EXPERIENCE OF THE DEVELOPMENT OF THE CONTROL SYSTEM OF MECHANISMS WITH PARALLEL STRUCTURE OF THE TYPE "HEXAPOD" FOR POSITIONING AND INTRODUCTION OF LARGE-SIZED OBJECTS OF INFORMATION SPACE PLATFORM

2018 ◽  
pp. 111-123 ◽  
Author(s):  
A. V. Gorbunov ◽  
E. B. Korotkov ◽  
A. V. Lekanov ◽  
S. A. Matveev ◽  
N. S. Slobodzyan ◽  
...  
Keyword(s):  
Neural Network ◽  
Control System ◽  
Control Unit ◽  
Network Control ◽  
Parallel Structure ◽  
Nonlinear Controller ◽  
System A ◽  
Control Scheme ◽  
Adaptive Neural Network Control

The problems of designing a hexapod control system - a mechanism with parallel kinematics, designed for guidance and positioning of instruments and antennas of orbiting satellite platforms are considered. Based on the solution of the extended kinematics problem, the algorithm for controlling linear drives with a kinematic pair of screw-nut and two two-axis hinges is specified. The hexapod control scheme with the spatial load position sensor is given, the feasibility of positional control algorithms is estimated on the basis of the modern domestic element base. The estimation is made by the method of mathematical modeling. An algorithm for adaptive neural network control of a hexapod is proposed. An artificial neural network has been developed, which together with a nonlinear controller regulates the force acting on linear actuators by control error. To assess the quality of hexapod control, a dynamic model of the hexapod control system was created in the simulation package SimMechanics of the MATLAB Simulink system. A description is given of the hardware part of the digital control system-the hexapod control unit).

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.

Novel Adaptive Recurrent Legendre Neural Network Control for PMSM Servo-Drive Electric Scooter

2014 ◽  
Vol 137 (1) ◽  
Author(s):  
Chih-Hong Lin
Keyword(s):  
Neural Network ◽  
Control System ◽  
Permanent Magnet Synchronous Motor ◽  
Parameter Tuning ◽  
Network Control ◽  
The Novel ◽  
Servo Drive ◽  
Control Scheme ◽  
Electric Scooter ◽  
Adaptation Law

Because an electric scooter driven by permanent magnet synchronous motor (PMSM) servo system has the unknown nonlinearity and the time-varying characteristics, its accurate dynamic model is difficult to establish for the design of the linear controller in whole system. In order to conquer this difficulty and raise robustness, a novel adaptive recurrent Legendre neural network (NN) control system, which has fast convergence and provide high accuracy, is proposed to control for PMSM servo-drive electric scooter under external torque disturbance in this study. The novel adaptive recurrent Legendre NN control system consists of a recurrent Legendre NN control with adaptation law and a remunerated control with estimation law. In addition, the online parameter tuning methodology of the recurrent Legendre NN control and the estimation law of the remunerated control can be derived by using the Lyapunov stability theorem. Finally, comparative studies are demonstrated by experimental results in order to show the effectiveness of the proposed control scheme.

scholarly journals Application of High-Speed Wind Tunnel Control Based on PID Neural Network

2011 ◽  
Vol 2-3 ◽  
pp. 3-6
Author(s):  
Ji Li ◽  
Hong Wang ◽  
Hai Long Huang
Keyword(s):  
Neural Network ◽  
Control System ◽  
Wind Tunnel ◽  
Pid Control ◽  
High Speed ◽  
Network Control ◽  
Parallel Structure ◽  
System Response ◽  
Back Propagation Algorithm ◽  
Pid Neural Network

The traditional PID control in nonlinear system such as high-speed wind tunnel has limitations, and the range of using is limited. The BP neural network has been widely applied to the optimization of the PID controller parameter adjustment. The PID neural network control system is introduced in the conventional PID control, which has advantages such as simple structure, physical meaning clear parameters, but also has a neural network of parallel structure and the function of learning and memory and nonlinear mapping capability. The controller uses BP (error back propagation) algorithm to correct connection weights, through on-line training and learning and make objective function to achieve optimal value. This improvement scheme can not only improve algorithm in the training process, and the convergence speed in the wind tunnel, the control valve control system response speed, high precision, meet the steady-state real-time control requirements.

scholarly journals Accuracy Improvement of Robot Arm Trajectory based on Adaptive Neural Network Algorithm

CONVERTER ◽  
2021 ◽  
pp. 709-715
Author(s):  
Peibo Li, Peixing Li, Chen Yanpeng
Keyword(s):  
Neural Network ◽  
Control System ◽  
Control Method ◽  
Tracking Error ◽  
Network Control ◽  
Neural Network Control ◽  
Robot Arm ◽  
Adaptive Neural Network ◽  
Adaptive Neural Network Control ◽  
Simulation Results

An adaptive neural network control method was proposed to solve the problems such as unstable motion and large trajectory tracking error when the robot arm was disturbed by the external environment.The dynamic equations of the manipulator were given and the dynamic characteristics of the manipulator were studied by using the positive feedback neural network. Then the adaptive neural network control system was designed, and the stability and convergence of the closed-loop system were proved by the Lyapunov function. Later, the model diagram of the robot arm was established, and the dynamics parameters of the manipulator were simulated by MATLAB /Simulink software.At the same time, they were compared with the simulation results of the PID control system for analysis.The simulation results showed that the trajectory tracking error and input torque fluctuation were smaller when the trajectory of the robot arm was disturbed by the external world. When adopting the control method of the adaptive neural network, the robot arm could improve the control precision of the trajectory, thus reducing the jitter of the robot arm motion.

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