Adaptive neural sliding mode control for two wheel self balancing robot

Nghia, Vo Ba Viet; Thien, Tran Van; Sơn, Nguyễn Ngọc; Long, Mai Thang

doi:10.1007/s40435-021-00832-1

Cited by 8 publications

(8 citation statements)

References 31 publications

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“…However, when the robot operates in nonlinear regions with large pitch angles because of external disturbances, modeling errors, or internal maneuvers, the control performance of the linear control approaches will be degraded. In order to alleviate these problems and enhance the control performance, many nonlinear control methods have been investigated, such as feedback linearization control [17] [18], sliding mode control [19][20][21], backstepping control [22][23][24], and model predictive control [25] [26]. These approaches usually require a mathematical model for the design procedure.…”

Section: Introductionmentioning

confidence: 99%

“…In practice, it is hard to determine exactly the mathematical model, parameters usually change with respect to time due to the aging and affections of the external environment. In order to handle these challenges, adaptive control [19] [27][28][29], neural network control [19] [30][31][32], and fuzzy control [33][34][35][36] have been investigated for the TWIP robots. In [19], a neural network was used to estimate the unknown model parameters and a robust adaptive control was applied to compensate for the estimator errors and uncertainties in a two-wheeled self-balancing robot system.…”

Section: Introductionmentioning

confidence: 99%

“…In order to handle these challenges, adaptive control [19] [27][28][29], neural network control [19] [30][31][32], and fuzzy control [33][34][35][36] have been investigated for the TWIP robots. In [19], a neural network was used to estimate the unknown model parameters and a robust adaptive control was applied to compensate for the estimator errors and uncertainties in a two-wheeled self-balancing robot system. In [27], an adaptive backstepping control was constructed for a wheeled inverted pendulum system under the presence of the model parameter uncertainties.…”

Section: Introductionmentioning

confidence: 99%

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A Fuzzy LQR PID Control for a Two-Legged Wheel Robot with Uncertainties and Variant Height

Tran,

Hoang,

Loc

et al. 2023

Journal of Robotics and Control

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This paper proposes a fuzzy LQR PID control for a two-legged wheeled balancing robot for keeping stability against uncertainties and variant heights. The proposed control includes the fuzzy supervisor, LQR, PID, and two calibrations. The fuzzy LQR is conducted to control the stability and motion of the robot while its posture changes with respect to time. The fuzzy supervisor is used to adjust the LQR control according to the robotic height. It consists of one input and one output. The input and output have three membership functions, respectively, to three postures of the robot. The PID control is used to control the posture of the robot. The first calibration is used to compensate for the bias value of the tilting angle when the robot changes its posture. The second calibration is applied to compute the robotic height according to the hip angle. In order to verify the effectiveness of the proposed control, a practical robot with the variant height is constructed, and the proposed control is embedded in the control board. Finally, two experiments are also conducted to verify the balancing and moving ability of the robot with the variant posture.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Fuzzy LQR PID Control for a Two-Legged Wheel Robot with Uncertainties and Variant Height

Tran,

Hoang,

Loc

et al. 2023

Journal of Robotics and Control

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“…In Pathak et al, 5 partial feedback linearization is designed to control the two-wheeled inverted pendulum system. 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.…”

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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“…The domain of control systems has long been a cornerstone of engineering, dating to the application of Lagrangian mechanics and Newtonian mechanics to derive the governing equations of various systems [1]- [3]. While these classical mechanics have paved the way for modern control strategies, such as Proportional-Integral-Derivative (PID) controllers, linear quadratic regulators (LQR), fuzzy logic, and neural networks, a shift toward more advanced approaches is evident [4]- [17].…”

Section: Introductionmentioning

confidence: 99%

Comparative Study of Takagi-Sugeno-Kang and Madani Algorithms in Type-1 and Interval Type-2 Fuzzy Control for Self-Balancing Wheelchairs

Kiew-ong-art,

Chotikunnan,

Wongkamhang

et al. 2023

IJRCS

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This study examines the effectiveness of four different fuzzy logic controllers in self-balancing wheelchairs. The controllers under consideration are Type-1 Takagi-Sugeno-Kang (TSK) FLC, Interval Type-2 TSK FLC, Type-1 Mamdani FLC, and Interval Type-2 Mamdani FLC. A MATLAB-based simulation environment serves for the evaluation, focusing on key performance indicators like percentage overshoot, rise time, settling time, and displacement. Two testing methodologies were designed to simulate both ideal conditions and real-world hardware limitations. The simulations reveal distinct advantages for each controller type. For example, Type-1 TSK excels in minimizing overshoot but requires higher force. Interval Type-2 TSK shows the quickest settling times but needs the most force. Type-1 Mamdani has the fastest rise time with the lowest force requirement but experiences a higher percentage of overshoot. Interval Type-2 Mamdani offers balanced performance across all metrics. When a 2.7 N control input cap is imposed, Type-2 controllers prove notably more efficient in minimizing overshoot. These results offer valuable insights for future design and real-world application of self-balancing wheelchairs. Further studies are recommended for the empirical testing and refinement of these controllers, especially since the initial findings were limited to four-wheeled self-balancing robotic wheelchairs.

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Adaptive neural sliding mode control for two wheel self balancing robot

Cited by 8 publications

References 31 publications

A Fuzzy LQR PID Control for a Two-Legged Wheel Robot with Uncertainties and Variant Height

A Fuzzy LQR PID Control for a Two-Legged Wheel Robot with Uncertainties and Variant Height

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

Comparative Study of Takagi-Sugeno-Kang and Madani Algorithms in Type-1 and Interval Type-2 Fuzzy Control for Self-Balancing Wheelchairs

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