Adaptive Backstepping Hierarchical Sliding Mode Control for 3-Wheeled Mobile Robots Based on RBF Neural Networks

Dang, Son Tung; Dinh, Xuan Minh; Kim, Thai Dinh; Xuan, Hai Le; Ha, Manh-Hung

doi:10.3390/electronics12112345

Cited by 14 publications

(3 citation statements)

References 31 publications

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“…Moreover, in the broader field of robot control, advanced control methods such as Model Predictive Control (MPC) [7,8], Reinforcement learning [9,10], Deep learning [11,12], or Deep reinforcement learning [12,13] have been employed. Some studies have explored an adaptive control approach using Radial Basis Function Neural Network (RBF) with online learning capability to estimate unknown nonlinear components of the model, enhancing control performance [14,15]. In studies on intelligent control methods [16], the use of Recurrent Fuzzy Wavelet Neural Network (RFWNN) has demonstrated powerful learning capabilities.…”

Section: Engineeringmentioning

confidence: 99%

Adaptive fuzzy-neural network effectively disturbance compensate in sliding mode control for dual arm robot

Nguyen,

Bui,

Nguyen

et al. 2024

Eureka: PE

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In this study, an Adaptive Backstepping Sliding Mode Controller (ABSMC) is introduced based on the Radial Basis Function (RBF) neural network and a fuzzy logic modifier. The proposed method is used to control a Dual-Arm Robot (DAR) – a nonlinear structure with unstable parameters and external disturbances. The control aims to track the motion trajectory of both arms in the flat surface coordinate within a short time, maintaining stability, and ensuring that the tracking error converges in finite time, especially when influenced by unforeseen external disturbances. The nonlinear Backstepping Sliding Mode Control (BSMC) is effective in trajectory tracking control; however, undesired phenomena may occur if there are uncertain disturbances affecting the system or model parameters change. It is proposed to use a neural network to estimate a nonlinear function to handle unknown uncertainties of the system. The neural network parameters can be adaptively adjusted to optimal values through adaptation rules derived from Lyapunov's theorem. Additionally, fuzzy logic theory is also employed to adjust the controller parameters to accommodate changes or unexpected impacts. The performance of the Fuzzy Neural Network Backstepping Sliding Mode Control (FNN-BSMC) is evaluated through simulation results using Matlab/Simulink software. Two simulation cases are conducted: the first case assumes stable model parameters without uncertain disturbances affecting the joints, while the second case considers a model with changing parameters and disturbances. Simulation results demonstrate the effective adaptability of the proposed method when the system model is affected by various types of uncertainties from the environment

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Section: Engineeringmentioning

confidence: 99%

Adaptive fuzzy-neural network effectively disturbance compensate in sliding mode control for dual arm robot

Nguyen,

Bui,

Nguyen

et al. 2024

Eureka: PE

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“…However, these control strategies effectively handle self-propelled robots in ideal working conditions without external disturbances. Some studies [12]- [15] have introduced uncertainty parameters into the dynamic equations of autonomous robots, leading to the utilization of hybrid Bulletin of Electr Eng & Inf ISSN: 2302-9285  adaptive control methods that merge adaptive control with neural network approximation of unknown components [16]. Another research direction combines adaptive management with fuzzy logic control [17].…”

Section: Introductionmentioning

confidence: 99%

Trajectory tracking control based on genetic algorithm and proportional integral derivative controller for two-wheel mobile robot

Ha,

Thi Thuong,

Ngoc Truc

2024

Bulletin EEI

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This paper uses the genetic algorithm (GA) to optimize the proportional integral derivative (PID) controller parameters to present the motion control design for a two-wheeled mobile robot autonomous system. The GA algorithm determines a collision-free travel curve for a robot with a tangential velocity restriction constraint. A trajectory-tracking controller based on the PID control structure is developed to monitor the calculated route curves for the mobile robot. Simulation results show the effectiveness of the GA-PID controller compared to the PID controller. The GA-PID controller demonstrates improved performance in trajectory tracking and collision avoidance, making it suitable for controlling the motion of two-wheeled mobile robots. The GA's optimization process allows for better tuning of the PID controller parameters, resulting in more efficient and accurate robot motion control. The results suggest that the proposed GA-PID controller is a promising approach for enhancing mobile robots' autonomous navigation capabilities.

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“…Finally, in the case of dynamic constraints, a quadratic programming method is used to optimize the tracking path to improve the smoothness. Dang [28] proposed an Adaptive Back-stepping Hierarchical Sliding Mode Control (ABHSMC) scheme for three-wheeled mobile robots (3WMRs) based on RBF neural networks. By aggregating all uncertain components in specific vectors and estimating using an RBF neural network, the effect of uncertainties will be minimized.…”

Section: Introductionmentioning

confidence: 99%

Global Path Planning for Differential Drive Mobile Robots Based on Improved BSGA* Algorithm

Yao,

Deng,

Feng

et al. 2023

Applied Sciences

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The global path planner is an important part of the navigation system for autonomous differential drive mobile robots (DDMRs). Aiming at the problems such as long calculation time, large number of search nodes, and poor smoothness of path when A* is applied to global path planning, this study proposes an improved bidirectional search Gaussian-A* (BSGA*) algorithm. First, the Gaussian function is introduced to realize the dynamic weighting of the heuristic function, which reduces the calculation time. Secondly, the bidirectional search (BS) structure is adopted to solve the problem of nodes’ repeated search when there are large obstacles between the starting point and the target point. Finally, a multi-layer turning point filter strategy is proposed to further smooth the path. In order to verify the performance of the improved BSGA* algorithm, experiments are carried out in simulation environments with the size of 15 × 15 and 30 × 30, respectively, and compared with the five common global path planning algorithms including ant colony optimization (ACO), D* lite algorithm, and genetic algorithm (GA). The results show that the improved BSGA* algorithm has the lowest calculation time and generates the shortest and smoothest path in the same environment. Finally, the program of the improved BSGA* algorithm is embedded into the LEO ROS mobile robot and two different real environments were built for experimental verification. By comparing with the A* algorithm, Dijkstra algorithm, ACO, D* lite algorithm, and GA, the results show that the improved BSGA* algorithm not only outperforms the above five algorithms in terms of calculation time, length, and total turning angle of the generated paths, but also consumes the least time when DDMR drives along the generated paths.

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Adaptive Backstepping Hierarchical Sliding Mode Control for 3-Wheeled Mobile Robots Based on RBF Neural Networks

Cited by 14 publications

References 31 publications

Adaptive fuzzy-neural network effectively disturbance compensate in sliding mode control for dual arm robot

Adaptive fuzzy-neural network effectively disturbance compensate in sliding mode control for dual arm robot

Trajectory tracking control based on genetic algorithm and proportional integral derivative controller for two-wheel mobile robot

Global Path Planning for Differential Drive Mobile Robots Based on Improved BSGA* Algorithm

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