integrated System, the author considers the manufacturing error chain as a closed-loop. According to the feedback method of closed-loop error, the author applies the BP neural network into the error control of the Rapid Prototyping and Manufacturing Integrated System to improve the product precision. And the error control method is applied in production.

Synchronization of Chaotic Gyro Systems via Sliding Mode Control with Cooperative Weights Neural Network

2013 ◽  
Vol 427-429 ◽  
pp. 1101-1104
Author(s):  
Yan Qiu Che ◽  
Ting Ting Yang ◽  
Xiao Qin Li ◽  
Rui Xue Li
Keyword(s):  
Neural Network ◽  
Sliding Mode Control ◽  
Closed Loop ◽  
Control Method ◽  
Sliding Mode ◽  
Closed Loop System ◽  
External Disturbances ◽  
Mode Control ◽  
Nonlinear Uncertainties ◽  
Stability Method

In this paper, a sliding mode control (SMC) with a cooperative weights neural network (CWNN) is proposed to realize the synchronization of two chaotic Gyro systems with nonlinear uncertainties and external disturbances. By the Lyapunov stability method, the overall closed-loop system is shown to be stable and chaos synchronizationis obtained. The simulation results demonstrate the effectiveness of the proposed control method.

Symmetrical valve controlled asymmetrical cylinder based on wavelet neural network

2017 ◽  
Vol 34 (7) ◽  
pp. 2154-2167 ◽  
Author(s):  
Haitao Qi ◽  
Zilong Liu ◽  
Yan Lang
Keyword(s):  
Neural Network ◽  
Adaptive Control ◽  
Control System ◽  
Dynamic Response ◽  
Position Control ◽  
Reference Model ◽  
Wavelet Neural Network ◽  
Content Type ◽  
Hydraulic Servo ◽  
Model Reference Adaptive

Purpose The symmetrical valve is usually used in the hydraulic servo control system to control the asymmetrical cylinder, but this system’s structure involves asymmetry, and so its dynamic characteristics are asymmetrical, which causes issues in the control system of symmetric response. The purpose of this paper is to achieve the aim of symmetric control. Design/methodology/approach In this paper, the authors proposed a method that combined wavelet neural network (WNN) and model reference adaptive control. The reference model determined the dynamic response that the system was expected to achieve, and the WNN adaptive control made the system follow the reference model to achieve the purpose of symmetric control. Findings The experimental results show that the method can achieve a more accurate symmetric control and position control compared with the solutions via the classical PID control. Originality/value The proposed combination of the WNN and the reference model can effectively compensate for the asymmetry of dynamic response of the asymmetric cylinder in forward and return directions, which can be extended to deal with other classes of applications.

scholarly journals Fuzzy Backstepping Torque Control Of Passive Torque Simulator With Algebraic Parameters Adaptation

2015 ◽  
Vol 66 (4) ◽  
pp. 203-213
Author(s):  
Nasim Ullah ◽  
Shaoping Wang ◽  
Xingjian Wang
Keyword(s):  
Adaptive Control ◽  
Closed Loop ◽  
Control Method ◽  
Estimation Error ◽  
Loop System ◽  
Torque Control ◽  
Closed Loop System ◽  
Adaptation Algorithm ◽  
Saturation Function ◽  
Load Simulator

Abstract This work presents fuzzy backstepping control techniques applied to the load simulator for good tracking performance in presence of extra torque, and nonlinear friction effects. Assuming that the parameters of the system are uncertain and bounded, Algebraic parameters adaptation algorithm is used to adopt the unknown parameters. The effect of transient fuzzy estimation error on parameters adaptation algorithm is analyzed and the fuzzy estimation error is further compensated using saturation function based adaptive control law working in parallel with the actual system to improve the transient performance of closed loop system. The saturation function based adaptive control term is large in the transient time and settles to an optimal lower value in the steady state for which the closed loop system remains stable. The simulation results verify the validity of the proposed control method applied to the complex aerodynamics passive load simulator.

scholarly journals Design and Speed-Adaptive Control of a Powered Geared Five-Bar Prosthetic Knee Using BP Neural Network Gait Recognition

Sensors ◽  
2019 ◽  
Vol 19 (21) ◽  
pp. 4662 ◽  
Author(s):  
Yuanxi Sun ◽  
Rui Huang ◽  
Jia Zheng ◽  
Dianbiao Dong ◽  
Xiaohong Chen ◽  
...  
Keyword(s):  
Neural Network ◽  
Adaptive Control ◽  
Control System ◽  
Bp Neural Network ◽  
Network Control ◽  
Attitude Measurement ◽  
Neural Network Control ◽  
Variable Speed ◽  
Prosthetic Knee ◽  
The Neural Network

To improve the multi-speed adaptability of the powered prosthetic knee, this paper presented a speed-adaptive neural network control based on a powered geared five-bar (GFB) prosthetic knee. The GFB prosthetic knee is actuated via a cylindrical cam-based nonlinear series elastic actuator that can provide the desired actuation for level-ground walking, and its attitude measurement is realized by two inertial sensors and one load cell on the prosthetic knee. To improve the performance of the control system, the motor control and the attitude measurement of the GFB prosthetic knee are run in parallel. The BP neural network uses input data from only the GFB prosthetic knee, and is trained by natural and artificially modified various gait patterns of different able-bodied subjects. To realize the speed-adaptive control, the prosthetic knee speed and gait cycle percentage are identified by the Gaussian mixture model-based gait classifier. Specific knee motion control instructions are generated by matching the neural network predicted gait percentage with the ideal walking gait. Habitual and variable speed level-ground walking experiments are conducted via an able-bodied subject, and the experimental results show that the neural network control system can handle both self-selected walking and variable speed walking with high adaptability.

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