Self-balancing Robot Using Arduino and PID Controller

Savithri, C. N.; Roopesh, R. S.; Devi, N Lavanya; Shanthakumar, P.; Thirumurugan, P.

doi:10.1007/978-981-19-7169-3_18

Cited by 3 publications

(3 citation statements)

References 7 publications

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“…It is confirmed that by providing the condition SṠ < 0 the sliding surface S = 0 is an attractive surface that guarantees the stability of the sliding mode controller. 16 We have considered only SMC surfaces of the tilt angle and the longitudinal velocity of TWSBV for the SIMO subsystem. In order to prove the Lyapunov-based stability of the whole system, the steering subsystem which is single-input, single-output (SISO) should also be considered.…”

Section: Control System Designmentioning

confidence: 99%

“…Since the structure of the proposed control algorithm is based on the simplification of mathematical model to keep the controller design easy. 16 Hence, to clarify the effect of nonholonomic constraint on performance of system, before transitioning it to real-world applications. 17 It is necessary to be verified the performance of controller for both models with slippery road case and a with assumption of pure rolling, no-slip.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Control System Designmentioning

confidence: 99%

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 project's goal is to demonstrate a platform that automatically maintains the horizontal position using balancing sensors [22] that control at all times. Different researchers use PID controller [23] and Arduino to develop the self-balancing controller, which helps the platform with several parameters along the vertical axis and provides the signals for controlling the values of the accelerometer and gyroscope sensors to determine the precise balance [24].…”

Section: Introductionmentioning

confidence: 99%

Designing and controlling a self-balancing platform mechanism based on 3-RCC spherical parallel manipulator

Chen¹,

Tung²,

Lee³

et al. 2023

Robot. syst., appl.

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Motion control platforms have various applications in the manufacturing and automation industries. Different literature provides multiple issues related to the kinematics and dynamics of self-guided robots for transportation regarding platform balancing. Self-balancing platforms are utilized in many deliveries, stabilization, and transportation systems, and they are especially well suited for outdoor activities when the ground surface is not flat or structured. This paper describes developing a control technique for a self-balancing platform using the 3-RCC spherical parallel manipulator. This mechanism was designed to support an AGV (Automated Guided Vehicle) for transporting and lifting heavy weights for industrial applications. The AGV carries a robotic arm on top for different tasks. When the AGV encounters a steep slope or a rough surface, the AGV tilts, and the robotic arm’s performance is significantly affected. So, this study gives a solution to avoid these circumstances with a novel approach for the platform’s self-balancing mechanism consisting of a 3-RCC spherical parallel manipulator. Real-time stabilization and kinematics analysis methods are used to achieve the self-balancing system of the platform. When both methods are observed through different tilting angles for automation stability, Kinematic analysis performs more efficiently with less time duration when compared with the real-time stabilization method.

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Simultaneous regulation and velocity tracking control of a two-wheeled self-balancing robot

Ghahremani

Khalaji

2023

2023 International Conference on Control, Automation and Diagnosis (ICCAD)

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Self-balancing Robot Using Arduino and PID Controller

Cited by 3 publications

References 7 publications

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

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

Designing and controlling a self-balancing platform mechanism based on 3-RCC spherical parallel manipulator

Simultaneous regulation and velocity tracking control of a two-wheeled self-balancing robot

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