The Robot Arm Control Based on RBF with Incremental PID and Sliding Mode Robustness

Guo, Xiqing; Li, Zongfeng; Sun, Guangbin

doi:10.1109/wrc-sara.2019.8931971

Cited by 7 publications

(3 citation statements)

References 12 publications

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“…The motor control algorithm utilizes an incremental PID control algorithm [12]. The incremental algorithm requires historical deviations, during the kth control cycle, the deviations input during the k-1th and k-2th control operations are used [13]. The calculation yields ∆𝑢 , as shown in (4), and the accumulation of these yields provides the PID output, as demonstrated in (5).…”

Section: Motor Control Algorithmmentioning

confidence: 99%

Enhancing 2D Path Search and PID Control for Autonomous Mobile Robots: A* Algorithm Optimization and Hardware

Zhou

2024

AETR

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Intelligent autonomous mobile robots find diverse applications across various fields. This article delves into the optimization of path search planning and implementation for these robots in specific scenarios. The study enhances the traditional right-hand principle's search strategy, integrating a forward-looking approach grounded in the goal-oriented law with A* algorithm path planning. Emphasizing the algorithm's lightweight and high efficiency, the study also considers its interaction with hardware systems. The real-world application scenario is abstracted into a two-dimensional planar environment. Collaborative efforts between hardware and software systems result in a comprehensive robot system, validated through tests ensuring swift navigation from the starting point to the endpoint along the planned path.

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Section: Motor Control Algorithmmentioning

confidence: 99%

Enhancing 2D Path Search and PID Control for Autonomous Mobile Robots: A* Algorithm Optimization and Hardware

Zhou

2024

AETR

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“…In addition, the method requires specialized knowledge and much experience to maintain the efficiency and stability of the system. Researchers [21][22] proposed a controller using a Radial Baseline Functional Neural Network (RBFNN). The main purpose of RBFNN is to approximate a non-linear function from the input to the corresponding output.…”

Section: Introductionmentioning

confidence: 99%

An Application of Modified T2FHC Algorithm in Two-Link Robot Controller

Ngo,

Tran,

et al. 2023

Journal of Robotics and Control

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Parallel robotic systems have shown their advantages over the traditional serial robots such as high payload capacity, high speed, and high precision. Their applications are widespread from transportation to manufacturing fields. Therefore, most of the recent studies in parallel robots focus on finding the best method to improve the system accuracy. Enhancing this metric, however, is still the biggest challenge in controlling a parallel robot owing to the complex mathematical model of the system. In this paper, we present a novel solution to this problem with a Type 2 Fuzzy Coherent Controller Network (T2FHC), which is composed of a Type 2 Cerebellar Model Coupling Controller (CMAC) with its fast convergence ability and a Brain Emotional Learning Controller (BELC) using the Lyaponov-based weight updating rule. In addition, the T2FHC is combined with a surface generator to increase the system flexibility. To evaluate its applicability in real life, the proposed controller was tested on a Quanser 2-DOF robot system in three case studies: no load, 180 g load and 360 g load, respectively. The results showed that the proposed structure achieved superior performance compared to those of available algorithms such as CMAC and Novel Self-Organizing Fuzzy CMAC (NSOF CMAC). The Root Mean Square Error (RMSE) index of the system that was 2.20E-06 for angle A and 2.26E-06 for angle B and the tracking error that was -6.42E-04 for angle A and 2.27E-04 for angle B demonstrate the good stability and high accuracy of the proposed T2FHC. With this outstanding achievement, the proposed method is promising to be applied to many applications using nonlinear systems.

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“…Fuzzy control and neural network control, which are based on the data model rather than the mathematical model, make their precision difficult to meet the high accuracy requirements of 3C industry. Moreover, [19] proposed a tracking control scheme with radial basis function (RBF) neural network, which combines the incremental PID control and sliding mode control. [20] used an adaptive inertia weight particle swarm optimization.…”

Section: Introductionmentioning

confidence: 99%