scholarly journals Neural Network Control for Trajectory Tracking and Balancing of a Ball-Balancing Robot with Uncertainty

2021 ◽  
Vol 11 (11) ◽  
pp. 4739
Author(s):  
Hyo-Geon Jang ◽  
Chang-Ho Hyun ◽  
Bong-Seok Park
Keyword(s):  
Neural Network ◽  
Control System ◽  
Trajectory Tracking ◽  
Control Method ◽  
Tracking Error ◽  
System Structure ◽  
Underactuated System ◽  
Dynamic Surface ◽  
Comparison Results ◽  
Balancing Robot

In this paper, a neural-network-based control method to achieve trajectory tracking and balancing of a ball-balancing robot with uncertainty is presented. Because the ball-balancing robot is an underactuated system and has nonlinear couplings in the dynamic model, it is challenging to design a controller for trajectory tracking and balancing. Thus, various approaches have been proposed to solve these problems. However, there are still problems such as the complex control system and instability. Therefore, the objective of this paper was to propose a solution to these problems. To this end, we developed a virtual angle-based control scheme. Because the virtual angle was used as the reference angle to achieve trajectory tracking while keeping the balance of the ball-balancing robot, we could solve the underactuation problem using a single-loop controller. The radial basis function networks (RBFNs) were employed to compensate uncertainties, and the controller was designed using the dynamic surface control (DSC) method. From the Lyapunov stability theory, it was proven that all errors of the closed-loop control system were uniformly ultimately bounded. Therefore, the control system structure was simple and ensured stability in achieving simultaneous trajectory tracking and balancing of the ball-balancing robot with uncertainty. Finally, the simulation results are given to verify the performance of the proposed controller through comparison results. As a result, the proposed method showed a 19.2% improved tracking error rate compared to the existing method.

scholarly journals Adaptive Dynamic Surface Control for Generator Excitation Control System

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Zhang Xiu-yu ◽  
Liu Cui-ping ◽  
Wang Jian-guo ◽  
Lin Yan
Keyword(s):  
Neural Network ◽  
Control System ◽  
Tracking Error ◽  
Dynamic Surface Control ◽  
Excitation Control ◽  
Dynamic Surface ◽  
Adaptive Dynamic ◽  
Surface Control ◽  
Control Scheme ◽  
Generator Excitation Control

For the generator excitation control system which is equipped with static var compensator (SVC) and unknown parameters, a novel adaptive dynamic surface control scheme is proposed based on neural network and tracking error transformed function with the following features: (1) the transformation of the excitation generator model to the linear systems is omitted; (2) the prespecified performance of the tracking error can be guaranteed by combining with the tracking error transformed function; (3) the computational burden is greatly reduced by estimating the norm of the weighted vector of neural network instead of the weighted vector itself; therefore, it is more suitable for the real time control; and (4) the explosion of complicity problem inherent in the backstepping control can be eliminated. It is proved that the new scheme can make the system semiglobally uniformly ultimately bounded. Simulation results show the effectiveness of this control scheme.

scholarly journals Adaptive Finite-time Dynamic Surface Neural Network Control of an Uncertain Robot with Output Constraint and Input Saturation

2021 ◽  
Author(s):  
Zhao Zhang ◽  
Lingxi Peng ◽  
Zhijia Zhao
Keyword(s):  
Neural Network ◽  
Finite Time ◽  
Control Method ◽  
Tracking Error ◽  
Input Saturation ◽  
Network Control ◽  
Neural Network Control ◽  
Dynamic Surface ◽  
Time Dynamic ◽  
Output Constraints

Abstract In this study, a finite-time dynamic surface neural network control is developed for an uncertain n-link robot subject to input saturation and output constraints. First, a barrier Lyapunov function and a hyperbolic tangent function are applied to solve the system constraints using a dynamic surface control. Subsequently, a radial basis function neural network is utilized to handle system uncertainties. Then, a finite-time filter is employed in the design to achieve the fast convergence and a Nussbaum function is employed to optimize the design process. Finally, the simulation results show that the dynamic tracking error is proved to converging to zero, and the proposed control method is effective and never violates the constraints.

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.

Backstepping Control Method for the Trajectory Tracking for the Underactuated Autonomous Underwater Vehicle

2013 ◽  
Vol 798-799 ◽  
pp. 484-488 ◽  
Author(s):  
Lei Wan ◽  
Nan Sun ◽  
Yu Lei Liao
Keyword(s):  
Control System ◽  
Trajectory Tracking ◽  
Autonomous Underwater Vehicle ◽  
Control Method ◽  
Autonomous Underwater Vehicles ◽  
Path Following ◽  
Underactuated System ◽  
Strong Nonlinearity ◽  
Parameter Uncertainties ◽  
Path Following Control

The underactuated autonomous underwater vehicles (AUV) have the characteristics of strong nonlinearity and model uncertainty. A method of backstepping path following control was raised for the trajectory tracking control problem of the AUV under Serret-Frenet frame. It transformed the original underactuated system into an actuated nonlinear system based on simplified analysis. A backstepping trajectory tracking controller was proposed based on backstepping method. By means of Lyapunov stability theory, it was proven that the proposed controller can guarantee the path following control system globally asymptotically stable. Simulation experiments show that the control system has good adaptability and robustness in case of parameter uncertainties and external disturbances to avoid shaking of performance.

scholarly journals Observer-based adaptive neural network backstepping sliding mode control for switched fractional order uncertain nonlinear systems with unmeasured states

2021 ◽  
pp. 002029402110211
Author(s):  
Tao Chen ◽  
Damin Cao ◽  
Jiaxin Yuan ◽  
Hui Yang
Keyword(s):  
Neural Network ◽  
Nonlinear Systems ◽  
Sliding Mode Control ◽  
Fractional Order ◽  
Sliding Mode ◽  
Tracking Error ◽  
Adaptive Neural Network ◽  
Dynamic Surface ◽  
Mode Control ◽  
The Stability

This paper proposes an observer-based adaptive neural network backstepping sliding mode controller to ensure the stability of switched fractional order strict-feedback nonlinear systems in the presence of arbitrary switchings and unmeasured states. To avoid “explosion of complexity” and obtain fractional derivatives for virtual control functions continuously, the fractional order dynamic surface control (DSC) technology is introduced into the controller. An observer is used for states estimation of the fractional order systems. The sliding mode control technology is introduced to enhance robustness. The unknown nonlinear functions and uncertain disturbances are approximated by the radial basis function neural networks (RBFNNs). The stability of system is ensured by the constructed Lyapunov functions. The fractional adaptive laws are proposed to update uncertain parameters. The proposed controller can ensure convergence of the tracking error and all the states remain bounded in the closed-loop systems. Lastly, the feasibility of the proposed control method is proved by giving two examples.

scholarly journals Adaptive speed control method for electromagnetic direct drive vehicle robot driver based on fuzzy logic

2019 ◽  
Vol 52 (9-10) ◽  
pp. 1344-1353 ◽  
Author(s):  
Gang Chen ◽  
Weigong Zhang ◽  
Xu Li ◽  
Bing Yu
Keyword(s):  
Fuzzy Logic ◽  
Control Method ◽  
Tracking Error ◽  
Speed Control ◽  
System Structure ◽  
Direct Drive ◽  
Test Standards ◽  
Speed Controller ◽  
Fuzzy Neural ◽  
Vehicle Test

To solve the shortcomings of existing control methods for an electromagnetic direct drive vehicle robot driver, including large speed tracking error and large mileage deviation, a new adaptive speed control method for the electromagnetic direct drive vehicle robot driver based on fuzzy logic is proposed in this paper. The electromagnetic direct drive vehicle robot driver adapts an electromagnetic linear motor as its drive mechanism. The control system structure is designed. The coordinated controller for multiple manipulators is presented. Moreover, an adaptive speed controller for the electromagnetic direct drive vehicle robot driver is proposed to achieve the accurate tracking of desired speed. Experiments are conducted using a Ford FOCUS car. Performances of the proposed method, proportional–integral–derivative, and fuzzy neural network are compared and analyzed. Experimental results demonstrate that the proposed control method can accurately track the target speed, and it can inhabit the change of speed caused by interference under different test conditions, and it has small mileage deviation, which can meet the requirements of national vehicle test standards.

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 Neural networks-based robust adaptive flight path tracking control of large transport

2018 ◽  
Vol 38 (3) ◽  
pp. 268-278
Author(s):  
Maolong Lv ◽  
Xiuxia Sun ◽  
G. Z. Xu ◽  
Z. T. Wang
Keyword(s):  
Neural Network ◽  
Control Method ◽  
Nonlinear Modeling ◽  
Dynamic Surface Control ◽  
Continuous Functions ◽  
Network Dynamic ◽  
Dynamic Surface ◽  
The Neural Network ◽  
Neural Network Dynamic ◽  
Robust Adaptive

For the ultralow altitude airdrop decline stage, many factors such as actuator nonlinearity, the uncertain atmospheric disturbances, and model unknown nonlinearity affect the precision of trajectory tracking. A robust adaptive neural network dynamic surface control method is proposed. The neural network is used to approximate unknown nonlinear continuous functions of the model, and a nonlinear robust term is introduced to eliminate the actuator’s nonlinear modeling error and external disturbances. From Lyapunov stability theorem, it is rigorously proved that all the signals in the closed-loop system are bounded. Simulation results confirm the perfect tracking performance and strong robustness of the proposed method.

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