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Implementation of AU balancing ballbot (AUB3
Abstract
This paper discusses on the AU Balancing BallBot or known as AUB 3 . AUB 3 in a structure of robot is using three Omni wheels, which drive by stepper motors. Accelerometer and Gyroscope are used as main sensors. PIC24FJ48GA002 is used as a main controller. An intelligent complimentary filter with PD control algorithm is used to control AUB3 to be in balanced position. The controller is proven on the prototype of AUB 3 . AUB 3 obtains it stability on the ball without falling off.
... Another linear double-loop controller [7], including inner-and outer-loop controllers with PID, was designed to control the balancing and transferring of a ballbot. Moreover, linear controllers [8][9][10][11][12][13] have been introduced for ballbots. In general, the linear control schemes have the great advantage of being simple and easy to apply to actual ballbots. ...
... The RB is required to transfer along the desired linear trajectory. Here, the real position of the ball can be calculated on basis of three encoders and system kinematics (11). When the robot was commanded to move the straight line, the results obtained are shown in Fig. 8. ...
A ridable ballbot (RB) is a car robot with novel inverse pendulum structures, wherein a body consisting of a cabin and a person dynamically balances on top of a ball. One of the interesting but challenging features of the RB is unstable, underactuated, and second-order constraints. The underactuated and unstable dynamics of the RB result in challenging planning and control tasks. To address these concerns, a full three-dimensional (3D) model of the RB and a nonlinear control approach for the complicated operation duties including balancing, heading, and transferring tasks have been proposed in this study. An Euler–Lagrange method is applied to achieve the full 3D dynamics of the RB. By applying an approach to transform the full 3D dynamics into two subsystems. Thus, a nonlinear feedback controller can be applied to control the RB system based
on stabilizing the transformed two subsystems. The proposed control quality and the stability analysis of the system are discussed. Simulation results are provided to demonstrate the superior performance of the proposed controller. Meanwhile, the experimental performance of the proposed controller is validated on an actual RB. Comparative simulation is conducted to demonstrate the merit features of the proposed control scheme.
... Rezero [4], a type of ballbot, was developed in 2010 and used three omnidirectional wheels to drive a spherical wheel, thereby resulting in smooth mobility on the floor. Several other ballbots [5], [6] equipped with a three-omnidirectionalwheel drive mechanism were also developed but were also limited in motion. ...
... Together with the development of ballbot structures and types (as shown in Figure 1), control problems (which vary from classical control methods [6], [8], [9] to modern techniques with single-loop [3], [10] and multiloop [1], [2], [11], [12] control systems) were discussed by many researchers. A linear-quadratic regulator (LQR) control law was found by using a linear state-space model to control balancing and station keeping [10]. ...
Mobile robots typically have more than three contacts with the ground, including at least two independent driving wheels, to be statically stable with a wide base for a large support polygon. These robots can rotate at any point but cannot make an instantaneous movement in every direction. They cannot move freely even in an open space when they are crowded with people in a common human-coexisting environment. Moreover, these robots have other limitations, such as minimum passage width and minimum turning radius.
... The ballbot is an underactuated system that has strong nonlinear couplings. Early studies used single-loop control methods such as proportional-integral-derivative (PID) [5], [6] and linear quadratic regulator (LQR) [7] based on the linearized model of the ballbot. However, linear control methods using a single-loop only focus on balance control. ...
This paper presents a robust control method for trajectory tracking and balancing of a ballbot with uncertainty. Since the ballbot is an underactuated system, previous studies have designed controllers using a hierarchical strategy and/or multi-loop approach. However, multi-loop control systems require several controllers and the hierarchical strategy has a local minimum problem that does not guarantee the convergence of all errors globally. To overcome these drawbacks, we introduce a virtual angle and design a sliding mode controller with a single-loop control system. As a result, the proposed controller is simple and can achieve simultaneous tracking and balancing of the ballbot. From Lyapunov stability theory, it is proven that the tracking and balancing errors of the ballbot are uniformly ultimately bounded and can be made arbitrarily small. Finally, simulation results are presented to verify the proposed control system.
... The AUSB2 has used a gyroscope and an encoder as sensors for the system. Another work done by Aphiratsakun et al., [4] is on the implementation of the AU Balancing BallBot (AuB3). A gyroscope has been used as a sensor for the AuB3. ...
This paper presents and discusses the Assumption University unicycle (hereafter referred to as AU unicycle). The AU unicycle was built on campus, and it can work in two modes: balancing control and riding control. A gyroscope and an accelerometer sensor are used as sensors to sense the balancing position of the unicycle. A DC motor equipped with a gear set, sprockets, and a chain acts as an actuator to operate the wheel of the unicycle system. The main contribution and the objective of this paper are to demonstrate the implementation methodology and the balancing and riding control methods of the AU unicycle. The experiments on the AU unicycle show good results for both the balancing control method and the riding control method.
A ridable ballbot has unstable underactuated dynamics with nonholonomic velocity constraints and input coupling case. Such a robot can carry people and move in any direction on the floor. In this study, a pseudo-2D dynamic model of the ridable ballbot is derived. An improved nonlinear double-loop control system combined with the partial feedback linearization (PFL) technique is proposed for the ridable ballbot, which allows it to balance and transfer. Feedforward compensation is added to the controller to consider uncertainties, friction, and disturbances. Numerical simulations and experiments show the validity of the PFL double-loop control based on the pseudo-2D dynamic model.
In this study, we propose a nonlinear double-loop control system for a ridable ballbot. An improved nonlinear double-loop control technique based on double-loop control scheme and sliding mode technology is used, and the inner-loop consists of proportional–integral feedback plus feedforward control. A sliding mode control enables the ridable ballbot to balance and transfer on the floor. Feedforward compensation has been added for the stability of the ballbot. As a result, the control system can stabilize all state variables of the ballbot system despite the uncertainties of the system parameters such as model, friction, and external disturbances, and relatively large body inertia. Experiments demonstrate the effectiveness of the proposed control system and the performance validation of the nonlinear double-loop control.
In this study, the dynamical model of a ridable ballbot system is investigated to derive a nonlinear model that can describe the spatial dynamics of the mechatronic system by use of a set of differential equations. An aggregated hierarchical sliding mode control is proposed for a spatial ridable ballbot which is a class of multiple-input and multiple-output underactuated systems funder input coupling case and nonholonomic velocity constraints. The spatial ridable ballbot system is divided into ball and body subsystems. The hierarchical structure of the sliding mode surfaces (SMSs) is designed on the basis of the two subsystems as follows. First, the SMS of every subsystem is defined. Then the SMS of each subsystem is defined as the first layer SMS. The first layer SMS is used to construct the second layer SMS with the SMS of the other subsystem. This process continues until the SMS of all subsystems is included. The total control law is deduced from the hierarchical structure. Asymptotic stability of the control system is theoretically proven by Barbalat’s lemma in the ideal of Lyapunov theory. Simulation and experimental results indicate the validity of the proposed controller.
This paper proposes a robot balanced on a ball. In contrast to an inverted pendulum with two wheels, such as the Segway Human Transporter, an inverted pendulum using a ball can traverse in any direction without changing its orientation, thereby enabling stable motion. Such robots can be used in place of the two-wheeled robots. The robot proposed in this paper is equipped with three omnidirectional wheels with stepping motors that drive the ball and two sets of rate gyroscopes and accelerometers as attitude sensors. The robot has a simple design; it is controlled with a 16-bit microcontroller and runs on Ni-MH batteries. It can not only stand still but also traverse on floor and pivot around its vertical axis. Inverted pendulum control is applied in two axes for attitude control, and commanded motions are converted into velocity commands for the three wheels. The mechanism, control method, and experimental results are described in this paper.
This paper presents a control framework for a biped robot to maintain balance and walk on a rolling ball. The control framework consists of two primary components: a balance controller and a footstep planner. The balance controller is responsible for the balance of the whole system and combines a state-feedback controller designed by pole assignment with an observer to estimate the system's current state. A wheeled linear inverted pendulum is used as a simplified model of the robot in the controller design. Taking the output of the balance controller, namely the ideal center of pressure of the biped robot on the ball, as the input, the footstep planner computes the foot placements for the robot to track the ideal center of pressure and avoid a fall from the rolling ball. Simulation results show that the proposed controller can enable a biped robot to stably walk on balls of different sizes and rotate a ball to desired positions at desired speeds.
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