Development of a coaxial self-balancing robot based on sliding mode control

Dai, Fuquan; Li, Fangxing; Bai, Yuchuan; Guo, Wenzeng; Zong, Cheng; Gao, Xueshan

doi:10.1109/icma.2012.6283528

Cited by 8 publications

(4 citation statements)

References 13 publications

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“…S. C. Lin et.al [27] introduced the balanced two-wheel robot as a vehicle and used the adaptive sliding mode control method to maintain balance and control its angle around the transverse axis in the presence of uncertainties. F. Dai et.al [28] proposed a solution for maintaining balance and accurate tracking control of a balanced two-wheel robot with a small center of mass using sliding mode control. A. Filipescu et.al [29] introduced a discrete-time sliding mode controller to track the optimal path of a balanced two-wheel robot.…”

Section: Review Of the Previous Articlesmentioning

confidence: 99%

“…Theorem 2. In the subsystem (28), if u2(t) will be chosen as follows, the tracking errors e5 and e6 converge to zero asymptotically with any initial conditions.…”

Section: Design the First Smc For The Full-excited Subsystemmentioning

confidence: 99%

“…Note 4. Since a sliding surface is defined for the full-excited subsystem (28), therefore, to prove theorem 2, there is no need to define the variable z anymore, and for this reason, proving theorem 2 is easier than theorem 1.…”

Section: Design the First Smc For The Full-excited Subsystemmentioning

confidence: 99%

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Robust Sliding Mode Control for Two-Wheel Robot Without Kinematic Equations

Mousaei

Gheisarnejad

Khooban

2023

Preprint

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This article proposes solutions to control the balance of a two-wheel robotic system in the presence of uncertainties in the dynamic equations and without the need for kinematic equations. To do this, the dynamic equations of this system are first transferred to the error area and then these equations are divided into two completely independent subsystems, under excitation, and full excitation. In the following, two completely different sliding mode controllers are presented to control the under-excitation subsystem, which can make this subsystem asymptotically stable in the presence of structural and non-structural uncertainties. After that, a sliding mode control is proposed to control the entire excitation subsystem, making this subsystem asymptotically stable in the presence of existing uncertainties. Since these two subsystems are completely independent of each other, proving their global asymptotic stability provides proof of the global asymptotic stability of the closed-loop system. The isolation of two-wheel balancing robot subsystems eliminates the need to use kinematic equations, resulting in structural uncertainties not affecting the tracking accuracy of closed-loop system state variables. Finally, to verify the performance of the proposed controllers and compare their performance results, three-stage simulations are implemented on the two-wheel Balance robotic system. Mathematical proofs and simulation results show the optimal performance of the proposed solutions.

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Section: Review Of the Previous Articlesmentioning

confidence: 99%

“…Theorem 2. In the subsystem (28), if u2(t) will be chosen as follows, the tracking errors e5 and e6 converge to zero asymptotically with any initial conditions.…”

Section: Design the First Smc For The Full-excited Subsystemmentioning

confidence: 99%

See 1 more Smart Citation

Robust Sliding Mode Control for Two-Wheel Robot Without Kinematic Equations

Mousaei

Gheisarnejad

Khooban

2023

Preprint

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“…Different nonlinear controllers (optimal/adaptive) approaches have been suggested to control the TWSB robotic system like sliding mode controllers [12][13][14], adaptive robust backstepping controller [15], backstepping controller is also combined with PID controller as in [16].…”

Section: Introductionmentioning

confidence: 99%

Modified Integral Sliding Mode Controller Design based Neural Network and Optimization Algorithms for Two Wheeled Self Balancing Robot

Karam¹,

Mjeed²

2018

IJMECS

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Two-wheeled Self-balancing (TWSB) mobile robot is considered to be highly nonlinear and unstable dynamic system. Unstable means that the robot is free to advance forward or backward without any forces applied. It must, therefore, be controlled. The purpose of this work is to design an intelligent nonlinear Modified Integral Sliding Mode Controller (MISMC) based on simple Adaline neural network for balancing a two-wheeled selfbalancing mobile robot, in addition to improve the performance of this robot in tracking the desired trajectory. The simple Adaline neural network is used to enhance the performance of the conventional Integral Sliding Mode Controller (ISMC) which is an effective and powerful technique because it has a high performance. Also, in this work, a Modified Particle Swarm Optimization (MPSO) and Modified Cuckoo Search (MCS) algorithms have been proposed to find and tune the best MISMC parameters and hence enhance the performance characteristics of the robot system by reducing the processing time as well as improving the response accuracy through minimizing the tracking error of the mobile robot. The Integral Square Error (ISE) method has been used as a performance index for the two algorithms (MPSO, MCS) to measure the performance of the proposed controller. Numerical simulations show the efficiency of the suggested controller by handling the balance and tracking problems of the two-wheeled selfbalancing mobile robot. Index Terms-Modified Integral sliding mode controller, Two-wheeled self-balancing mobile robot, modified PSO, modified CS, optimization algorithms, neural network.

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Adaptive sliding mode attitude control of two-wheel mobile robot with an integrated learning-based RBFNN approach

Pang

Liu

et al. 2022

Neural Comput & Applic

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Development of a coaxial self-balancing robot based on sliding mode control

Cited by 8 publications

References 13 publications

Robust Sliding Mode Control for Two-Wheel Robot Without Kinematic Equations

Robust Sliding Mode Control for Two-Wheel Robot Without Kinematic Equations

Modified Integral Sliding Mode Controller Design based Neural Network and Optimization Algorithms for Two Wheeled Self Balancing Robot

Adaptive sliding mode attitude control of two-wheel mobile robot with an integrated learning-based RBFNN approach

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