• Journal of the Korean Institute of Intelligent Systems
• /
• v.20 no.5
• /
• pp.617-623
• /
• 2010

In this research an LMI based mixed $H_2/H_{\infty}$ controller for a Wheeled Inverted Pendulum is designed and a numerical simulation of that is carried out. The Wheeled Inverted Pendulum is a kind of an inverted pendulum that has two equivalent points. To keep that the naturally unstable equivalent point, a controller should control the wheels persistently. Dynamic equations of the Wheeled Inverted Pendulum are derived with considering inclined road that is one of the representative road conditions. A Linear Matrix Inequality method is used to construct a controller that is able to stabilize the Wheeled Inverted Pendulum with considering the inclined road condition aggressively. Various numerical simulations show that the LMI based controller is doing well on not only flat road but also inclined road condition.

• The Transactions of the Korean Institute of Power Electronics
• /
• v.16 no.2
• /
• pp.162-172
• /
• 2011

This paper proposes embedded System of two wheeled inverted pendulum robot designed by model based design method, using MATLAB/SIMULINK and LEGO NXT Mindstorms. At first, stability and performance of controller is verified through modeling and simulation. After that direct conversion from simulation model to C code is carried and effectiveness of controller is experimentally verified. Two wheeled inverted pendulum robot has basic function about autonomous balancing control using principle of inverted pedulum and it is also possible to arrive at destination. In this paper, state feedback controller designed by quadratic optimal control method is used. And quadratic optimal control uses state feedback control gain K to minimize performance index function J. Because it is easy to find gain, this control method can be used in the controller of two wheeled inverted pendulum robot. This proposed robot system is experimentally verified with following performances - balancing control, disturbance rejection, remote control, line following and obstacle avoidance.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.16 no.2
• /
• pp.228-236
• /
• 2012

In this paper, a controller for two wheeled inverted pendulum robot is designed to have more stable balancing capability than conventional controller. Fuzzy control structure is chosen for the two wheeled inverted pendulum robot, and fuzzy membership function factors for the controller are obtained for specified 3 users' weights using trial-and-error method. Next a neural network is employed to generate fuzzy membership function factors for more stable control performance when the user's weight is arbitrarily selected. Through the simulation study we find that the designed fuzzy controller using the neural network is superior to the conventional fuzzy controller.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.16 no.3
• /
• pp.432-439
• /
• 2012

In this paper, instead of the conventional PD controller for balancing control of two wheeled inverted pendulum robots, an improved PD controller using the neural network is proposed and implemented for performance verification. First, a two wheeled inverted pendulum robot system is constructed for experiment. Next proper gains of the conventional PD controller according to users' weights are obtained for balancing the robot by use of the trial and error method. The PD gains based on the trial and error method are generalized through the neural network. Experiment results show that the PD controller based on the neural network has better performance than the conventional PD controller.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.15 no.3
• /
• pp.536-544
• /
• 2011

In this paper, a controller for two wheeled inverted pendulum type robot is designed to have more stable balancing capability than conventional controllers. Traditional PID control structure is chosen for the two wheeled inverted pendulum type robot, and proper gains for the controller are obtained for specified user's weights using trial-and-error methods. Next a neural network is employed to generate PID controller gains for more stable control performance when the user's weight is arbitrarily selected. Through simulation studies we find that the designed controller using the neural network is superior to the conventional PID controller.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.17 no.1
• /
• pp.8-14
• /
• 2013

In this paper, a control system for two wheeled inverted pendulum robot is implemented to have more stable balancing capability than the conventional control system. Fuzzy control structure is chosen for the two wheeled inverted pendulum robot, and fuzzy membership function factors for the control system are obtained for 3 specified weights using a trial-and-error method. Next a neural network is employed to generate fuzzy membership function factors for more stable control performance when the weight is arbitrarily selected. Through some experiments, we find that the proposed fuzzy control system using the neural network is superior to the conventional fuzzy control system.

• The Journal of Korea Robotics Society
• /
• v.4 no.4
• /
• pp.289-297
• /
• 2009

This paper presents the development and control of a two wheeled car-like mobile robot using balancing mechanism whose heading control is done by turning the handle. The mobile inverted pendulum is a combined system of a mobile robot and an inverted pendulum system. A sensor fusion technique of low cost sensors such as a gyro sensor and a tilt sensor to measure the balancing angle of the inverted pendulum robot system accurately is implemented. Experimental studies of the trajectory following control task has been conducted by command of steering wheel while balancing.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.20 no.5
• /
• pp.652-658
• /
• 2010

This paper presents the implementation and control of a two wheeled mobile robot(TWMR) based on a balancing mechanism. The TWMR is a mobile inverted pendulum structure that combines an inverted pendulum system and a mobile robot system with two arms instead of a rod. To improve robustness due to disturbances, the radial basis function (RBF) network is used to control an angle and a position at the same time. The reference compensation technique(RCT) is used as a neural control method. Experimental studies are conducted to demonstrate performance of neural network controllers. The robot are implemented with the remote control capability.

• Journal of Institute of Control, Robotics and Systems
• /
• v.18 no.5
• /
• pp.496-501
• /
• 2012

An equilibrium point of a WIP (Wheeled Inverted Pendulum) with changing its center of gravity is derived and validated by various numerical simulations. Generally, the WIP has two equilibrium points which are unstable and stable one. The unstable one is interested in this study. To keep the WIP over the unstable equilibrium point, the WIP is consistently being adjusted. A state feedback controller for the WIP needs a control reference for the equilibrium point. The control reference can be obtained by studying an equilibrium point of the WIP based on statics. By using Lagrange method, this study is deriving dynamic equations of the WIP both with and without changing its center of gravity. Various numerical simulations are carried out to show the validation of the equilibrium point.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.15 no.9
• /
• pp.1871-1880
• /
• 2011

In this paper, a controller for two wheeled inverted pendulum robot, i.e., Segway type robot that is a convenient and easily handled vehicle is designed to have more stable balancing and faster velocity control compared to the conventional method. First, a widely used PID control structure is applied to the two wheeled inverted pendulum robot and proper PID control gains for some specified weights of users are obtained to get accurate balancing and velocity control by use of experimental trial-and-error method. Next, neural network is employed to generate appropriate PID control gains for arbitrarily selected weight. Here the PID gains based on the trial-and-error method are used as training data. Simulation study has been carried out to find that the performance of the designed controller using the neural network is more excellent than the conventional PID controller in terms of faster balancing and velocity control.