Neural hybrid control of manipulators, stability analysis

Saadia, Nadia; Pontnau, J.; M'Sirdi, Kouider

doi:10.1017/s0263574700002885

Cited by 9 publications

(6 citation statements)

References 18 publications

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“…This algorithm utilizes position, velocity and acceleration errors or force error for learning the command input required to perform tasks. It guarantees the convergence of both position and force tracking errors, as well as robustness, for sufficiently small parameter uncertainties and disturbances [7].…”

Section: Introductionmentioning

confidence: 94%

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Position with Force Feedback Control of Manipulator Arm

Rajeswari¹,

Chitra²,

Gopal³

2018

IJERT

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Abstract-This paper deals with the modeling and simulation of position with force feedback control of manipulator arm. The manipulator is used to collect the potentially hazardous samples and store it. Each joint in the manipulator is actuated by electromechanical actuator. In the proposed work the Electromechanical actuator consists of Permanent Magnet Synchronous Motor (PMSM) with gear box and its accessories. The position control of the manipulator is executed through PMSM motor which is controlled using the vector control technique with the force control loop being added as the outer loop. The entire process is executed using MATLAB SIMULINK software. Simulation results show that the PMSM drive nearly catches the reference signal and we can get the desired speed and position variation according to the reference command. In case of force control some deviations are observed which is due to the environment stiffness parameter.

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Section: Introductionmentioning

confidence: 94%

“…Learning algorithm is applied to the hybrid position/force control when the robot performs the same task repeatedly [7]. This algorithm utilizes position, velocity and acceleration errors or force error for learning the command input required to perform tasks.…”

Section: Introductionmentioning

confidence: 99%

Position with Force Feedback Control of Manipulator Arm

Rajeswari¹,

Chitra²,

Gopal³

2018

IJERT

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“…Both the stability of the system and the convergence of the controller are guaranteed when oV ot 0 [48]. Thus, the derivative of Lyapunov function ( oV ot ) is acquired as …”

Section: Stability Analysis For the Generalized Str Based On Svrmentioning

confidence: 99%

Generalized self-tuning regulator based on online support vector regression

Uçak

Günel

2016

Neural Comput & Applic

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This paper introduces a novel generalized selftuning regulator based on online support vector regression (OSVR) for nonlinear systems. The main idea is to approximate the parameters of an adaptive controller by optimizing the regression margin between reference input and system output. For this purpose, ''closed-loop margin'' which depends on tracking error is defined, and the parameters of the adaptive controller are optimized so as to minimize the tracking error which leads simultaneously to the optimization of the closed-loop margin. The overall architecture consists of an online SVR which computes a forward model of the system, an adaptive controller with tunable parameters and an adaptation mechanism realized by separate online SVRs to estimate each tunable controller parameter. The proposed architecture is implemented with adaptive proportional-integral-derivative (PID) and adaptive fuzzy PID in the controller block. The performance of the generalized self-tuning regulator mechanism has been examined via simulations performed on a bioreactor benchmark system, and the results show that the generalized adaptive controller and OSVR model attain good control and modeling performances.

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“…The stability of the neural network in relation with the positive learning rate, , is explored by Saadia et al (2001). Considering Figure 2 and the analysis given in the study mentioned above (Saadia et al, 2001), the following positive definite Lyapunov function is defined, where P is the identity matrix:…”

Section: Stability Of the Neural Networkmentioning

confidence: 99%

“…Considering Figure 2 and the analysis given in the study mentioned above (Saadia et al, 2001), the following positive definite Lyapunov function is defined, where P is the identity matrix:…”

Section: Stability Of the Neural Networkmentioning

confidence: 99%

End-effector Trajectory Control in a Two-Link Flexible Manipulator Through Reference Joint Angle Values Modification by Neural Networks

Öke

Istefanopulos²

2006

Journal of Vibration and Control

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The basic difficulty in the control of flexible link manipulators stems from the fact that the link deflections cannot be controlled directly. Since the number of control inputs, applied by the actuators, is less than the total number of variables to be controlled, control approaches aiming at the suppression of deflections and vibrations are generally insufficient. Another possible approach is to determine new joint trajectories to minimize the error of the end-effector in the operational space. In this paper, a neural network is designed to compute incremental changes for the reference values of the joint angles to achieve successful tip tracking in the operational space. Tip position errors in the x-and y-directions are utilized as inputs to the neural network. The cost function, which is minimized in training the neural network, is also chosen as the sum of squares of the tip position error in both directions. Joint angle control is provided by a PD controller. Simulations are carried out to evaluate the performance of the neural-network-based trajectory tracking method, and the results are depicted in both joint and operational spaces.

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Neural hybrid control of manipulators, stability analysis

Cited by 9 publications

References 18 publications

Position with Force Feedback Control of Manipulator Arm

Position with Force Feedback Control of Manipulator Arm

Generalized self-tuning regulator based on online support vector regression

End-effector Trajectory Control in a Two-Link Flexible Manipulator Through Reference Joint Angle Values Modification by Neural Networks

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