Top PDF Neuro-fuzzy modelling and control of robotic manipulators

Neuro-fuzzy modelling and control of robotic manipulators

Neuro-fuzzy modelling and control of robotic manipulators

controllers have been reported due to recent advances in intelligent modelling techniques. Actually, the key characteristics of the IMC described above also apply in the non-linear case. For example, a number of researchers have suggested Neural Networks to provide the non-linear plant models necessary for IMC from input/output data collected from the plant. Likewise, the application of neural networks to the inverse modelling o f non-linear systems is common in the literature, particularly in the field of robotics control. This due to the fact that Neural Networks parallel processing architecture, adaptation and learning capabilities, and fast processing for large-scale dynamic systems provide solid base to represent the robot forward and inverse model within the IMC controller structure. Li et al. proposed compensations procedure for the robot dynamics, before the standard IMC scheme can be applied. This compensation procedure consists o f two stages, namely pre-linearization using approximate inverse dynamic model and pre-stabilization using a conventional PD feedback loop [Li et al., 1995]. Li et al. proposed an adaptive algorithm based on Neural Networks to construct a joint-based IMC for robot manipulators. In this method, a Neural Network inverse model and a conventional PD feedback were used to pre-linearise and pre-stabilize the plant in a fixed structure IMC controller. The utilized Neural Network consists of an n sub-network structure, each sub-network operates independently based on each link angle, velocity, and acceleration to generate respective link actuating torque.
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Robust control for robotic manipulators with non smooth strategy

Robust control for robotic manipulators with non smooth strategy

Abstract: In this paper, a novel non-smooth robust control approach is presented for robotic manipulators. By using decimal power rule in Lyapunov redesign methods, the conventional robust control for robot is improved. Our approach can achieve higher control precision with faster convergence speed. The formulations of estimating residual set and settling time have been initially established. The practical stability property is analyzed. An illustrative example is bench tested to validate the effectiveness of the proposed approach.

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Synchronized control with neuro agents for leader follower based multiple robotic manipulators

Synchronized control with neuro agents for leader follower based multiple robotic manipulators

disturbance and so forth, which deteriorates system control performance seriously. It should be accommodated in synchronized controller design. NN has strong learning ability and can approximate almost all of nonlinear function [20]. It has found that NN can estimate system uncertainty of robotic manipulator online effectively. Some NN based adaptive control algorithms are designed for single robotic manipulator [21-23]. Note that the mentioned NN based control algorithms are appropriate for the single robotic manipulators but cannot be used to the MRMS. Some indispensable extensions are required to design NN based synchronized controller according to the MRMS’s kinematics and dynamics properties. It is not trivial to design the new RBF NN based adaptive law for MRMS because the leader-follower based synchronization error and graph weighted adjacency matrices should be embedded into it. The motivations of using RBF NN in MRMS are two folds: (1) It can compensate follower manipulators’ system uncertainty online and then reduces the controller design complexity. (2) It can estimate the leader manipulator’s acceleration online which is very difficult to measure in practice. Most of the leader-follower control algorithms assume that the bound of the acceleration should be given before the controller design [24] or complex observer should be used for the estimation [25]. In summary, using RBF NN in synchronized control of MRMS is novel, efficient, and challenging in designing such class of control systems and can simplify the controller design.
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NEURO FUZZY MODELLING AND CONTROL OF MULTISTAGE DYNAMIC PROCESSES THAT DEPEND ON 
INPUTS WITH UNCERTAINTY ELEMENTS

NEURO FUZZY MODELLING AND CONTROL OF MULTISTAGE DYNAMIC PROCESSES THAT DEPEND ON INPUTS WITH UNCERTAINTY ELEMENTS

Currently, the high-quality control of complex processes requires highly precise calculation of significant parameters. Methods of artificial intelligence based on fuzzy and neuro-fuzzy modelling are widely used in the problems of modelling processes that are characterised by complexity and presence of measurement accuracies. The advantages of fuzzy models include resistance to inaccurate input data. Imprecision in fuzzy models is accounted for by fuzzification – conversion of crisp input values into fuzzy ones. Neuro-fuzzy models are very popular, as they combine advantages of fuzzy models and neural networks, which are the simplicity and ability to automatically configure parameters (training).
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Adaptive Hybrid Regressor and Approximation Control of Robotic Manipulators in Constrained Space

Adaptive Hybrid Regressor and Approximation Control of Robotic Manipulators in Constrained Space

trajectory of robots with uncertain parameters and nonlinear disturbances. The idea behind the adaptive approximation control is to estimate the uncertain term in terms of weighting and basis function matrices, then design a control law based on the update of the weighting matrix. The strength of approximation technique is that it can be applied to different types of robots using the same structure of control law, while this is not the case for a regressor technique in which each robot has its own regressor matrix. In general, there two types of adaptive approximation control: partitioned approximation, and augmented approximation. Partitioned approximation means an estimation of each term on the left-hand side of the equation of motion of the target robot in terms of basis functions and weighting function matrices. Whereas augmented approximation attempts to approximate the whole uncertainty in one term. Lewis et al. [26-28] have applied both the partitioned and augmented approximation control separately on robotic manipulators. Although the authors preferred to use a partitioned approximation, they did not justify the computational complexity inherent in their algorithm when it is used with high DoF robot; the computational complexity grows exponentially with the DoFs of the robots. Huang and Chien [29] have applied partitioned approximation-based adaptive control for robot manipulators considering different cases such as actuator dynamics, joint flexibility, and impedance control. Al-Shuka et al. [24] have extended the work of [29] to be applied on high DoF robot by using the Virtual Decomposition Control (VDC) [23]; the idea of the VDC is to virtually decompose the whole robotic system into sub-systems and controlling each subsystem separately such that the Lyapunov's stability of the whole system is guaranteed. Liu [30] has applied separately the partitioned and augmented approximation-based adaptive control for nonlinear electro-mechanical systems. Cong et al. [31] have proposed function approximation-based sliding mode adaptive control for DC motor with dead zone uncertainty. The authors have approximated the unmodeled dynamics by using orthogonal Laguerre functions and another sliding mode term. The idea is to estimate the uncertain term by updating the weighting coefficients of Laguerre functions and guaranteeing the output error by using Lyapunov's stability.
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Adaptive Control for Robotic Manipulators base on RBF Neural Network

Adaptive Control for Robotic Manipulators base on RBF Neural Network

The trajectory tracking control problems of robotic manipulators are attracted more and more attentions [1-3]. However, since robotic manipulators system is a time-varying, strong-coupling and non-linear system, and it contains many traits such as parameter errors, unmodeled dynamics external interference as well as various other unknown non- linear in the actual project. The traditional PID control schemes is difficult to obtain good control precision for the robot, Therefore, variety intelligent control schemes based on nonlinear compensation method are continuously put forward in recent years [4-13].
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Towards Sophisticated Control of Robotic Manipulators: An Experimental Study on a Pseudo-Industrial Arm

Towards Sophisticated Control of Robotic Manipulators: An Experimental Study on a Pseudo-Industrial Arm

This paper presents the design, simulation and hardware realization of CTC and VSC strategies. The simulation results have been verified through experimental implementation on a physical platform. Trajectory tracking results showed that the derived laws can effectively track the desired reference input for both non-linear control methods. The coupling effects present in the joints are less visible in the simulation but are more prominent in the hardware implementation. Future work will include a task dependent performance comparison of robust control strategies on multi-DOF robotic manipulators.
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A Robust Fuzzy Tracking Control Scheme for Robotic Manipulators with Experimental Verification

A Robust Fuzzy Tracking Control Scheme for Robotic Manipulators with Experimental Verification

The performance of any fuzzy logic controller (FLC) is greatly dependent on its inference rules. In most cases, the closed-loop control performance and stability are enhanced if more rules are added to the rule base of the FLC. However, a large set of rules requires more on-line computational time and more parameters need to be adjusted. In this paper, a robust PD-type FLC is driven for a class of MIMO second order nonlin- ear systems with application to robotic manipulators. The rule base consists of only four rules per each de- gree of freedom (DOF). The approach implements fuzzy partition to the state variables based on Lyapunov synthesis. The resulting control law is stable and able to exploit the dynamic variables of the system in a lin- guistic manner. The presented methodology enables the designer to systematically derive the rule base of the control. Furthermore, the controller is decoupled and the procedure is simplified leading to a computationally efficient FLC. The methodology is model free approach and does not require any information about the sys- tem nonlinearities, uncertainties, time varying parameters, etc. Here, we present experimental results for the following controllers: the conventional PD controller, computed torque controller (CTC), sliding mode con- troller (SMC) and the proposed FLC. The four controllers are tested and compared with respect to ease of design, implementation, and performance of the closed-loop system. Results show that the proposed FLC has outperformed the other controllers.
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NEURO FUZZY MODELLING AND CONTROL OF MULTISTAGE DYNAMIC PROCESSES THAT DEPEND ON 
INPUTS WITH UNCERTAINTY ELEMENTS

NEURO FUZZY MODELLING AND CONTROL OF MULTISTAGE DYNAMIC PROCESSES THAT DEPEND ON INPUTS WITH UNCERTAINTY ELEMENTS

But, being de facto a standard in consumer sector of international communications, Skype in corporate sector is often losing to more centralized VoIP solutions built on a number of technologies established around SIP protocol [2]. In particular, such centralized solutions allow for collecting statistics, control and adjustment from a single point, building centers for distributed processing of incoming and outcoming voice traffic based on a single information system. It is logical that such technologies are becoming the basis of most
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NEURO FUZZY MODELLING AND CONTROL OF MULTISTAGE DYNAMIC PROCESSES THAT DEPEND ON 
INPUTS WITH UNCERTAINTY ELEMENTS

NEURO FUZZY MODELLING AND CONTROL OF MULTISTAGE DYNAMIC PROCESSES THAT DEPEND ON INPUTS WITH UNCERTAINTY ELEMENTS

REVIEW OF THE MAIN PARTICIPANTS communications in web browser are allowed by the OF MARKET OF STREAMING DATA several technologies: use of various web-browser TRANSMISSION IN A WEB-BROWSE[r]

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NEURO FUZZY MODELLING AND CONTROL OF MULTISTAGE DYNAMIC PROCESSES THAT DEPEND ON 
INPUTS WITH UNCERTAINTY ELEMENTS

NEURO FUZZY MODELLING AND CONTROL OF MULTISTAGE DYNAMIC PROCESSES THAT DEPEND ON INPUTS WITH UNCERTAINTY ELEMENTS

The system of manual and automated drilling control systems have been studied. An automated system has been developed which permits simultaneous tracking of variations of structural and strength properties of rock mass during drilling. Technological flowchart of operation of the considered system has been developed. The structure and mathematical dependences have been determined on the basis of which the drilling control system with subsystem of monitoring of rock properties operates. Maximum efficiency of rock destruction during roller drilling of boreholes has been studied as a function of drill bit rotation frequency, time of energy transfer leading to destruction of the required rock amount and feed thrust of working unit. The calculation procedure has been developed of optimum process variables of drill bit drilling of rocks with significant fracturing, lamination and varying drillability factor. Comparative analysis of increase in productivity of drilling rig as a consequence of application of automated system based on adaptive rotating-feeding device. The necessity to equip drilling rigs with automated intelligent system has been substantiated with capabilities to provide duly rapid response of the system to variations of working object properties and maintaining of optimum ratio of the adjusted process variables of "drilling rig--drill bit--rock ore" system. Execution of the aforementioned tasks by intelligent control system will make it possible to reduce drilling expenses and increase in operation efficiency of the mentioned flowchart. Keywords: Automated Control System, Closed Loop, Adaptive Element.
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NEURO FUZZY MODELLING AND CONTROL OF MULTISTAGE DYNAMIC PROCESSES THAT DEPEND ON 
INPUTS WITH UNCERTAINTY ELEMENTS

NEURO FUZZY MODELLING AND CONTROL OF MULTISTAGE DYNAMIC PROCESSES THAT DEPEND ON INPUTS WITH UNCERTAINTY ELEMENTS

For effective and quick reconfiguration of the server in accordance with the current requirements of the system for resistance to time analysis, for performance and for consumption of memory Mamdani mechanism of fuzzy logic operating on the mini-max principle is used. The rule base of fuzzy controller operating on the classical mechanism of Mamdani, consists of 63 rules. Conducted investigations confirmed the efficiency of means of optimal selection of method of the modular exponentiation based on Mamdani fuzzy inference.
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NEURO FUZZY MODELLING AND CONTROL OF MULTISTAGE DYNAMIC PROCESSES THAT DEPEND ON 
INPUTS WITH UNCERTAINTY ELEMENTS

NEURO FUZZY MODELLING AND CONTROL OF MULTISTAGE DYNAMIC PROCESSES THAT DEPEND ON INPUTS WITH UNCERTAINTY ELEMENTS

In this paper, a non-destructive condition assessment approach has been proposed for automobile rotating machine using data mining technique through frequency domain analysis on acoustic[r]

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NEURO FUZZY MODELLING AND CONTROL OF MULTISTAGE DYNAMIC PROCESSES THAT DEPEND ON 
INPUTS WITH UNCERTAINTY ELEMENTS

NEURO FUZZY MODELLING AND CONTROL OF MULTISTAGE DYNAMIC PROCESSES THAT DEPEND ON INPUTS WITH UNCERTAINTY ELEMENTS

The simulation results showed that the long TPF improves both of UE User Equipment throughput and cell eNodeB throughput, however the achieved scheduler fairness in this case is low.. On[r]

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NEURO FUZZY MODELLING AND CONTROL OF MULTISTAGE DYNAMIC PROCESSES THAT DEPEND ON 
INPUTS WITH UNCERTAINTY ELEMENTS

NEURO FUZZY MODELLING AND CONTROL OF MULTISTAGE DYNAMIC PROCESSES THAT DEPEND ON INPUTS WITH UNCERTAINTY ELEMENTS

With robustness of Fuzzy k-Medoids on outliers existence, its invariant property under translation and data transformation and also kernel function capability while dealing with multidimensional data, in this paper we develop a combination of Fuzzy k-Medoids method and kernel method, which is called Fuzzy Kernel k- Medoids (FKkM).

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NEURO FUZZY MODELLING AND CONTROL OF MULTISTAGE DYNAMIC PROCESSES THAT DEPEND ON 
INPUTS WITH UNCERTAINTY ELEMENTS

NEURO FUZZY MODELLING AND CONTROL OF MULTISTAGE DYNAMIC PROCESSES THAT DEPEND ON INPUTS WITH UNCERTAINTY ELEMENTS

Reddy L.S.S, "Automated generation of Test cases for testing critical regions of Embedded systems through Adjacent Pair-wise Testing", International Journal of Mathematics and Computatio[r]

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NEURO FUZZY MODELLING AND CONTROL OF MULTISTAGE DYNAMIC PROCESSES THAT DEPEND ON 
INPUTS WITH UNCERTAINTY ELEMENTS

NEURO FUZZY MODELLING AND CONTROL OF MULTISTAGE DYNAMIC PROCESSES THAT DEPEND ON INPUTS WITH UNCERTAINTY ELEMENTS

the simulation results show that the new JUS-CQI increased slightly as the number of users in increased to 22, compared to JUS 0.96 of JUSCQI compare to 0.89 of JUS; this is because in t[r]

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NEURO FUZZY MODELLING AND CONTROL OF MULTISTAGE DYNAMIC PROCESSES THAT DEPEND ON 
INPUTS WITH UNCERTAINTY ELEMENTS

NEURO FUZZY MODELLING AND CONTROL OF MULTISTAGE DYNAMIC PROCESSES THAT DEPEND ON INPUTS WITH UNCERTAINTY ELEMENTS

In regard to this an explorative statistical analysis model is used here in this paper that assesses two proposed metrics called resource optimal utilization ropt and coupling between [r]

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NEURO FUZZY MODELLING AND CONTROL OF MULTISTAGE DYNAMIC PROCESSES THAT DEPEND ON 
INPUTS WITH UNCERTAINTY ELEMENTS

NEURO FUZZY MODELLING AND CONTROL OF MULTISTAGE DYNAMIC PROCESSES THAT DEPEND ON INPUTS WITH UNCERTAINTY ELEMENTS

Economic Indicators Of The Manufacturing Industry Of Kazakhstan In The Years 1998-2010 With Additional Calculations Using The Cobb-Douglas Production Function With Α+Β=1 And With Taking [r]

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NEURO FUZZY MODELLING AND CONTROL OF MULTISTAGE DYNAMIC PROCESSES THAT DEPEND ON 
INPUTS WITH UNCERTAINTY ELEMENTS

NEURO FUZZY MODELLING AND CONTROL OF MULTISTAGE DYNAMIC PROCESSES THAT DEPEND ON INPUTS WITH UNCERTAINTY ELEMENTS

Several methods have been used to solve job-shop scheduling problems and the method proposed here is artificial intelligence by using the artificial immune system algorithm AIS.. The adv[r]

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