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Adaptive neural controller
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This paper presents the design and development of a Neural Netwwork-Adaptive backstepping (NN-Adaptive backstepping) controller to track the trajectory of a differentially controlled mobile robot. The controller is designed based on the robot dynamics equation and on the basis of the Backstepping controller.
11p
visergeyne
18-06-2024
0
0
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Part 2 of ebook "Measuring technology and mechatronics automation in electrical engineering" provides readers with contents including: hardware-in-the-loop for on-line identification of SSP driving motor; hybrid adaptive fuzzy vector control vector control for single-phase induction motors; hybrid intelligent algorithm based on hierarchical encoding for training of RBF neural network; improved fuzzy neural network for stock market prediction and application; landslide recognition in mountain image based on support vector machine;...
259p
dongmelo
20-05-2024
4
1
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In this paper, a modified feedback error learning approach (called MFEL) is proposed for a nonlinear system. In MFEL, an inverse evolutionary neural (IEN) model that dynamically identifies offline all nonlinear features of the nonlinear system, provides the initial value of a feedforward compensator.
8p
vijeff
01-12-2023
5
3
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This paper proposes a simple adaptive controller for pitch angle control of the variable speed wind turbine. The aim of the controller is to keep the speed of the generator at the rated value when the wind speed is above the nominal value.
8p
vijeff
30-11-2023
5
3
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The paper investigates the lane following and changing maneuvers of autonomous vehicles in the presence of unknown disturbances, taking into account the dynamic system states and input constraints. The integrated longitudinal-lateral and yaw rate dynamics of the vehicle are simultaneously considered to improve the tracking accuracy and system stability when navigating under critical conditions.
11p
vihawkeye
26-05-2023
12
5
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The content of this research paper presents the method to control Grid Side Converter (GSC) of a Doubly Fed Induction Generator (DFIG)-based wind turbine (WT) for improving the stability of the generator. To present the power systems, a well-known power system with three Synchronous Generators (SGs) and nine buses that is widely used in many types of research is applied.
6p
vidoctorstrange
06-05-2023
2
2
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This paper confirms the practical effect of applying Artificial Neural Networks (ANNs) using Radial basis function (RBF) bases on Sliding mode control (SMC) to control nonlinear systems. The proposed algorithm is put into comparison with the super twisting 2-SMC, which was designed to reduce chattering and increase the performance of conventional SMC.
7p
vidoctorstrange
06-05-2023
9
5
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This paper represents superior properties of advanced control methods such as Fuzzy Logic, Neural network to PID controller for uncertainties systems to achieve good tracking response in real time. All three control methods are based on the feedback error signal that is then calculated on the processor through algorithms and outputting the optimal control signals.
7p
vidoctorstrange
06-05-2023
8
4
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In this paper, we consider the adaptive sliding mode control with radial basis function neural networks for the Omni-directional mobile robot. This is a holonomic robot that can operate easily in small and narrow spaces, due to the ability of flexible rotational and translational moving, simultaneously and independently.
8p
vidoctorstrange
06-05-2023
12
6
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This paper presents comparative simulation results of Ha Tien - Phu Quoc power system using a Series Static Synchronous Compensator (SSSC). For improving the stability of the studied system, an Adaptive Neural Fuzzy Inference System (ANFIS) controller is designed.
4p
vispyker
16-11-2022
3
1
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This present study proposes a design and the analysis of the novel adaptive robust neural networks (ARNNs) based on the backstepping control method for industrial robot manipulators (IRMs). In this research, the ARNNs controller has combined the advantages of Radial Basis Function neural network (RBFNN), the robust term, and adaptive backstepping control technique without the requirement of prior knowledge.
7p
vigeneralmotors
13-07-2022
471
10
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The paper has developed an adaptive algorithm using neural network for controlling dual-arm robotic system in stable holding a rectangle object and moving it to track the desired trajectories. Firstly, an overall dynamic of the system including the dual-arm robot and the object is derived based on Euler-Lagrangian principle.
7p
visherylsandber
04-07-2022
8
2
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This paper presents a controller synthesis method for continuous mixing technology commonly encountered in industry. The kinematic model of the control object is described in the form of a system of nonlinear equations and is affected by unknown external disturbance.
9p
viirenerosenfeld
02-06-2022
11
3
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This paper presents a method to synthesize the adaptive sliding mode controller for a class of MIMO Euler-Lagarance systems with variable parameters. We perform a Taylor series expansion of a class of MIMO Euler-Lagarance systems into nonlinear state-space equations, considering cases of varying parameters and unmeasured external disturbances.
9p
viirenerosenfeld
26-05-2022
17
2
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This paper presents a novel adaptive controller for two-wheeled selfbalancing mobile robots combining sliding mode control and hierarchical sliding control techniques. In addition, the radial basis function neural networks (RBFNN) are also applied to approximate the uncertain components in the system.
9p
viericschmid
12-01-2022
23
3
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The paper has developed an adaptive control using neural network for controlling a dual-arm robotic system in moving a rectangle object to the desired trajectories. Firstly, the overall dynamics of the manipulators and the object have been derived based on Euler-Lagrangian principle. And then based on the dynamics, a controller has been proposed to achieve the desired trajectories of the grasping object
9p
spiritedaway36
28-11-2021
35
5
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In this paper, a robust hierarchical method for trajectory tracking control of a quadrotor unmanned aerial vehicle (UAV) subjected to parameter uncertainties and external disturbances is presented. A robust control scheme based on a fast nonsingular terminal sliding mode strategy is designed to achieve fast response and excellent tracking accuracy. Moreover, a radial basis function artificial neural network with online adaptive schemes to estimate unknown aerodynamic parameters and external disturbances is developed to improve the control performance and reduce the chattering phenomenon.
6p
cothumenhmong11
05-05-2021
14
3
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In this study, a combination of backstepping technique, double recurrent fuzzy wavelet based on neural networks (DRFWBONNs), adaptive sliding mode controller (ASMC), and adaptive proportional-integral (API) control with dead-zone friction is introduced to the industrial robot manipulator (IRM). Simulation results show the high performance of this control method when compared to adaptive-fuzzy (AF) and proportional-integral-derivative (PID) controller.
8p
quenchua10
18-01-2021
30
2
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In this work, a sliding mode control (SMC) method and a composite learning SMC (CLSMC) method are proposed to solve the synchronization problem of chaotic fractional-order neural networks (FONNs). A sliding mode surface and an adaptive law are constructed to update parameter estimation. The SMC ensures that the synchronization error asymptotically tends to zero under a strict permanent excitation (PE) condition. To reduce its rigor, online recording data together with instantaneous data is used to define a prediction error about the uncertain parameter.
10p
dayhoctainha
10-09-2020
22
0
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This article highlights a robust adaptive tracking control approach for a nonholonomic wheeled mobile robot by which the bad problems of both unknown slippage and uncertainties are dealt with. The radial basis function neural network in this proposed controller assists unknown smooth nonlinear dynamic functions to be approximated. Furthermore, a technical solution is also carried out to avoid actuator saturation. The validity and efficiency of this novel controller, finally, are illustrated via comparative simulation results.
18p
nguyenanhtuan_qb
18-06-2020
27
2
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