A robust control method of two-wheeled self-balancing robot

Wu, Jian; Liang, Yuxin; Wang, Zhe

doi:10.1109/ifost.2011.6021196

Cited by 11 publications

(4 citation statements)

References 7 publications

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“…Therefore, in case of uncertainty and external disturbances, the balance of the robot will be disturbed and the system will become unstable. To deal with existing uncertainties, C. H. Chiu [11], proposed an adaptive output recurrent cerebellar model controller, H. T. Yau et.al [12] propounded a robust control, S. C. Lin et.al [13] suggested a nonlinear adaptive sliding mode control, J. Wu et.al [14] presented a solution to control the two-wheel robot system using robust and adaptive control methods, and A. Wasif [15] et.al introduced a two-level adaptive control. Although the proposed solutions are successful in overcoming the existing uncertainties, their design steps are very complex and have a very high number of calculations.…”

Section: Review Of the Previous Articlesmentioning

confidence: 99%

“…A. Ghani et.al [25], and J. Wu et.al [26] used only this control method to maintain the balance of the two-wheel robot and did not propose a solution to track the desired path. Mathematical proof and simulation results show the performance of the proposed controllers, but the problem of maintaining balance limits the application of the balanced two-wheel robot in real-time.…”

Section: Review Of the Previous Articlesmentioning

confidence: 99%

“…Equation (26) shows the dynamic equations of this system can be divided into two subsystems: underexcitation and full-excitation. The equations of the under-excitation are as follows:…”

Section: Transferring the Dynamic Equations Of The State Space Of The...mentioning

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%

Section: Review Of the Previous Articlesmentioning

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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“…In Nghia et al, 6 adaptive control is designed using a network neural function RBF and an SMC for the two wheeled self-balanced robot. In Junfeng et al, 7 the sliding mode control is used for the stabilization of the robot and the disturbance rejection. Chawla et al 8 have validated the robustness of the SMC and LQR controllers in the presence of matched uncertainties and input disturbance.…”

Section: Introductionmentioning

confidence: 99%

Simultaneous adjustment of balance maintenance and velocity tracking for a two-wheeled self-balancing vehicle

Ghahremani,

Righettini,

Strada

2023

Proceedings of the Institution of Mechanical Engineers, Part C:

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The purpose of this research is the development a method for simultaneously adjusting the velocity tracking control and the inclination angle stabilization using control techniques for a two-wheeled self-balancing vehicle. The control tasks involve balancing the vehicle around its unstable equilibrium configuration along with steering and velocity tracking. In this study, the mathematical dynamic model of the vehicle is derived using the Lagrange method, under the assumptions of pure rolling and no-slip conditions which are expressed through nonholonomic constraint equations. Along with the mathematical descriptions, a multibody virtual prototype featuring advanced tire-ground interaction modeling has been developed using the MSC Adams software suite. Several classical and modern control strategies are investigated and compared to implement the method. These include Sliding Mode Control (SMC), Proportional Integral Derivative (PID), Feedback Linearization (FL), and Linear Quadratic Regulator Control (LQR) for the under-actuated and unstable subsystem that accounts for the pitch and longitudinal motions. The capabilities of these control strategies are verified and compared not only through Matlab simulation but also using Adams-Matlab co-simulation of the controller and the plant. Although every control technique has its advantages and limitations, the extensive simulation activities conducted for this study suggest that the SMC controller offers superior performances in keeping the system balanced and providing good velocity-tracking responses. Moreover, a Lyapunov-based analysis is used to prove that the sliding mode control achieves finite time convergence to a stable sliding surface. These advantages are counterbalanced by the complexity and the large number of parameters belonging to the designed SMC laws, the scheduling of which can be difficult to implement. For the comparison results another non-linear control strategy, that is, the feedback linearization method, is presented as an alternative. Through the Jacobian linearization approach the mathematical model of the system is linearized, allowing the use of control techniques such as linear quadratic regulation, which are deployed to treat the balancing, steering, and velocity tracking tasks. Finally, the empirical tuning of a PID controller is also demonstrated. The performance and robustness of each controller are evaluated and compared through several driving scenarios both in pure-Matlab and Adams-Matlab co-simulations.

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