Nonlinear control of mobile inverted pendulum

Brisilla, R. M.; Sankaranarayanan, V.

doi:10.1016/j.robot.2015.02.012

Cited by 41 publications

(21 citation statements)

References 29 publications

(51 reference statements)

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“…This makes sense in physical applications, since too large an initial error may lead to overshoot, thereby causing unsafe factors. Moreover, in other studies on MWIP systems, the significant initial errors are not taken into account [1,[24][25][26]. Especially, in [26], there exists a support frame to guarantee a small initial error, which ensures a smooth start for the MWIP.…”

Section: Methodsmentioning

confidence: 99%

Adaptive Super-Twisting Control for Mobile Wheeled Inverted Pendulum Systems

2019

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Recently, the mobile wheeled inverted pendulum (MWIP) has gained an increasing interest in the field of robotics due to traffic and environmental protection problems. However, the MWIP system is characterized by its nonlinearity, underactuation, time-varying parameters, and natural instability, which make its modeling and control challenging. Traditionally, sliding mode control is a typical method for such systems, but it has the main shortcoming of a "chattering" phenomenon. To solve this problem, a super-twisting algorithm (STA)-based controller is proposed for the self-balancing and velocity tracking control of the MWIP system. Since the STA is essentially a second-order sliding mode control, it not only contains the merits of sliding mode control (SMC) in dealing with the uncertainties and disturbances but can also be effective in chattering elimination. Based on the STA, we develop an adaptive gain that helps to learn the upper bound of the disturbance by applying an adaptive law, called an adaptive super-twisting control algorithm (ASTA). The stability of the closed-loop system is ensured according to the Lyapunov theorem. Both nominal experiments and experiments with uncertainties are conducted to verify the superior performance of the proposed method.

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Section: Methodsmentioning

confidence: 99%

Adaptive Super-Twisting Control for Mobile Wheeled Inverted Pendulum Systems

2019

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“…The mathematical representation that describes the dynamic behavior of the inverted pendulum can be obtained from the application of the Newtonian, Lagrangian or Hamiltonian mechanical theory, which in general terms leads to a non-homogeneous second order difference equation with constant coefficients, which for simulation and methodology validation purposes does not take into account the dynamics of the actuator and the friction in the joints. In this way, the mathematical model of the pendulum is modeled as shown in equation (1).…”

Section: Inverted Pendulummentioning

confidence: 99%

“…The inverted pendulum model is a classic problem of non-linear control due to its complexity and non-linearity, which allows analogies to be made with phenomena such as the stabilization of cranes, buildings and the applications of robotics, to evaluate and analyze the phenomenon as such and perform the respective control for optimum and efficient performance. Non-linear control strategies have been applied to move a vehicle with the pendulum inverted on board, thus stabilizing the pendulum by moving the vehicle from one place to another using non-linear coordinate transformations based on navigation designs [1]. Similarly, a non-linear cascade control was applied to an unmanned aerial vehicle to keep the pendulum and the spacecraft stabilized during the pendulum's trajectory, with the result that the longitudinal movement of the inverted pendulum is easier to control than its transverse movement [2].…”

Section: Introductionmentioning

confidence: 99%

Fuzzy control of an inverted pendulum systems in MATLAB/Simulink

Ochoa¹,

Forero²,

Quiñones³

2018

ces

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This paper presents the effect of the performance tuning a fuzzy controller in terms of the integral of the absolute error value in order to control the position of classical nonlinear problem of inverted pendulum. The methodological development is based on the theory of fuzzy logic controller design considering the expertise acquired on the dynamics of the mechanical system under consideration, thus avoiding the development of rigorous models to represent the dynamics. A set of simulations to evaluate the driver capabilities were developed for the nonlinear system model to different tuning values, which uses 7 membership functions for position and 3 for speed, allowing to determine the sensitivity of the membership functions based on the operational response of this system.

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“…Known for their rich dynamics, pendular systems (PS) have been widely used for modelling and as benchmark systems in many areas, namely long-chain molecular physics (Mezić, 2006;Vesely, 2013), control theory (Boubaker, 2013;Kurdekar & Borkar, 2013;Larcombe, 1992), robotics (Ali, Motoi, Heerden, & Kawamura, 2013;Brisilla & Sankaranarayanan, 2015), biomechanics (Barin, 1992;Schiehlen, 2014), building structures (Housner, 1963;Zayas, Low, & Mahin, 1990), chaos and nonlinear dynamics (Gmiterko & Grossman, 2010;Hedrih, 2008;Lobas, 2005;Yu & Bi, 1998), among others (Baker & Blackburn, 2005;Jadlovská, Sarnovskỳ, Vojtek, & Vošček, 2015).…”

Section: Introductionmentioning

confidence: 99%