Adaptive Robust Controller Design-Based RBF Neural Network for Aerial Robot Arm Model

Al-Darraji, Izzat; Piromalis, Dimitrios; Kakei, Ayad; Khan, Fazal Qudus; Stojmenović, Miloš; Tsaramirsis, Georgios; Papageorgas, Panagiotis

doi:10.3390/electronics10070831

Cited by 32 publications

(18 citation statements)

References 37 publications

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“…In this section, the state and output equations of the robot arm model are developed. Selection of the appropriate components [23] and modeling is the important step that should be implemented before any development [24,25]. Firstly, equations of motion of the robot arm shown in Fig.…”

Section: Development Of State Space Modelmentioning

confidence: 99%

Takagi–Sugeno Fuzzy Modeling and Control for Effective Robotic Manipulator Motion

Malhotra¹,

Bala²

2022

Computers, Materials &Amp; Continua

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Robotic manipulators are widely used in applications that require fast and precise motion. Such devices, however, are prompt to nonlinear control issues due to the flexibility in joints and the friction in the motors within the dynamics of their rigid part. To address these issues, the Linear Matrix Inequalities (LMIs) and Parallel Distributed Compensation (PDC) approaches are implemented in the Takagy-Sugeno Fuzzy Model (T-SFM). We propose the following methodology; initially, the state space equations of the nonlinear manipulator model are derived. Next, a Takagy-Sugeno Fuzzy Model (T-SFM) technique is used for linearizing the state space equations of the nonlinear manipulator. The T-SFM controller is developed using the Parallel Distributed Compensation (PDC) method. The prime concept of the designed controller is to compensate for all the fuzzy rules. Furthermore, the Linear Matrix Inequalities (LMIs) are applied to generate adequate cases to ensure stability and control. Convex programming methods are applied to solve the developed LMIs problems. Simulations developed for the proposed model show that the proposed controller stabilized the system with zero tracking error in less than 1.5 s.

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Section: Development Of State Space Modelmentioning

confidence: 99%

Takagi–Sugeno Fuzzy Modeling and Control for Effective Robotic Manipulator Motion

Malhotra¹,

Bala²

2022

Computers, Materials &Amp; Continua

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“…Ayeb et al [32] designed an adaptive sliding mode controller based on an RBF neural network to improve the trajectory tracking performance of nonholonomic mobile robots and to avoid jitters. Al-Darraji et al [33] designed an adaptive robust controller based on an RBF neural network, which takes into account high nonlinearity, high modeling errors, and the interference caused by payload and environmental conditions. It was able to combat effectively the nonlinear and uncertain problems of aerial robot arms.…”

Section: Related Workmentioning

confidence: 99%

Trajectory Planning of Robot Manipulator Based on RBF Neural Network

Song

Bai

et al. 2021

Entropy

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Robot manipulator trajectory planning is one of the core robot technologies, and the design of controllers can improve the trajectory accuracy of manipulators. However, most of the controllers designed at this stage have not been able to effectively solve the nonlinearity and uncertainty problems of the high degree of freedom manipulators. In order to overcome these problems and improve the trajectory performance of the high degree of freedom manipulators, a manipulator trajectory planning method based on a radial basis function (RBF) neural network is proposed in this work. Firstly, a 6-DOF robot experimental platform was designed and built. Secondly, the overall manipulator trajectory planning framework was designed, which included manipulator kinematics and dynamics and a quintic polynomial interpolation algorithm. Then, an adaptive robust controller based on an RBF neural network was designed to deal with the nonlinearity and uncertainty problems, and Lyapunov theory was used to ensure the stability of the manipulator control system and the convergence of the tracking error. Finally, to test the method, a simulation and experiment were carried out. The simulation results showed that the proposed method improved the response and tracking performance to a certain extent, reduced the adjustment time and chattering, and ensured the smooth operation of the manipulator in the course of trajectory planning. The experimental results verified the effectiveness and feasibility of the method proposed in this paper.

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“…With the development of intelligent manufacturing, robot manipulators [1][2][3][4] have been widely used in various industry fields, such as spraying [5], welding [6], transport [7], and assembly [8]. Traditional nonredundant robot manipulators could not complete a precise operation with high flexibility requirements due to their freedom limitation.…”

Section: Introductionmentioning

confidence: 99%

Novel Voltage-Based Weighted Hybrid Force/Position Control for Redundant Robot Manipulators

2022

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Existing hybrid force/position control algorithms mostly explicitly contain a dynamic model. Moreover, force and position controllers will be switched frequently. To solve the above problems, a novel voltage-based weighted hybrid force/position control algorithm is proposed for redundant robot manipulators. Firstly, mapping between voltage and terminal position and orientation is established so that the designed controller can be simplified by adopting the motor current as the feedback to replace the tedious calculation of the dynamic model. Secondly, a voltage-based weighted hybrid force/position control algorithm is proposed to eliminate the selection matrix. Force and position control laws are summed directly through a weighted way to avoid the problems of space decomposition and switching. Thirdly, the stability is proven using Lyapunov stability theory, then the selection method for weighted coefficient is provided. Fourthly, comparative simulations are performed. Results show that the proposed algorithm is suitable for impedance control and hybrid force/position control and can compensate for their deficiencies. Lastly, the transport experiment in the YZ plane is conducted. Results show that position and force accuracies in the Y- and Z-axis directions are 3.489 × 10−4 and 7.313 × 10−4 m and 1.238 × 10−1 and 1.997 × 10−1 N, respectively. Accordingly, it can effectively improve the operation capability and control accuracy.

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Adaptive Robust Controller Design-Based RBF Neural Network for Aerial Robot Arm Model

Cited by 32 publications

References 37 publications

Takagi–Sugeno Fuzzy Modeling and Control for Effective Robotic Manipulator Motion

Takagi–Sugeno Fuzzy Modeling and Control for Effective Robotic Manipulator Motion

Trajectory Planning of Robot Manipulator Based on RBF Neural Network

Novel Voltage-Based Weighted Hybrid Force/Position Control for Redundant Robot Manipulators

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