Neuroadaptive control of elastic-joint robots using robust performance enhancement

Robotica ◽  
2001 ◽  
Vol 19 (6) ◽  
pp. 619-629 ◽  
Author(s):  
C.J.B. Macnab ◽  
G.M.T. D'Eleuterio
Keyword(s):  
Neural Network ◽  
Degrees Of Freedom ◽  
Performance Enhancement ◽  
Control Law ◽  
Network Stability ◽  
Elastic Joint ◽  
Lyapunov Techniques ◽  
The Neural Network ◽  
Control Scheme ◽  
Neuroadaptive Control

A neuroadaptive control scheme for elastic-joint robots is proposed that uses a relatively small neural network. Stability is achieved through standard Lyapunov techniques. For added performance, robust modifications are made to both the control law and the weight update law to compensate for only approximate learning of the dynamics. The estimate of the modeling error used in the robust terms is taken directly from the error of the network in modeling the dynamics at the currant state. The neural network used is the CMAC-RBF Associative Memory (CRAM), which is a modification of Albus's CMAC network and can be used for robots with elastic degrees of freedom. This results in a scheme that is computationally practical and results in good performance.

Recurrent Higher Order Neural Network Control for Output Trajectory Tracking with Neural Observers and Constrained Inputs

2010 ◽  
pp. 286-311 ◽  
Author(s):  
Luis J. Ricalde ◽  
Edgar N. Sanchez ◽  
Alma Y. Alanis
Keyword(s):  
Neural Network ◽  
Tracking Error ◽  
Network Control ◽  
Design Parameters ◽  
The Neural Network ◽  
Control Scheme ◽  
Output Trajectory ◽  
Error Dynamics ◽  
Adaptation Law ◽  
Constrained Inputs

This Chapter presents the design of an adaptive recurrent neural observer-controller scheme for nonlinear systems whose model is assumed to be unknown and with constrained inputs. The control scheme is composed of a neural observer based on Recurrent High Order Neural Networks which builds the state vector of the unknown plant dynamics and a learning adaptation law for the neural network weights for both the observer and identifier. These laws are obtained via control Lyapunov functions. Then, a control law, which stabilizes the tracking error dynamics is developed using the Lyapunov and the inverse optimal control methodologies . Tracking error boundedness is established as a function of design parameters.

scholarly journals A Biologically Inspired Framework for the Intelligent Control of Mechatronic Systems and Its Application to a Micro Diving Agent

2018 ◽  
Vol 2018 ◽  
pp. 1-16 ◽  
Author(s):  
Wallace M. Bessa ◽  
Gerrit Brinkmann ◽  
Daniel A. Duecker ◽  
Edwin Kreuzer ◽  
Eugen Solowjow
Keyword(s):  
Neural Network ◽  
Sliding Mode ◽  
Weight Vector ◽  
Mechatronic Systems ◽  
Biologically Inspired ◽  
Control Approach ◽  
The Neural Network ◽  
Control Scheme ◽  
Intelligent Behavior ◽  
Intelligent Controllers

Mechatronic systems are becoming an intrinsic part of our daily life, and the adopted control approach in turn plays an essential role in the emulation of the intelligent behavior. In this paper, a framework for the development of intelligent controllers is proposed. We highlight that robustness, prediction, adaptation, and learning, which may be considered the most fundamental traits of all intelligent biological systems, should be taken into account within the project of the control scheme. Hence, the proposed framework is based on the fusion of a nonlinear control scheme with computational intelligence and also allows mechatronic systems to be able to make reasonable predictions about its dynamic behavior, adapt itself to changes in the plant, learn by interacting with the environment, and be robust to both structured and unstructured uncertainties. In order to illustrate the implementation of the control law within the proposed framework, a new intelligent depth controller is designed for a microdiving agent. On this basis, sliding mode control is combined with an adaptive neural network to provide the basic intelligent features. Online learning by minimizing a composite error signal, instead of supervised off-line training, is adopted to update the weight vector of the neural network. The boundedness and convergence properties of all closed-loop signals are proved using a Lyapunov-like stability analysis. Numerical simulations and experimental results obtained with the microdiving agent demonstrate the efficacy of the proposed approach and its suitableness for both stabilization and trajectory tracking problems.

Neural Network-Based Speed Control of A Two-Mass-Model System

1999 ◽  
Vol 3 (5) ◽  
pp. 427-430 ◽  
Author(s):  
Rached Dhaouadi ◽  
◽  
Khaled Nouri
Keyword(s):  
Neural Network ◽  
Inverse Dynamics ◽  
Mass Model ◽  
Neural Network Learning ◽  
Inverse Control ◽  
Network Learning ◽  
The Neural Network ◽  
Control Scheme ◽  
On Line ◽  
Neural Network Structure

We present an application of artificial neural networks to the problem of controlling the speed of an elastic drive system. We derive a neural network structure to simulate the inverse dynamics of the system, then implement the direct inverse control scheme in a closed loop. The neural network learning is done on-line to adaptively control the speed to follow a stepwise changing reference. The experimental results with a two-mass-model analog board confirm the effectiveness of the proposed neurocontrol scheme.

Solution to the Forward and Inverse Kinematic Equations Using Multilayer Back-Propagation Neural Network

1994 ◽  
Author(s):  
Phani K. Nagarjuna ◽  
Athamaram H. Soni
Keyword(s):  
Neural Network ◽  
Degrees Of Freedom ◽  
Robot Manipulator ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Inverse Kinematic ◽  
Robot Arm ◽  
Kinematic Equation ◽  
Neural Network Approach ◽  
The Neural Network

Abstract The problem of inverse kinematics in Robotics, is a nonlinear mapping from a given cartesian coordinates to the desirable joint coordinates of the robot arm. It is found that an appropriately designed neural network can be trained to learn the non-linearity of the Inverse Kinematic Equation (IKE). We present an approach for solving the Forward Kinematic Equation (FKE) and the IKE by means of a Multi Layer Back-Propagation Neural Network (Rumelhart et al., 1986). The neural network approach is applied to a Two Degrees-of-Freedom (DOF) robot manipulator and the results are compared with those obtained using the analytical solution. The results obtained from the simulation of the neural network indicate a fairly accurate learning of the FKE and IKE by the Multi Layer Back-Propagation Neural Network.

Visual Control of Robotic Manipulator Based on Artificial Neural Network

1991 ◽  
Vol 3 (5) ◽  
pp. 394-400 ◽  
Author(s):  
Hideki Hashimoto ◽  
◽  
Takashi Kubota ◽  
Motoo Sato ◽  
Fumio Harashima ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Visual Information ◽  
Degrees Of Freedom ◽  
Robotic Manipulator ◽  
Inverse Kinematic ◽  
End Effector ◽  
Control Scheme ◽  
Neural Network System ◽  
Artificial Neural

This paper describes a control scheme for a robotic manipulator system which uses visual information to position and orientate the end-effector. In the scheme the position and the orientation of the target workpiece with respect to the base frame of the robot are assumed to be unknown, but the desired relative position and orientation of the end-effector to the target workpiece are given in advance. The control system directly integrates visual data into the servoing process without subdividing the process into determination of the position, orientation of the workpiece and inverse kinematic calculation. An artificial neural network system is used for determining the change in joint angles required in order to achieve the desired position and orientaion. The proposed system can control the robot so that it approach the desired position and orientaion from arbitary initial ones. Simulation for the robotic manipulator with six degrees of freedom is done. The validity and the effectiveness of the proposed control scheme are varified by computer simulations.

A Neural Network Based Adaptive Tracking Controller for Nonholonomic Wheeled Mobile Robots With Unknown Dynamics

2010 ◽  
Author(s):  
Omid Mohareri ◽  
Rached Dhaouadi
Keyword(s):  
Neural Network ◽  
Control Algorithm ◽  
Wheeled Mobile Robots ◽  
Adaptive Capabilities ◽  
The Neural Network ◽  
Robot Tracking ◽  
Control Scheme ◽  
Tracking Motion ◽  
Backstepping Controller ◽  
Simulation Results

This paper presents the design, implementation and comparative analysis of an intelligent neural network based controller used for adaptive trajectory tracking of a wheeled mobile robot with unknown dynamics. In this proposed control scheme, the neural network is used to continuously tune the gains of the kinematic based controller in a backstepping structure. The online learning and adaptive capabilities of the neural network are utilized to achieve a smooth and fast robot tracking motion. The simulation results are used to verify the tracking performance of the proposed control algorithm and to compare it with the conventional backstepping controller.

scholarly journals Neural Network Control for the Probe Landing Based on Proportional Integral Observer

2015 ◽  
Vol 2015 ◽  
pp. 1-10
Author(s):  
Yuanchun Li ◽  
Tianhao Ma ◽  
Bo Zhao
Keyword(s):  
Neural Network ◽  
Control Algorithm ◽  
Control Method ◽  
Landing Site ◽  
Network Control ◽  
Control Law ◽  
Neural Network Control ◽  
Control Approach ◽  
Proportional Integral ◽  
The Neural Network

For the probe descending and landing safely, a neural network control method based on proportional integral observer (PIO) is proposed. First, the dynamics equation of the probe under the landing site coordinate system is deduced and the nominal trajectory meeting the constraints in advance on three axes is preplanned. Then the PIO designed by using LMI technique is employed in the control law to compensate the effect of the disturbance. At last, the neural network control algorithm is used to guarantee the double zero control of the probe and ensure the probe can land safely. An illustrative design example is employed to demonstrate the effectiveness of the proposed control approach.

Fuzzy Control of a Novel Type of Translational Meshing Motors

2010 ◽  
Vol 121-122 ◽  
pp. 1038-1043
Author(s):  
Wei Wang ◽  
Xin Jian Shan ◽  
Shi Min Wei
Keyword(s):  
Neural Network ◽  
Control System ◽  
Fuzzy Control ◽  
Fuzzy Controller ◽  
Torque Control ◽  
Training Procedure ◽  
Test Results ◽  
Model Reference Control ◽  
The Neural Network ◽  
Control Scheme

Owing to the nonlinear characteristic of a novel type of translational meshing motor with model uncertainties, a model reference control system which consists of a neural network and a fuzzy controller is used. The torque model is identified based on BP neural network, and then Fuzzy controller works as the controller. The description of the control system and training procedure of the neural network are given. The test results obtained for a torque control scheme suitable for the control of the motor are also presented to verify the effectiveness of the proposed nonlinear control scheme. It has been found that the fuzzy control system is able to work reliably.

scholarly journals Neural-Network-Based Collaborative Control for Continuous Unknown Nonlinear Systems

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Siyu Gao ◽  
Xin Wang
Keyword(s):  
Neural Network ◽  
Nonlinear System ◽  
Cooperative Learning ◽  
Cooperative Control ◽  
Closed Loop ◽  
Collaborative Control ◽  
The Neural Network ◽  
Control Scheme ◽  
Optimal Value ◽  
Trained Neural Network

This paper proposes an NN-based cooperative control scheme for a type of continuous nonlinear system. The model studied in this paper is designed as an interconnection topology, and the main consideration is the connection mode of the undirected graph. In order to ensure the online sharing of learning knowledge, this paper proposes a novel weight update scheme. In the proposed update scheme, the weights of the neural network are discrete, and these discrete weights can gradually approach the optimal value through cooperative learning, thereby realizing the control of the unknown nonlinear system. Through the trained neural network, it is proved if the interconnection topology is undirected and connected, the state of the unknown nonlinear system can converge to the target trajectory after a finite time, and the error of the system can converge to a small neighbourhood around the origin. It is also guaranteed that all closed-loop signals in the system are bounded. A simulation example is provided to more intuitively prove the effectiveness of the proposed distributed cooperative learning control scheme at the end of the article.

scholarly journals Assignment of Focus Position with Convolutional Neural Networks in Adaptive Lens Based Axial Scanning for Confocal Microscopy

2022 ◽  
Vol 12 (2) ◽  
pp. 661
Author(s):  
Katharina Schmidt ◽  
Nektarios Koukourakis ◽  
Jürgen W. Czarske
Keyword(s):  
Neural Network ◽  
Applied Voltage ◽  
Laser Scanning ◽  
Degrees Of Freedom ◽  
Single Shot ◽  
Training Procedure ◽  
Focus Position ◽  
The Neural Network ◽  
Complex Adaptive ◽  
Axial Scanning

Adaptive lenses offer axial scanning without mechanical translation and thus are promising to replace mechanical-movement-based axial scanning in microscopy. The scan is accomplished by sweeping the applied voltage. However, the relation between the applied voltage and the resulting axial focus position is not unambiguous. Adaptive lenses suffer from hysteresis effects, and their behaviour depends on environmental conditions. This is especially a hurdle when complex adaptive lenses are used that offer additional functionalities and are controlled with more degrees of freedom. In such case, a common approach is to iterate the voltage and monitor the adaptive lens. Here, we introduce an alternative approach which provides a single shot estimation of the current axial focus position by a convolutional neural network. We use the experimental data of our custom confocal microscope for training and validation. This leads to fast scanning without photo bleaching of the sample and opens the door to automatized and aberration-free smart microscopy. Applications in different types of laser-scanning microscopes are possible. However, maybe the training procedure of the neural network must be adapted for some use cases.

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