Ball-balancing Robot Research Articles

Adaptive-based hierarchical sliding mode control for ball-balancing robots moving on an inclined plane

In contrast to managing a robot on a flat surface, controlling a balanced robot on a spherical wheel on an inclined surface is more challenging since the robot travelling up and down a slope has a higher chance of falling over. In this paper, the kinematic equation of the ball balancing robot (ballbot) is built as the learning method according to a 2D mathematical model. The authors use an adaptive-based hierarchical sliding mode control (AHSMC) based on Lyapunov theory to handle the target of a system whose parameters are undefined or change over time. The proposed sliding surfaces are demonstrated to be asymptotically stable and are validated through a simulation model implemented in Simscape software. The simulation results of the closed control design work correctly to control the motion of a ballbot when moving up the inclined surface.

Design, Implementation, and Control of a Ball-Balancing Robot

Design, Implementation, and Control of a Ball-Balancing Robot

A Comparison Between PID and LQR Controllers for Stabilization of a Ball Balancing Robot

Ball balancing robot (BBL) forms a dynamically stable system mounted on a ball which is in point contact with the ground surface. An omni-directional system for the BBL with maneuvering ability in the horizontal plane is attained as compared to two-wheeled robots, which can only move forward or backward. The stability of the BBL is defined by its capability to retain the upright position under all circumstances. Available literature [1, 2, 4, 5] includes the use of several single controllers to stabilize the BBL. This study performs a comparison of two popular controllers for stability analysis of the BBL, which included two model-based controllers, i.e., Proportional Integral Derivative (PID) and Linear Quadratic Regulator (LQR). A 2D planar model is considered for mathematical modeling at the two vertical planes as well as the horizontal plane. Furthermore, the steady state equations are derived using the Euler-Lagrangian method. PID and LQR controllers are used to provide stability to the BBL using a mathematical toolkit in MATLAB. The results from MATLAB are used to study the differences between PID and LQR for stability of the BBL based on time needed to balance the robot. The settling time for the PID and LQR controllers was 0.79 seconds and 2.25 seconds, respectively. The results illustrate that the PID controller stabilized the BBL in upright position efficiently and more swiftly as compared to the LQR controller.

Bayesian Multi-Task Learning MPC for Robotic Mobile Manipulation

Mobile manipulation in robotics is challenging due to the need to solve many diverse tasks, such as opening a door or picking-and-placing an object. Typically, a basic first-principles system description of the robot is available, thus motivating the use of model-based controllers. However, the robot dynamics and its interaction with an object are affected by uncertainty, limiting the controller's performance. To tackle this problem, we propose a Bayesian multi-task learning model that uses trigonometric basis functions to identify the error in the dynamics. In this way, data from different but related tasks can be leveraged to provide a descriptive error model that can be efficiently updated online for new, unseen tasks. We combine this learning scheme with a model predictive controller, and extensively test the effectiveness of the proposed approach, including comparisons with available baseline controllers. We present simulation tests with a ball-balancing robot, and door opening hardware experiments with a quadrupedal manipulator.

H - infinity full-state feedback control for a ball-balancing robot

H - infinity full-state feedback control for a ball-balancing robot

Model-Based Fault Diagnosis Algorithms for Robotic Systems

In this paper, three model-based fault diagnosis algorithms for robotic systems are designed, compared, simulated, and implemented. The first algorithm is based on a nonlinear adaptive observer (NLAO), where a sufficient condition for the convergence of the estimator is derived in terms of linear matrix inequality (LMI) under persistence of excitation condition. The second algorithm is based on an adaptive extended Kalman filter (AEKF). Unlike traditional approaches, where the fault parameters are considered as augmented state variables, the AEKF directly estimates the fault parameters from measurement data. The third algorithm is based on a cascade of a nonlinear observer (NLO) and a linearized adaptive Kalman filter (LAKF), called the adaptive exogenous Kalman filter (AXKF). The pros and cons for each algorithm are discussed. The performance of the algorithms is compared in a single-link joint robot system. Furthermore, the algorithms are implemented in a ball-balancing robot to detect and estimate the magnitude of the actuator faults.

Neural Network Control for Trajectory Tracking and Balancing of a Ball-Balancing Robot with Uncertainty

In this paper, a neural-network-based control method to achieve trajectory tracking and balancing of a ball-balancing robot with uncertainty is presented. Because the ball-balancing robot is an underactuated system and has nonlinear couplings in the dynamic model, it is challenging to design a controller for trajectory tracking and balancing. Thus, various approaches have been proposed to solve these problems. However, there are still problems such as the complex control system and instability. Therefore, the objective of this paper was to propose a solution to these problems. To this end, we developed a virtual angle-based control scheme. Because the virtual angle was used as the reference angle to achieve trajectory tracking while keeping the balance of the ball-balancing robot, we could solve the underactuation problem using a single-loop controller. The radial basis function networks (RBFNs) were employed to compensate uncertainties, and the controller was designed using the dynamic surface control (DSC) method. From the Lyapunov stability theory, it was proven that all errors of the closed-loop control system were uniformly ultimately bounded. Therefore, the control system structure was simple and ensured stability in achieving simultaneous trajectory tracking and balancing of the ball-balancing robot with uncertainty. Finally, the simulation results are given to verify the performance of the proposed controller through comparison results. As a result, the proposed method showed a 19.2% improved tracking error rate compared to the existing method.

볼 밸런싱 로봇의 경로 추종 및 자세제어를 위한 외란 관측기 기반 비선형 제어

This paper proposes a disturbance observer based nonlinear control method to realize the trajectory tracking and balancing of a ball balancing robot with external disturbances. As a ball balancing robot is underactuated and involves nonlinear couplings, it is difficult to design a controller that can simultaneously realize trajectory tracking and balancing. To address this problem, we introduce a virtual angle as a the reference angle for the trajectory tracking of the robot. In this manner, simultaneous trajectory tracking and balancing of the ball balancing robot can be realized using a single loop controller. In addition, we develop a disturbance observer to estimate the external disturbances and design the controller using the dynamic surface control method. According to the Lyapunov stability theory, all the errors are noted to be uniformly ultimately bounded. The simulation results demonstrate the performance of the proposed control system.

Sliding mode control of a ball balancing robot

This paper presents a sliding mode control design for a ball-balancing robot (ballbot), with associated real-time results. The sliding mode control is designed based on the linearized plant model, and is robust to matched uncertainties. The design is considerably simpler than other nonlinear control strategies presented in the literature, and the experimental results for stabilization and tracking show much better performances than those obtained with linear control (in particular, a linear quadratic regulator).

Whole-Body MPC for a Dynamically Stable Mobile Manipulator

Autonomous mobile manipulation offers a dual advantage of mobility provided by a mobile platform and dexterity afforded by the manipulator. In this paper, we present a whole-body optimal control framework to jointly solve the problems of manipulation, balancing and interaction as one optimization problem for an inherently unstable robot. The optimization is performed using a Model Predictive Control (MPC) approach; the optimal control problem is transcribed at the end-effector space, treating the position and orientation tasks in the MPC planner, and skillfully planning for end-effector contact forces. The proposed formulation evaluates how the control decisions aimed at end-effector tracking and environment interaction will affect the balance of the system in the future. We showcase the advantages of the proposed MPC approach on the example of a ball-balancing robot with a robotic manipulator and validate our controller in hardware experiments for tasks such as end-effector pose tracking and door opening.

Evaluating Direct Transcription and Nonlinear Optimization Methods for Robot Motion Planning

This letter studies existing direct transcription methods for trajectory optimization applied to robot motion planning. There are diverse alternatives for the implementation of direct transcription. In this study, we analyze the effects of such alternatives when solving a robotics problem. Different parameters such as integration scheme, number of discretization nodes, initialization strategies, and complexity of the problem are evaluated. We measure the performance of the methods in terms of computational time, accuracy, and quality of the solution. Additionally, we compare two optimization methodologies frequently used to solve the transcribed problem, namely sequential quadratic programming (SQP) and interior point method (IPM). As a benchmark, we solve different motion tasks on an underactuated and nonminimal-phase ball-balancing robot with a 10-D state space and 3-D input space. Additionally, we validate the results on a simulated 3-D quadrotor. Finally, as a verification of using direct transcription methods for trajectory optimization on real robots, we present hardware experiments on a motion task including path constraints and actuation limits.

2球体を用いた移動ロボットの開発

In this paper, we propose a dynamically balancing robot on the double ball and a ball driving method of it. It is possible that ball balancing robots move omnidirectionally with small footprint. Moreover, using double ball enables it to improve moving performance on discontinuous rough terrain without increasing footprint and complexity of the mechanism. This paper reports a driving ball method in the plurality of wheels and dynamic equations of unstable Double Ball Balancing Robot.

332 摩擦伝動を用いた2球式駆動系による玉乗りロボットの不整地適応性の向上(制御・ロボティクス)

332 摩擦伝動を用いた2球式駆動系による玉乗りロボットの不整地適応性の向上(制御・ロボティクス)