Fuzzy control based on LMI approach and fuzzy interpretation of the rider input for two wheeled balancing human transporter

Abstract

This paper presents a novel hierarchy intelligent control scheme for a two-wheeled human transportation vehicle which is similar to an inverted pendulum. As a highlevel supervisory piloting scheme, the paper presents a fuzzy intelligent interpretation of the rider's body inclination. It provides an interface between human user and the vehicle with the aim to enhance the piloting capabilities and convenience from human user viewpoint. As a direct controller, considering the nonlinearities of the system, Takagi-Sugeno fuzzy model is proposed to combine linearized systems and their controllers, where the controller gains of each linear system are optimally obtained by LMI method. The effectiveness of the proposed approaches is verified by applying them on the nonlinear model of the system.

Dynamic modeling of a two-wheeled inverted pendulum balancing mobile robot

“…However, in [3,7,24], the unit vector differentiation process for the pendulum angular velocity was neglected because the pitch rate was wrongly described with respect to the fixed frame as 2 3ˆˆ.…”

“…Eventually, it led to an error in the dynamic model and arrived at the wrong conclusion that a certain pitch angle is necessary to keep uniform velocity of the robot. Furthermore, that misinformation was delivered to numerous follow-on works including [3,4,7,8,[12][13][14][15][16]21,22,24].…”

“…Thirdly, Kane's method [23] is also based on vector operations like Newtonian mechanics, but it doesn't have to find interactive forces between the rigid bodies. Instead, it determines generalized inertia forces and generalized active forces by means of the partial velocities along the generalized coordinates [3,7,24].…”

“…For example, it can pass through narrow spaces unlike three wheelers or four wheelers and enables the driver to steer on a spot and maintain an upright posture on inclined terrains. Hence, the 2WBMR platforms are being widely applied, from unmanned navigation vehicles [1][2][3][4] to personal transporters [5][6][7][8][9] and even to robot wheelchairs for the disabled [10,11].…”

A new device laboratory: Experimental validation

“…A commercial variant of the MIP is the well known Segway (Nguyen et al 2004). Based on LMI techniques, (Azizan et al 2010) presents a hierarchy intelligent control scheme for a Segway vehicle, but without real experimentation. Also, a fuzzy control is designed and implemented to the same problem in (Huang et al 2011).…”