Performance Analysis of a Neuro-PID Controller Applied to a Robot Manipulator

Pezeshki, Saeed; Badalkhani, Sajad; Javadi, Ali

doi:10.5772/51280

Cited by 8 publications

(5 citation statements)

References 30 publications

(25 reference statements)

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“…The values of the physical parameters of the manipulator are presented in Table 2 (Korayem et al, 2010;Pezeshki et al, 2012).…”

Section: Dynamic Model Of 3-dof Robotic Manipulatormentioning

confidence: 99%

“…where, τ is the actuator's torque vector,MðqÞis nxn symmetric and positively defined inertia matrix, i:eMðqÞ ¼ MðqÞ T >0, BðqÞ is coriolis torque matrix, and CðqÞ is centrifugal torque matrix, and GðqÞ is the force of gravity, and is an n × 1 matrix, qis an n × 1 vector, ½ _ q; _ q�is the vector of joint velocity and ½ _ q� 2 is the derived vector given by: Pezeshki et al, 2012). In order to obtain the dynamic equations that reflect the reality of the physical system, it is essential to model (at least approximately) the forces of friction (Craig, 2009).…”

Section: Dynamic Model Of 3-dof Robotic Manipulatormentioning

confidence: 99%

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Modeling and control of a 3-DOF articulated robotic manipulator using self-tuning fuzzy sliding mode controller

2021

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Robotic manipulators are highly coupled multi-input multi-output (MIMO) nonlinear systems with uncertainties and highly time-varying dynamic capabilities. These characteristics make the trajectory control of a robotic manipulator very challenging. This paper presents the modeling and trajectory tracking control of a 3-DOF robotic manipulator using self-tuning fuzzy sliding mode controller (ST-FSMC). The stability of the system was investigated using the Lyapunov direct method. The controller was implemented using MATLAB/Simulink and its performance was evaluated. The simulation results show that the proposed controller removed chattering phenomena from the control effort and reduced the tracking error (average steady-state error) to 0.0036 rad. However, in the case of the conventional controllers, the average steady-state error increased to 0.0413 rad, 0.0044 rad, and 0.0053 rad for the Proportional-integral-derivative (PID) controller, Sliding mode controller (SMC), and Fuzzy sliding mode controller (FSMC), respectively. The conventional controllers (PID, SMC, and FSMC) were designed for the Aderajew Ashagrie

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“…The values of the physical parameters of the manipulator are presented in Table 2 (Korayem et al, 2010;Pezeshki et al, 2012).…”

Section: Dynamic Model Of 3-dof Robotic Manipulatormentioning

confidence: 99%

Section: Dynamic Model Of 3-dof Robotic Manipulatormentioning

confidence: 99%

Modeling and control of a 3-DOF articulated robotic manipulator using self-tuning fuzzy sliding mode controller

2021

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“…Neural network (NN) proportional-integral-derivative (PID) control is a typical MFMC strategy that combines NN technology and traditional PID control [15]. By training the NN model, NN PID control can adaptively adjust the parameters of the controller to accommodate different system dynamics and control requirements [16]. To be specific, the network learns to mimic the behavior of a PID controller, with different neurons contributing to the proportional, integral, and differential aspects of the control action.…”

Section: Introductionmentioning

confidence: 99%

Improving Model-Free Control Algorithms Based on Data-Driven and Model-Driven Approaches: A Research Study

Guo,

Yang

2023

Mathematics

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Given the challenges associated with accurately modeling complex nonlinear systems with time delays in industrial processes, this paper introduces an advanced model-free control algorithm that combines data-driven and model-driven approaches. Initially, an enhanced algorithm for multi-innovation model-free control, incorporating error feedback, is presented based on the error feedback principle. Subsequently, a novel control strategy is introduced by delving into PID neural network (NN) recognition and control theory, merging PID NN control with multi-innovation feedback control. Through meticulous mathematical derivation, the proposed strategy is proven to ensure system stability. Compared with traditional NN PID controllers, the convergence rate of the proposed scheme is 50 s faster and the steady-state errors are limited to ±1.

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“…Therefore, linear control methods alone are not usually sufficient for ensuring the desired performance in terms of the power output and self-starting capability. Some studies [21,22] have attempted to combine neural networks with conventional controllers, such as PI or PID, to develop a stable and robust control system for handling nonlinear system dynamics.…”

Section: Introductionmentioning

confidence: 99%

Design and Implementation of an Intelligent Blade Pitch Control System and Stability Analysis for a Small Darrieus Vertical-Axis Wind Turbine

et al. 2021

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A few studies have been conducted recently in order to improve the aerodynamic performance of Darrieus vertical-axis wind turbines with straight blades (H-type VAWTs). The blade pitch angle control is proposed to enhance the performance of H-type VAWTs. This paper aims to investigate the performance of an H-type VAWT in terms of its power output and self-starting capability using an intelligent blade pitch control strategy based on a multi-layer perceptron artificial neural network (MLP-ANN) method. The performance of the proposed blade pitch controller is investigated by adding a conventional controller (PID) to the MLP-ANN controller (i.e., a hybrid controller). The dynamics of an H-type VAWT is mathematically modeled in a nonlinear state space for the stability analysis in the sense of Lyapunov. The effectiveness of the proposed pitch control system is validated by building an H-type VAWT prototype model that is extensively tested outdoors under different conditions for both fixed and variable pitch angle configurations. Results demonstrated that the blade-pitching technique enhanced the power output of an H-type VAWT by approximately 22%. The hybrid controller that used a high percentage of the MLP-ANN controller achieved a better control performance by reducing the overshoot of the control response at high rotor speeds.

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Performance Analysis of a Neuro-PID Controller Applied to a Robot Manipulator

Cited by 8 publications

References 30 publications

Modeling and control of a 3-DOF articulated robotic manipulator using self-tuning fuzzy sliding mode controller

Modeling and control of a 3-DOF articulated robotic manipulator using self-tuning fuzzy sliding mode controller

Improving Model-Free Control Algorithms Based on Data-Driven and Model-Driven Approaches: A Research Study

Design and Implementation of an Intelligent Blade Pitch Control System and Stability Analysis for a Small Darrieus Vertical-Axis Wind Turbine

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