Adaptive Super-Twisting Control for Mobile Wheeled Inverted Pendulum Systems

Mengshi Zhang,Yu Cao,Jian Huang

doi:10.3390/app9122508

Mengshi Zhang, Yu Cao + Show 1 more

Open Access

https://doi.org/10.3390/app9122508

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Abstract

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.

Highlights

Mobile robots are widely used in various fields
The neural network (NN) training method requires a large amount of data, and the corresponding parameters are adjusted through the neurons, which cause a heavy computational burden [2]
One can conclude from the above observation that both strategies can balance the mobile wheeled inverted pendulum (MWIP) system effectively

Summary

Introduction

Mobile robots are widely used in various fields. As a special kind of mobile robot, the mobile wheeled inverted pendulum (MWIP) has attracted more and more attention thanks to its compact size, strong mobility, and high flexibility [1]. A plurality of methods have been studied based on the MWIP system. The model-free ones are applicable to general systems, but the control accuracy demands of physical applications are often difficult to meet due to the lack of system information. These algorithms, such as PID control, largely rely on well-tuned parameters, and it is difficult to guarantee the stability of the closed-loop system without the mathematical model. Another branch of the model-free approach is neural network (NN)-based approximation.

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