Trajectory Tracking of a Flexible Robot Manipulator by a New Optimized Fuzzy Adaptive Sliding Mode-Based Feedback Linearization Controller

Mahmoodabadi, Mohammad Javad; Nejadkourki, N.

doi:10.1155/2020/8813217

Cited by 6 publications

(3 citation statements)

References 63 publications

(74 reference statements)

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“…In Reference [24], the adaptive fuzzy sliding mode controller is proposed to stabilise the attitude of the flexible satellite. In Reference [25], a fuzzy adaptive sliding mode-based feedback linearisation controller is proposed. A feedback linearisation approach is utilised to change the nonlinear dynamics to a linear dynamics; then, a sliding mode control strategy is implemented as a trajectory tracking controller.…”

Section: Type Of Robots Humanoid Robot Snake Robot Rehabilitation Robotmentioning

confidence: 99%

A Novel Adaptive Sliding Mode Controller for a 2-DOF Elastic Robotic Arm

2022

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Collaborative robots (or cobots) are robots that are capable of safely operating in a shared environment or interacting with humans. In recent years, cobots have become increasingly common. Compliant actuators are critical in the design of cobots. In real applications, this type of actuation system may be able to reduce the amount of damage caused by an unanticipated collision. As a result, elastic joints are expected to outperform stiff joints in complex situations. In this work, the control of a 2-DOF robot arm with elastic actuators is addressed by proposing a two-loop adaptive controller. For the outer control loop, an adaptive sliding mode controller (ASMC) is adopted to deal with uncertainties and disturbance on the load side of the robot arm. For the inner loops, model reference adaptive controllers (MRAC) are utilised to handle the uncertainties on the motor side of the robot arm. To show the effectiveness of the proposed controller, extensive simulation experiments and a comparison with the conventional sliding mode controller (SMC) are carried out. As a result, the ASMC has a 50.35% lower average RMS error than the SMC controller, and a shorter settling time (5% criterion) (0.44 s compared to 2.11 s).

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Section: Type Of Robots Humanoid Robot Snake Robot Rehabilitation Robotmentioning

confidence: 99%

A Novel Adaptive Sliding Mode Controller for a 2-DOF Elastic Robotic Arm

2022

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“…It starts from the initial population and selects the best individual in the current cluster based on the evaluation function values of all corresponding individuals. It uses intersection and mutation to generate new subclusters [32], which evolve the clusters into better regions to find the optimal solution. The main calculation process is as follows:…”

Section: Displacement Trajectory Optimization Of Picking Robotmentioning

confidence: 99%

Artificial Intelligence in Agricultural Picking Robot Displacement Trajectory Tracking Control Algorithm

Hua-ying

2022

Wireless Communications and Mobile Computing

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With the development and leap of artificial intelligence technology, more and more robots have penetrated into all walks of life. Today’s agriculture is changing in the direction of modernization and automation. On the one hand, because of rising labor costs, it cannot afford to consume a large amount of labor for agricultural operations. On the other hand, the population is growing rapidly, and traditional agricultural production, picking, and other links have been unable to keep pace with the development of the times. Therefore, it is very necessary to use artificial intelligence technology to transform traditional agriculture. The purpose of this paper is to use artificial intelligence technology to plan, track, and optimize the displacement trajectory of the agricultural picking robot, so as to improve the working efficiency of the picking robot. In this paper, the neural network, the D-H modeling method of the manipulator, and the forward and reverse motion of the manipulator are explained and analyzed, and based on the relevant algorithms of neural network, the manipulator is modeled, and then the, forward and reverse motion of the manipulator is analyzed in detail, and the digital model of the picking robot is constructed. Then, the angle and motion speed of each joint of the robot are analyzed to reduce the motion trajectory error caused by friction and other factors. Then, the simulation experiment of the displacement trajectory tracking control is carried out, and the linear trajectory motion and the arc trajectory motion are deeply analyzed, and the axis error is greatly reduced after 6 iterations. Finally, the displacement trajectory is optimized. The optimized total movement time is shortened by 6.84 seconds, which enables the picking robot to not only ensure work efficiency but also accurately complete the planned displacement trajectory. After continuous experiments on the algorithm model and the picking robot, the actual trajectory of the picking robot at 0.7 seconds can be expected. The trajectories are completely coincident, indicating that the neural network plays a very important role in the trajectory research of picking robots.

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“…For this reason, many studies have been conducted in this field by various methods to investigate tracking and stability, including controller designs based on the Lyapunov method [6], the adaptive control method [7,8], the sliding mode method [9][10][11][12], the feedback linearization method [13], robust control [14,15], the fuzzy method [16,17], the backtracking method [18], a combination of the fuzzy methods of neural networks [19,20], etc. In [21], an optimal proportional-derivative controller based on feedback linearization is designed for tracking the trajectory of a wheeled robot. In other research, the active observer control method based on the Kalman filter for the dynamic control of mobile robots is proposed [22].…”

Section: Introductionmentioning

confidence: 99%

Taylor Series-Based Fuzzy Model Predictive Control for Wheeled Robots

Yang¹,

Guo

Mohammadzadeh

et al. 2022

Mathematics

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In this paper, a new hybrid method for controlling a wheeled robot is introduced. Model predictive control (MPC) is the main controller and a fuzzy controller is used as a compensator. The wheeled robot is a nonlinear, multi-input–multi-output system that requires new and combined methods for precise control. In order to stabilize the system the appropriate control input is set, and at the same time, attention is paid to the reference signal tracking. In the simulation section, several different scenarios are applied and parameter uncertainties and their effects on the controller’s performance are evaluated. The simulation results show the success and efficiency of the proposed method.

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Trajectory Tracking of a Flexible Robot Manipulator by a New Optimized Fuzzy Adaptive Sliding Mode-Based Feedback Linearization Controller

Cited by 6 publications

References 63 publications

A Novel Adaptive Sliding Mode Controller for a 2-DOF Elastic Robotic Arm

A Novel Adaptive Sliding Mode Controller for a 2-DOF Elastic Robotic Arm

Artificial Intelligence in Agricultural Picking Robot Displacement Trajectory Tracking Control Algorithm

Taylor Series-Based Fuzzy Model Predictive Control for Wheeled Robots

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