A Neural Network Controller for a Class of Nonlinear Non-Minimum Phase Systems with Application to a Flexible-Link Manipulator

2004 ◽  
Vol 127 (2) ◽  
pp. 289-294 ◽  
Author(s):  
H. A. Talebi ◽  
R. V. Patel ◽  
K. Khorasani
Keyword(s):  
Neural Network ◽  
Explicit Knowledge ◽  
Open Loop ◽  
Flexible Manipulator ◽  
Phase System ◽  
Zero Dynamics ◽  
Practical Case ◽  
Nonminimum Phase ◽  
Neural Network Controller ◽  
Improved Performance

This paper investigates the problem of controlling a nonlinear nonminimum phase system. An output re-definition strategy is first introduced to guarantee stable zero dynamics. This output re-definition scheme is applicable to a class of open-loop stable nonlinear systems whose input–output maps contain nonlinear terms in the output and linear terms in the input. No explicit knowledge about the nonlinearities of the system is required. The nonlinearities of the system are identified by a neural network. The identified neural network model is then used in modifying the zero dynamics of the system. A stable∕anti-stable factorization is performed on the zero dynamics of the system. The new output is re-defined using the neural identifier and the stable part of the zero dynamics. A controller is then designed based on the new output whose zero dynamics are stable and can be inverted. An experimental setup of a single-link flexible manipulator is considered as a practical case study of a nonlinear nonminimum phase system. Experimental results are presented to illustrate the advantages and improved performance of the proposed tracking controller over both linear and nonlinear conventional controllers in the presence of unmodeled dynamics and parameter variations.

scholarly journals Adaptive Neural Network Control with Backstepping for Surface Ships with Input Dead-Zone

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Guoqing Xia ◽  
Xingchao Shao ◽  
Ang Zhao ◽  
Huiyong Wu
Keyword(s):  
Neural Network ◽  
Explicit Knowledge ◽  
Dead Zone ◽  
Nonlinear Function ◽  
Lyapunov Theory ◽  
Adaptive Neural Network ◽  
Neural Network Controller ◽  
Network Controller ◽  
Adaptive Neural Network Control ◽  
Fuzzy Logic Technique

This paper addresses the problem of adaptive neural network controller with backstepping technique for fully actuated surface vessels with input dead-zone. The combination of approximation-based adaptive technique and neural network system is used for approximating the nonlinear function of the ship plant. Through backstepping and Lyapunov theory synthesis, an indirect adaptive network controller is derived for dynamic positioning ships without dead-zone property. In order to improve the control effect, a dead-zone compensator is derived using fuzzy logic technique to handle the dead-zone nonlinearity. The main advantage of the proposed controller is that it can be designed without explicit knowledge about the ship motion model, and dead-zone nonlinearity is well compensated. A set of simulations is carried out to verify the performance of the proposed controller.

Dynamic Analysis of Noncollocated Flexible Arms and Design of Torque Transmission Mechanisms

1994 ◽  
Vol 116 (2) ◽  
pp. 201-207 ◽  
Author(s):  
Jahng-Hyon Park ◽  
Haruhiko Asada
Keyword(s):  
Closed Loop ◽  
Open Loop ◽  
Design Guidelines ◽  
Phase System ◽  
Nonminimum Phase ◽  
Transmission Mechanisms ◽  
Flexible Arm ◽  
Torque Transmission ◽  
Endpoint Control ◽  
Flexible Arms

A new actuation method for one-link flexible arms is presented. The endpoint control of a flexible arm has been known as a nonminimum phase system due to the noncollocated sensor and actuator. By relocating the actuator near the endpoint, the system can be modified to approximate a minimum phase system. In order to implement this, transmission mechanisms are developed which transform the actuator torque to a combination of force and torque and transmit them to an appropriate point on the arm link. Exact pole-zero configurations are analyzed with regard to the location of the actuation point and the type of actuator used. Guidelines for design of the transmission mechanisms and the actuation points are developed with respect to the operation bandwidth, stability and controllability. A prototype flexible arm is designed based on the design guidelines and open-loop and closed-loop tests are performed to verify the effectiveness.

Direct Adaptive Control of Underwater Vehicles using Neural Networks

2003 ◽  
Vol 9 (5) ◽  
pp. 605-619 ◽  
Author(s):  
Myung-Hyun Kim ◽  
Daniel J. Inman
Keyword(s):  
Neural Network ◽  
Adaptive Control ◽  
Network Architecture ◽  
Explicit Knowledge ◽  
Tracking Error ◽  
Network Control ◽  
Lyapunov Theory ◽  
Direct Adaptive Control ◽  
Neural Network Controller ◽  
Comparison Of The Results

A direct adaptive neural network controller is developed for a model of an underwater vehicle. A radial basis neural network and a multilayer neural network are used in the closed-loop to approximate the nonlinear vehicle dynamics. No prior off-line training phase and no explicit knowledge of the structure of the plant are required, and this scheme exploits the advantages of both neural network control and adaptive control. A control law and a stable on-line adaptive law are derived using the Lyapunov theory, and the convergence of the tracking error to zero and the boundedness of signals are guaranteed. A comparison of the results with different neural network architecture is made, and the performance of the controller is demonstrated by computer simulations.

A Review on DSTATCOM Neural Network Control Algorithm for Power Quality Improvement

2015 ◽  
Vol 785 ◽  
pp. 363-367
Author(s):  
N.H. Baharudin ◽  
Syed Idris Syed Hassan ◽  
Puteh Saad ◽  
Tunku Muhammad Nizar Tunku Mansur ◽  
Rosnazri Ali
Keyword(s):  
Neural Network ◽  
Quality Improvement ◽  
Power Quality ◽  
Control Algorithm ◽  
Industrial Applications ◽  
Network Control ◽  
Phase System ◽  
Neural Network Control ◽  
Neural Network Controller ◽  
Power Quality Improvement

This paper reviews neural network control algorithm for power quality improvement. Further, this paper focuses on the neural network control algorithm for DSTATCOM and surveys its area of improvements. Various architectures of Neural Network such as Adaline/Widrow-Hoff, perceptron, Back-propagation (BP), Hopfield, and Radial Basis Function (RBF) that has been reviewed in this paper. It is found that many researches on theoretical works and single phase system are widely performed, whereas its application on distribution network for three phase system is hardly found. Even so much improvement that have been done by researchers theoretically to improve the drawbacks of Neural Network controller; there are still wide gaps for verification through experimental implementation and industrial applications.

Partitioned Neural Network Control and Hierarchical Fuzzy Logic Control for Space Flexible Manipulator With Unknown Parameters

2010 ◽  
Author(s):  
Dengfeng Huang ◽  
Li Chen
Keyword(s):  
Neural Network ◽  
Fuzzy Logic ◽  
Trajectory Tracking ◽  
Rigid Motion ◽  
Flexible Manipulator ◽  
Coordinated Motion ◽  
Fast Subsystem ◽  
Unknown Parameters ◽  
Neural Network Controller ◽  
Slow Subsystem

The trajectory tracking and vibration suppression control of free-floating space flexible manipulator with an attitude controlled base are discussed. With the law of conservation of momentum, the Lagrangian principle is utilized to model the dynamic function of the space flexible manipulator incorporating the assumed modes method. Using singular perturbation theory, a slow subsystem describing the rigid motion and a fast subsystem corresponding to the flexible motion are obtained. Then, a two-time scale controller for coordinated motion between the base’s attitude and the manipulator’s joints of space flexible manipulator system is designed. The slow-subsystem partitioned neural network controller dominates the trajectory tracking of coordinated motion in the presence of unknown parameters. The fast-subsystem controller damps out the vibration of the flexible link by hierarchical fuzzy logic controller. Numerical simulation results illustrate that the proposed controller is reliable and effective.

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