Modeling and Control of a Network of Cooperative Satellites Using Neural Networks

2013 ◽  
Author(s):  
Atefeh Einafshar ◽  
Farrokh Sassani
Keyword(s):  
Neural Network ◽  
Control System ◽  
Neural Control ◽  
Control Method ◽  
Neural Model ◽  
Satellite Networks ◽  
Space Vehicles ◽  
Operational Parameters ◽  
Modeling And Control ◽  
Control Scheme

An adaptable control system within and on-board a network of interacting satellites is an important module which is desired for unmanned space vehicles. However, such a control system is not easy to develop since it is the core of the network’s operation and all the earth-linked operational information is reviewed and analyzed through it. Due to multipurpose missions of satellites, several decisions are made simultaneously about necessary changes to the satellite’s operational parameters (i.e., orbit, inclination, etc.). In this paper, a neural network model-based control scheme is developed for a network of interacting satellites. The proposed neural control scheme refers to a methodology in which the controller is assumed as a neural network and the dynamical model of the system is identified through the training stages of the neural model. The simulation results show that the neural control method can be effectively applied in monitoring and controlling the satellite networks, without the necessity of determining the mathematical model of the system.

Independent wheel control system design for highly automated driving

2021 ◽  
pp. 095440702110065
Author(s):  
Shihuan Li ◽  
Lei Wang
Keyword(s):  
Control System ◽  
Control Method ◽  
Autonomous Driving ◽  
Control System Design ◽  
Steering Control ◽  
Automated Driving ◽  
Accuracy And Precision ◽  
Actual Observation ◽  
Control Scheme ◽  
Interference Measurement

For L4 and above autonomous driving levels, the automatic control system has been redundantly designed, and a new steering control method based on brake has been proposed; a new dual-track model has been established through multiple driving tests. The axle part of the model was improved, the accuracy of the transfer function of the model was verified again through acceleration-slide tests; a controller based on interference measurement was designed on the basis of the model, and the relationships between the controller parameters was discussed. Through the linearization of the controller, the robustness of uncertain automobile parameters is discussed; the control scheme is tested and verified through group driving test, and the results prove that the accuracy and precision of the controller meet the requirements, the robustness stability is good. Moreover, the predicted value of the model fits well with the actual observation value, the proposal of this method provides a new idea for avoiding car out of control.

scholarly journals The Adaptive Neural Control for a Class of High-Order Uncertain Stochastic Nonlinear Systems

2018 ◽  
Vol 2018 ◽  
pp. 1-10
Author(s):  
Xiaoyan Qin
Keyword(s):  
Nonlinear Systems ◽  
Neural Control ◽  
Closed Loop ◽  
Control Method ◽  
High Order ◽  
Stochastic Nonlinear Systems ◽  
Closed Loop System ◽  
Adaptive Neural Control ◽  
Control Scheme ◽  
Approximation Capability

This paper studies the problem of the adaptive neural control for a class of high-order uncertain stochastic nonlinear systems. By using some techniques such as the backstepping recursive technique, Young’s inequality, and approximation capability, a novel adaptive neural control scheme is constructed. The proposed control method can guarantee that the signals of the closed-loop system are bounded in probability, and only one parameter needs to be updated online. One example is given to show the effectiveness of the proposed control method.

scholarly journals Adaptive Neural Network Control of Zero-Speed Vessel Fin Stabilizer Based on Command Filter

2022 ◽  
Vol 12 (2) ◽  
pp. 754
Author(s):  
Ziteng Sun ◽  
Chao Chen ◽  
Guibing Zhu
Keyword(s):  
Neural Network ◽  
Control System ◽  
Control Method ◽  
Large Angle ◽  
Network Control ◽  
Neural Network Control ◽  
Adaptive Neural Network ◽  
Adaptive Neural Network Control ◽  
Command Filter ◽  
Fin Stabilizer

This paper proposes a zero-speed vessel fin stabilizer adaptive neural network control strategy based on a command filter for the problem of large-angle rolling motion caused by adverse sea conditions when a vessel is at low speed down to zero. In order to avoid the adverse effects of the high-frequency part of the marine environment on the vessel rolling control system, a command filter is introduced in the design of the controller and a command filter backstepping control method is designed. An auxiliary dynamic system (ADS) is constructed to correct the feedback error caused by input saturation. Considering that the system has unknown internal parameters and unmodeled dynamics, and is affected by unknown disturbances from the outside, the neural network technology and nonlinear disturbance observer are fused in the proposed design, which not only combines the advantages of the two but also overcomes the limitations of the single technique itself. Through Lyapunov theoretical analysis, the stability of the control system is proved. Finally, the simulation results also verify the effectiveness of the control method.

The Intelligent Scheduling Method of Elevator Group Control Based on Fuzzy Neural Network

2012 ◽  
Vol 155-156 ◽  
pp. 653-657
Author(s):  
Yu Lin Dong ◽  
Xiao Ming Wang
Keyword(s):  
Neural Network ◽  
Control System ◽  
Fuzzy Neural Network ◽  
Control Method ◽  
Scheduling Algorithm ◽  
Mathematic Model ◽  
Group Control ◽  
Fuzzy Neural ◽  
And Performance ◽  
Elevator Group Control

Elevator group control system (EGCS) is a complex optimization system, which has the characteristics of multi-objective, uncertain, stochastic random decision-making and nonlinear. It is hard to describe the elevator group control system in exact mathematic model and to increase the capability of the system with traditional control method. In this paper, we aim at the characters of elevator group control system and intelligent control, introduce the system's control fashion and performance evaluate guidelines and propose an elevator group control scheduling algorithm based on fuzzy neural network.

scholarly journals Adaptive PD Control Based on RBF Neural Network for a Wire-Driven Parallel Robot and Prototype Experiments

2019 ◽  
Vol 2019 ◽  
pp. 1-15 ◽  
Author(s):  
Yuqi Wang ◽  
Qi Lin ◽  
Xiaoguang Wang ◽  
Fangui Zhou
Keyword(s):  
Neural Network ◽  
Adaptive Control ◽  
Control Method ◽  
Rbf Neural Network ◽  
Approximation Error ◽  
Wind Tunnel Test ◽  
Parallel Robot ◽  
Network Control ◽  
Pd Control ◽  
Control Scheme

An adaptive PD control scheme is proposed for the support system of a wire-driven parallel robot (WDPR) used in a wind tunnel test. The control scheme combines a PD control and an adaptive control based on a radial basis function (RBF) neural network. The PD control is used to track the trajectory of the end effector of the WDPR. The experimental environment, the external disturbances, and other factors result in uncertainties of some parameters for the WDPR; therefore, the RBF neural network control method is used to approximate the parameters. An adaptive control algorithm is developed to reduce the approximation error and improve the robustness and control precision of the WDPR. It is demonstrated that the closed-loop system is stable based on the Lyapunov stability theory. The simulation results show that the proposed control scheme results in a good performance of the WDPR. The experimental results of the prototype experiments show that the WDPR operates on the desired trajectory; the proposed control method is correct and effective, and the experimental error is small and meets the requirements.

Hot Blast Stove Temperature Control System Based on Neural Network Predictive Control

2014 ◽  
Vol 709 ◽  
pp. 281-284 ◽  
Author(s):  
Yao Wu Tang ◽  
Xiang Liu
Keyword(s):  
Neural Network ◽  
Control System ◽  
Predictive Control ◽  
Control Method ◽  
Delay System ◽  
Temperature Control System ◽  
Control Effect ◽  
Rolling Optimization ◽  
Hot Blast ◽  
Neural Network Predictive Control

Chain type coal-fired hot blast furnace boiler has a strong coupling, large delay, large inertia characteristics. Control effect of control method of mathematic modeling method and the classical routine of it is very difficult to produce the ideal. The predictive control theory combined with neural network theory. Through the model correction and rolling optimization control method of the system is good to overcome the effects of model error and time-varying process. The experimental results showed that neural network predictive control system is improved effectively the static precision and dynamic characteristic. It has better practicability of boiler temperature of this kind of large time delay system.

Temperature Control of Batch Reaction Based on Genetic Fuzzy Neural Network

2011 ◽  
Vol 204-210 ◽  
pp. 1968-1971 ◽  
Author(s):  
Chun Tao Man ◽  
Jia Cui ◽  
Xin Xin Yang ◽  
Jun Kai Wang ◽  
Tian Feng Wang
Keyword(s):  
Neural Network ◽  
Temperature Control ◽  
Fuzzy Neural Network ◽  
Control Method ◽  
Design Method ◽  
Batch Reactor ◽  
Batch Processes ◽  
Strong Nonlinearity ◽  
Control Scheme ◽  
Fuzzy Neural

The batch reactor has strong nonlinearity and hysteresis, the conventional control method is hard to meet the control requirements. According to the batch processes temperature control, this thesis proposed an intelligent control scheme. Combined neural networks with fuzzy logic control, searching and optimized parameters of fuzzy neural network by using Genetic Algorithm (GA), displayed the design method and optimization steps, and the simulation results verify the control scheme which proposed is feasible and effective.

Modeling and Vibration Control of a Flexible Rotor by Using Magnetic Bearings

2011 ◽  
Author(s):  
Takuya Nomoto ◽  
Daisuke Hunakoshi ◽  
Toru Watanabe ◽  
Kazuto Seto
Keyword(s):  
Control System ◽  
Control Method ◽  
Design Procedure ◽  
Modeling Method ◽  
Magnetic Bearings ◽  
Active Magnetic Bearings ◽  
Flexible Rotor ◽  
Modeling And Control ◽  
Elastic Modes ◽  
And Control

This paper presents a new modeling method and a control system design procedure for a flexible rotor with many elastic modes using active magnetic bearings. The purpose of our research is to let the rotor rotate passing over the 1st and the 2nd critical speeds caused by flexible modes. To achieve this, it is necessary to control motion and vibration of the flexible rotor simultaneously. The new modeling method named as Extended Reduced Order Physical Model is presented to express its motion and vibration uniformly. By using transfer function of flexible rotor-Active Magnetic Bearings system, we designed a Local Jerk Feedback Control system and conducted stability discrimination with root locus. In order to evaluate this modeling and control method, levitation experimentation is conducted.

scholarly journals Stem Control of a Sliding-Stem Pneumatic Control Valve Using a Recurrent Neural Network

2013 ◽  
Vol 2013 ◽  
pp. 1-7 ◽  
Author(s):  
Mohammad Heidari ◽  
Hadi Homaei
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Neural Control ◽  
Closed Loop ◽  
Control Valve ◽  
Superior Performance ◽  
Pneumatic Control ◽  
Response Characteristics ◽  
Control Scheme ◽  
Simulation Results

This paper presents a neural scheme for controlling an actuator of pneumatic control valve system. Bondgraph method has been used to model the actuator of control valve, in order to compare the response characteristics of valve. The proposed controller is such that the system is always operating in a closed loop, which should lead to better performance characteristics. For comparison, minimum- and full-order observer controllers are also utilized to control the actuator of pneumatic control valve. Simulation results give superior performance of the proposed neural control scheme.

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