2-DOF robot manipulator control using fuzzy PD control with SimMechanics and sliding mode control: A comparative study

Naik, Prabhas Ranjan; Samantaray, Jagannath; Roy, Binoy Krishna; Pattanayak, Sujit Kumar

doi:10.1109/epetsg.2015.7510101

Cited by 11 publications

(9 citation statements)

References 13 publications

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“…Chattering phenomenon is one of the main challenges in this controller and it can cause some important mechanical problems such as saturation and heats. Figure 4 shows the chattering phenomenon [8][9]. where is the boundary layer thickness.…”

Section: Desihn Model-free Nonlinear Controllermentioning

confidence: 99%

Nonlinear Model-Free Control and ARX Modeling of Industrial Motor

Sarostad¹,

Piltan²,

Ashkezari³

et al. 2016

IJSH

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System identification is one of the main challenges in real time control. To design the best controller for linear or nonlinear systems, mathematical modeling is the main challenge. To solve this challenge conventional and intelligent identification are recommended. The second important challenge in the field of control theory is, design high-performance controller. To improve the performance of controller, two factors are very important: 1) high performance mathematical or intelligent modeling, 2) chose the best controller for the system. This paper has two main objectives: after data collection from position motor from industry the first objective is modeling and system identification based on Auto-Regressive with eXternal model input (ARX) and defined Z-function and Sfunction and the second objective is; design the high-performance controller to have the minimum rise time and error.

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Section: Desihn Model-free Nonlinear Controllermentioning

confidence: 99%

Nonlinear Model-Free Control and ARX Modeling of Industrial Motor

Sarostad¹,

Piltan²,

Ashkezari³

et al. 2016

IJSH

View full text Add to dashboard Cite

show abstract

“…In a trajectory tracking control system, the feed forward velocity data was added to the control system as the correcting data from the FLC in accordance with the postures errors. In [21], a comparison between fuzzy proportional derivative control (PD) and sliding mode control (SMC) models for controlling a 2-DOF robot manipulator has been performed.…”

Section: Introductionmentioning

confidence: 99%

A New Computed Torque Control System with an Uncertain RBF Neural Network Controller for a 7-DOF Robot

Wang

Zhou

2020

Teh. vjesn.

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A novel percutaneous puncture robot system is proposed in the paper. Increasing the surgical equipment precision to reduce the patient's pain and the doctor's operation difficulty to treat smaller tumors can increase the success rate of surgery. To attain this goal, an optimized Computed Torque Law (CTL) using a radial basis function (RBF) neural network controller (RCTL) is proposed to improve the direction and position accuracy. BRF neural network with an uncertain term (URBF) which is able to compensate the system error caused by the imprecision of the model is added in the RCTL system. At first, a 7-DOF robotic system is established. It consists of robotic arm and actuator control channels. Now, the RBF compensator is added to the CTL to adjust the robot arm to reduce the position and direction errors. The angle and velocity errors of the robot arm are compensated using the RBF controller. According to the Lyapunov theory, the accuracy of torque control system depends on path tracking errors, inertia of robot, dynamic parameters and disturbance of each joint. Compared to general CTL approaches, the precision of a 7-DOF robot could be improved by adjusting the RBF parameters.

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“…Sliding mode control (SMC) has been widely applied to robotic systems because of its robustness to unknown exogenous disturbances, parameter variations and model perturbations (Van et al, 2013;Esmaili and Haron, 2017;Naik et al, 2016). However, to achieve a satisfactory performance, the chattering phenomenon that is usually caused by system disturbance can probably damage the actuator of robotic control systems (Yang et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

Active disturbance rejection sliding mode control for robot manipulation

Mou

2020

JIMSE

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PurposeIn recent years, owing to the rapidly increasing labor costs, the demand for robots in daily services and industrial operations has been increased significantly. For further applications and human–robot interaction in an unstructured open environment, fast and accurate tracking and strong disturbance rejection ability are required. However, utilizing a conventional controller can make it difficult for the robot to meet these demands, and when a robot is required to perform at a high-speed and large range of motion, conventional controllers may not perform effectively or even lead to the instability.Design/methodology/approachThe main idea is to develop the control law by combining the SMC feedback with the ADRC control architecture to improve the robustness and control quality of a conventional SMC controller. The problem is formulated and solved in the framework of ADRC. For better estimation and control performance, a generalized proportional integral observer (GPIO) technique is employed to estimate and compensate for unmodeled dynamics and other unknown time-varying disturbances. And benefiting from the usage of GPIO, a new SMC law can be designed by synthesizing the estimation and its history.FindingsThe employed methodology introduced a significant improvement in handling the uncertainties of the system parameters without compromising the nominal system control quality and intuitiveness of the conventional ADRC design. First, the proposed method combines the advantages of the ADRC and SMC method, which achieved the best tracking performance among these controllers. Second, the proposed controller is sufficiently robust to various disturbances and results in smaller tracking errors. Third, the proposed control method is insensitive to control parameters which indicates a good application potential.Originality/valueHigh-performance robot tracking control is the basis for further robot applications in open environments and human–robot interfaces, which require high tracking accuracy and strong disturbance rejection. However, both the varied dynamics of the system and rapidly changing nonlinear coupling characteristic significantly increase the control difficulty. The proposed method gives a new replacement of PID controller in robot systems, which does not require an accurate dynamic system model, is insensitive to control parameters and can perform promisingly for response rapidity and steady-state accuracy, as well as in the presence of strong unknown disturbances.

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2-DOF robot manipulator control using fuzzy PD control with SimMechanics and sliding mode control: A comparative study

Cited by 11 publications

References 13 publications

Nonlinear Model-Free Control and ARX Modeling of Industrial Motor

Nonlinear Model-Free Control and ARX Modeling of Industrial Motor

A New Computed Torque Control System with an Uncertain RBF Neural Network Controller for a 7-DOF Robot

Active disturbance rejection sliding mode control for robot manipulation

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