New hybrid adaptive neuro-fuzzy algorithms for manipulator control with uncertainties–Comparative study

Alavandar, Srinivasan; Nigam, M. J.

doi:10.1016/j.isatra.2009.05.003

Cited by 23 publications

(9 citation statements)

References 23 publications

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“…The control scheme adopted in this study is the computed torque control scheme which has been used widely, in industrial application, for robot manipulator control [4,[12][13][14][15]. It is based on the concept of inner and outer-loop control strategy which provide feedback linearization of the dynamic equation given in (10).…”

Section: B Pid-computed Torque Controlmentioning

confidence: 99%

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Intelligent trajectory conversion and inverse dynamic control of a 3-DOF neuro-rehabilitation platform

Sado

Sidek

Yusuf

2015

2015 10th Asian Control Conference (ASCC)

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Tracking a desired trajectory in joint space has been favored in several robot manipulators and end-effector control scheme due to the simplicity and high sampling rate offered by the joint space scheme. This usually require the trajectory conversion process, of the desired position, velocity, and acceleration, from Cartesian space to joint space using conventional inverse kinematics solutions which have been known to have several limitations and which often pose a big challenge, computationally, and even prohibitive, to achieve, for some robot designs. In this study, an intelligent approach to the inverse kinematics problem using adaptive neuro-fuzzy inference system (ANFIS) is proposed for control of a 3-DOF end-effector based neurorehabilitation platform. The joint positions, velocities, andaccelerations are achieved/predicted by means of the ANFIS networks which is trained with data obtained from the forward kinematics, velocity Jacobian and the differential of the velocity kinematics equations. Simulation studies have shown that the proposed intelligent techniques has simplified both the trajectory conversion process and the control framework while tracking is achieve to a high degree of accuracy.

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Section: B Pid-computed Torque Controlmentioning

confidence: 99%

“…Suppose that the end-effector is required to track a desired trajectory, defined in joint space with a tracking error given as (11) finding the second derivative of (11) gives (12) Solving for in (10) and substituting into (12) yields .…”

Section: B Pid-computed Torque Controlmentioning

confidence: 99%

Intelligent trajectory conversion and inverse dynamic control of a 3-DOF neuro-rehabilitation platform

Sado

Sidek

Yusuf

2015

2015 10th Asian Control Conference (ASCC)

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“…In non linear systems like manipulators the design and choice of the controllers is very much affected by the presence of uncertainties. The uncertainties can be classified into two categories: Structured uncertainties are characterized by model imprecision of link parameters, payload variations, inaccuracies of torque constants of actuators etc, whereas unstructured uncertainties are the ones produced by unmodelled dynamics, such as non-linear friction, external disturbances etc [5,6]. In all the practical cases, presence of these uncertainties makes the dynamic model of a robotic manipulator known partially and hence highlights the need of robust controllers.…”

Section: Introductionmentioning

confidence: 99%

Sliding Mode Control (SMC) of Robot Manipulator via Intelligent Controllers

Kapoor

Ohri

2016

J. Inst. Eng. India Ser. B

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Inspite of so much research, key technical problem, naming chattering of conventional, simple and robust SMC is still a challenge to the researchers and hence limits its practical application. However, newly developed soft computing based techniques can provide solution. In order to have advantages of conventional and heuristic soft computing based control techniques, in this paper various commonly used intelligent techniques, neural network, fuzzy logic and adaptive neuro fuzzy inference system (ANFIS) have been combined with sliding mode controller (SMC). For validation, proposed hybrid control schemes have been implemented for tracking a predefined trajectory by robotic manipulator, incorporating structured and unstructured uncertainties in the system. After reviewing numerous papers, all the commonly occurring uncertainties like continuous disturbance, uniform random white noise, static friction like coulomb friction and viscous friction, dynamic friction like Dhal friction and LuGre friction have been inserted in the system. Various performance indices like norm of tracking error, chattering in control input, norm of input torque, disturbance rejection, chattering rejection have been used. Comparative results show that with almost eliminated chattering the intelligent SMC controllers are found to be more efficient over simple SMC. It has also been observed from results that ANFIS based controller has the best tracking performance with the reduced burden on the system. No paper in the literature has found to have all these structured and unstructured uncertainties together for motion control of robotic manipulator.Keywords Non-linear system Á Sliding mode control (SMC) Á Heuristic intelligent controllers Á Neural network (NN) Á Fuzzy logic (FL) and adaptive neuro fuzzy inference system (ANFIS)

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“…To build and optimize the fuzzy models [24] proposed a HYFIS( Hybrid neural fuzzy Inference system), which inculcates the learning capabilities of neural networks with processing power of fuzzy logic. while considering the nonlinearities, uncertainties and external perturbations of an industrial robot manipulator, [25] proposed some new hybrid ANFIS (Adaptive neuro fuzzy inference system) control algorithms. The reference [26] applied ANFIS to address the inverse kinematic problem of determining a set of joint angles for a specific manipulator posture, while the robot's postures and trajectories were executed in Virtual Reality.…”

Section: Introductionmentioning

confidence: 99%

ANFIS PD+I Based Hybrid Force/ Position Control of an Industrial Robot Manipulator

Chaudhary¹,

Panwar²,

Sukavanam³

et al. 2014

IJMMM

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Abstract-A hybrid force plus position controller based on adaptive neuro fuzzy inference system proportional derivative + integral (ANFIS-PD+I), with unspecified robot dynamics has been proposed for a robot manipulator under constrained environment. The proposed controller has been employed as a principal controller to tune up orthodox PID gains throughout the complete trajectory tracking process. The validity of the proposed controller has been studied using a 6-Degree of Freedom (DOF) PUMA robot manipulator. Simulation outcomes illustrates that the projected force / position controller adheres to the desired path closer and smoother.Index Terms-Adaptive neuro fuzzy control, degrees of freedom, force control, position control, robot manipulator.

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New hybrid adaptive neuro-fuzzy algorithms for manipulator control with uncertainties–Comparative study

Cited by 23 publications

References 23 publications

Intelligent trajectory conversion and inverse dynamic control of a 3-DOF neuro-rehabilitation platform

Intelligent trajectory conversion and inverse dynamic control of a 3-DOF neuro-rehabilitation platform

Sliding Mode Control (SMC) of Robot Manipulator via Intelligent Controllers

ANFIS PD+I Based Hybrid Force/ Position Control of an Industrial Robot Manipulator

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