Self-balancing Robot Research Articles

Design and development of two-wheeled self-balancing robot and its controller

Solving self-balancing problems of a wheeled robot that can assist human beings in real-time applications is a challenging task. Researchers around the world are adopting the concept of the inverted pendulum to control the robot in both the static and dynamic conditions. In the present research, the authors have designed and developed a two-wheeled robot and established a PID controller to make the robot self-balancing in nature while performing the task in office environments. The gains of the PID controller that help in achieving the self-balancing phenomenon are tuned with the help of trial and error method. Moreover, the manoeuvrability of the robot is controlled with the help of a wired nunchuck controller that uses a wireless serial transceiver module. Furthermore, the performance of the robot is evaluated by analysing its strength, load carrying capacity and stability through real-time experiments.

Design and development of two-wheeled self-balancing robot and its controller

Solving self-balancing problems of a wheeled robot that can assist human beings in real-time applications is a challenging task. Researchers around the world are adopting the concept of the inverted pendulum to control the robot in both the static and dynamic conditions. In the present research, the authors have designed and developed a two-wheeled robot and established a PID controller to make the robot self-balancing in nature while performing the task in office environments. The gains of the PID controller that help in achieving the self-balancing phenomenon are tuned with the help of trial and error method. Moreover, the manoeuvrability of the robot is controlled with the help of a wired nunchuck controller that uses a wireless serial transceiver module. Furthermore, the performance of the robot is evaluated by analysing its strength, load carrying capacity and stability through real-time experiments.

셀프-밸런싱 역진자 로봇에 대한 모델기반 실시간 궤적생성 방법

When a two-wheeled self-balancing mobile robot performs a rapid speed change, the nonlinear characteristic of the inverted pendulum dominates the pitch motion, and maintaining the postural stability becomes difficult when using a conventional linear controller only. Hence, in order to ensure agility and mobility in addition to postural stability during an acceleration or deceleration phase, an appropriate reference trajectory for the velocity and pitch control loop is needed in place of the typical step reference input. In this paper, we propose a real-time trajectory generation method for the underactuated self-balancing robot based on solving the nonlinear equation of motion, considering the dynamic characteristics, and applying numerical approximations. The effectiveness of the proposed planning scheme was confirmed through comparative simulations and experiments.

Stabilized controller of a two wheels robot

The Segway Human Transport (HT) robot, it is dynamical self-balancing robot type. The stability control is an important thing for the Segway robot. It is an indisputable fact that Segway robot is a natural instability framework robot. The case study of the Segway robot focuses on running balance control systems. The roll, pitch, and yaw balance of this robot are obtained by estimating the Kalman Filter with a combination of the pole placement and the Linear Quadratic Regulator (LQR) control method. In our system configuration, the mathematical model of the robot will be proved by Matlab Simulink by modelling of the stabilizing control system of all state variable input. Furthermore, the implementation of this system modelled to the real-time test of the Segway robot. The expected result is by substitute the known parameters from Gyro, Accelero and both rotary encoder to initial stabilize control function, the system will respond to the zero input curve. The coordinate units of displacement response and inclination response pictures are the same. As our expected, the response of the system can reach the zero point position.

Design and Simulation of Two-Wheeled Balancing Mobile Robot with PID Controller

Mobile transportation robots using two wheels have now been investigated. The work within this study is to design and simulate two-wheeled robots, thus it can maintain its balance. Many control methods are used to determine satisfactory control optimization, therefore a proper response is obtained by sensor recitation corresponding with the reaction of a Direct Current (DC) motor. Recently, two-wheeled transportation robot is a Segway model. In this study, we apply a Proportional Integral Derivative (PID) controller as a control system in a self-balancing robot with a working principle is similar to an inverted pendulum. In the next study, the PID controller and the whole system are applied in the microcontroller board. The angular velocity of two DC motors used as a plant can be adjusted by Pulse Width Modulation (PWM) through a motor driver. An Inertial Measurement Unit (IMU) sensor is utilized to detect the angular acceleration and angular velocity of the self-balancing robot. The phase design is constructed by planning the robot dimension, mechanical system, and an electronic system. Particularly, this study performs mathematical modeling of the robot system to obtain the transfer function. In addition, we simulate the PID parameter with multiplication between the basic parameter and several fixed constants. The simulation results indicate that the robot can maintain its balance and remains perpendicularly stable for balancing itself.

Fractional Order Control Methodology for the SelfBalance Robot

In this research paper the control algorithms like LQR and PID has been proposed for the integer and fractional order system. In this research paper the modeling of the selfbalance robot system has been carried out in integer domain and fractional domain. This research paper presents the simulation analysis of control algorithms for two wheel self-balancing robot using Linear Quadratic Regulator, Proportional-IntegralDerivative and Fractional order Proportional-Integral-Derivative control algorithm. These all control algorithm are applied on the integer order system and the fractional order system and comparative analysis has been done. The comparison between integer order PID against the fractional order PID is also been made for the self-balance robot. It has been demonstrated through simulation that fractional order controller gives better response as compared to integer order controller. Further it has been found out that fractional order controller gives better results when applied to fractional order system compared to its integer order counterpart.

A nonlinear optimal control based on the SDRE technique for the two-wheeled self-balancing robot

ABSTRACT In this article, for the first time, a nonlinear optimal controller based on the state-dependent Riccati equation (SDRE) technique is proposed for the self-balancing two-wheeled Robot. The proposed optimal control is similar to linear quadratic Gaussian (LQG) control. The LQG controller is based on linearisation, but the proposed nonlinear optimal controller is based on parameterisation. For this purpose, at first, a three-degree-of-freedom (3-DOF) model of the two-wheeled robot including, longitudinal displacement, yaw, and pitch motion of the chassis are obtained using Kane’s method. Then, using the parameterisation method, the state-dependent coefficient (SDC) matrices are derived for the design of the nonlinear quadratic Gaussian (NLQG) controller. Consequently, the 3-DOF model of the two-wheeled robot and the NLQG controller algorithm are simulated in the MATLAB software environment. Also, using the proposed controller and the open-loop simulations the performance of the two-wheeled Robot is discussed. The simulation results demonstrate that the optimal controller based on the SDRE approach performs remarkably well when the system has an extremely nonlinear behaviour. Abbreviations: KF: Kalman filter; CG: Centre of gravity; DOF: degree of freedom; LQG: linear quadratic Gaussian; NLQG: nonlinear quadratic Gaussian; SDRE: state dependent Riccati equation; LQR: linear quadratic regulator; RBF: radial basis function; SDC: state-dependent coefficient.

Ultrasonic Sensor Application As A Performance Enhancement Of Robot Two Wheels

In this paper, Robot Two Wheels are developed further to improve performance or intelligence. A Robot Two Wheels is a robot that can move and balance itself on two wheels, maintain its position upright of the earth’s surface on a flat plane. The design includes the Arduino Mega 2560, accelerometer and gyroscope sensors, as well as proportional Integral Derivative (PID) control as the controlling method. Proportional Integral Derivative control is used to determine the magnitude of the DC Motor’s speed and rotary direction as the driver, based on the robotic body’s tilt angle to the surface of the flat plane. The angle of inclination is taken into consideration and the robot moves either in the forward or backward direction to balance itself. When operated, the self-balancing robot continuously rotates its motor in its efforts to balance at upright position. The self-balancing robot movement is not mapped to its range, where the robot will still move randomly to achieve balanced conditions. The concept of this paper is expand the ability of the robot to sense objects around it which is by adding an ultrasonic sensor as an object detector around it, including obstacles or walls around self balancing robots. Detection of obstructions by ultrasonic sensors will maintain the movement of the robot in the safe area to avoid the impact that can occur when the robot is trying to maintain balance.

Fuzzy control of self‐balancing robots: A control laboratory project

AbstractThis paper presents a novel control laboratory project that provides hands‐on experience in feedback control concepts (embedded control systems) through dedicated assignments, with a particular focus on the design and implementation of fuzzy control. The project is structured around an inexpensive, portable self‐balancing robot (SBR), whose embedded system is realized using commercially available breakout boards as the first assignment. For the stabilization of the plant, students are guided to execute the essential stages of control system design, from system modeling and parameter optimization, over basic or advanced control strategy design in the MATLAB/Simulink environment, to both implementation and validation of the closed loop on the real robot. To demonstrate and foster the application of fuzzy logic, the second part of the paper introduces a simple control strategy based on fuzzy logic controllers. Then, a lookup table‐based implementation technique is described for the demonstration of manual interfacing and embedded coding of fuzzy control strategies. The proposed methods are clear and straightforward; they highly foster the understanding of feedback control techniques and allow students to gain vast knowledge in the practical implementations of control systems.

Adaptive Observer-Based Output Feedback Control for Two-Wheeled Self-Balancing Robot

In this paper, an output feedback control approach based on an adaptive observer is developed for the two-wheeled self-balancing robot subject to unknown parameters (with nonlinear parameterization). Firstly, a high gain control method with state feedback is proposed. Then, an adaptive observer is designed to estimate the unknown state and the unknown body mass of the robot which influences the height of the center of mass. Next, the adaptive observer is combined with the designed high gain controller: a Lyapunov-based stability analysis of the closed loop system is developed to establish the convergence of the tracking error as well as estimation and adaptation errors. Simulation results assert the performance of the developed tracking control scheme for the two-wheeled self-balancing robot subject to mass variation.

Low cost two-wheels self-balancing robot for control education powered by stepper motors

Abstract This paper presents a low cost, two-wheels self-balancing robot for control education powered by stepper motors developed at the University of Seville. This design improves a previous model based on DC motors that has been used for the last five years in different courses in which students learn electronics, computer programming, modeling, control and signal processing by means of the construction and control of this robot. The new design improves the performance and reduces the total price of the device.

Adaptive Nonlinear Control Algorithm for a Self-Balancing Robot

We previously developed a novel composite wheel-leg-track explosive ordnance disposal (EOD) robot with high mobility, able to switch between a track or a self-balancing motion mode according to environmental conditions, named Scorpio. In this paper, we propose an adaptive nonlinear control algorithm for improving the stability of the robot in the self-balancing mode. First, a model of the dynamics of the robot was established, with which we designed the nonlinear cascade controller for combined balance and motion control. With our system, the attitude of the robot is estimated using a Kalman filtering algorithm. Based on this, an adaptive adjustment algorithm amends the parameters of the controller in real time according to the state of the robot, for improved stability. In addition, we formulated an adaptive zero-offset angle identification algorithm to compensate for deviations caused by changes to the robot's center of gravity (due to changes to its mechanical structure), ensuring that this stability could be maintained. Results of experiments conducted to verify their operation show that self-balancing control of Scorpio can be achieved with the proposed algorithms.

Decision support for Design Conflicts: A model-based method to analyze the interactions between technical requirements and product characteristics

The diversity and multi-disciplinary of the technical products introduces conflicts as not all of the requirements can be fulfilled to the satisfaction of the stakeholders. Here the knowledge on the interactions between technical requirements and the product characteristics can help to find solutions for the optimum trade-offs between the requirements.In this manuscript, the state space of the product concept with the integrated technical requirements is explored. The generated data set is analyzed by the sensitivity analysis and later trade-offs are evaluated. As an example, the application of the proposed method in the development of a self-balancing robot is studied.

Control descentralizado basado en eventos para el consenso de múltiples robots tipo péndulo invertido en el esquema líder-seguidor

<p>El trabajo presenta el diseno de una estrategia de control distribuido con comunicación activada por eventos, que resuelve el problema de consenso líder-seguidor, de un conjunto de robots móviles tipo péndulo invertido (RMPI). La linealización de las ecuaciones de movimiento de los RMPI, alrededor del punto de equilibrio, permiten explotar las propiedades de planitud diferencial, dando lugar a una reparametrizacion del sistema mediante la salida plana. Asumiendo que los vehículos se comunican mediante una red, cuya topología es representada por un grafo no dirigido y fuertemente conectado, se disena una ley de control distribuido y una funcion de evento que indica el instante en el que el i-ésimo vehículo debe transmitir informacion (su estado) a sus vecinos. El resultado es un intercambio asíncrono de informacion entre vehículos y donde el tiempo entre eventos no es equidistante. El analisis de estabilidad se lleva a cabo en el sentido de Lyapunov y en el sentido entrada-estado ISS (Input-to-State Stability). Los resultados en simulacion numérica muestran el buen desempeño del consenso de la red de vehículos en dos escenarios representativos: regulacion y seguimiento de trayectoria.</p>

ALERA

The successful deployment of autonomous real-time systems is contingent on their ability to recover from performance degradation of sensors, actuators, and other electro-mechanical subsystems with low latency. In this article, we introduce ALERA, a novel framework for real-time control law adaptation in nonlinear control systems assisted by system state encodings that generate an error signal when the code properties are violated in the presence of failures. The fundamental contributions of this methodology are twofold—first, we show that the time-domain error signal contains perturbed system parameters’ diagnostic information that can be used for quick control law adaptation to failure conditions and second, this quick adaptation is performed via reinforcement learning algorithms that relearn the control law of the perturbed system from a starting condition dictated by the diagnostic information, thus achieving significantly faster recovery. The fast (up to 80X faster than traditional reinforcement learning paradigms) performance recovery enabled by ALERA is demonstrated on an inverted pendulum balancing problem, a brake-by-wire system, and a self-balancing robot.

Fractional-order control for a novel chaotic system without equilibrium

The control problem is discussed for a chaotic system without equilibrium in this paper. On the basis of the linear mathematical model of the two-wheeled self-balancing robot, a novel chaotic system which has no equilibrium is proposed. The basic dynamical properties of this new system are studied via Lyapunov exponents and Poincare map. To further demonstrate the physical realizability of the presented novel chaotic system, a chaotic circuit is designed. By using fractional-order operators, a controller is designed based on the state-feedback method. According to the Gronwall inequality, Laplace transform and Mittag-Leffler function, a new control scheme is explored for the whole closed-loop system. Under the developed control scheme,the state variables of the closed-loop system are controlled to stabilize them to zero. Finally, the numerical simulation results of the chaotic system with equilibrium and without equilibrium illustrate the effectiveness of the proposed control scheme.

Non-singleton General Type-2 Fuzzy Control for a Two-Wheeled Self-Balancing Robot

This paper presents several non-singleton general type-2 fuzzy logic controllers (NGT2FLCs) for an under-actuated mobile two-wheeled self-balancing robot to improve the anti-interference capability of the system. Four kinds of fuzzifiers, including singleton fuzzifier, type-1 non-singleton fuzzifier, interval type-2 non-singleton fuzzifier and general type-2 non-singleton fuzzifier, are considered to construct different general type-2 fuzzy logic controllers (GT2FLCs). In order to show the superiority of the GT2FLCs, three kinds of interval type-2 fuzzy logic controllers (IT2FLCs), including singleton IT2FLCs, type-1 non-singleton IT2FLCs (N1IT2FLCs) and interval type-2 non-singleton IT2FLCs (N2IT2FLCs), are also presented. A comparative study between singleton fuzzy controllers and non-singleton fuzzy controllers, and IT2FLCs and GT2FLCs is also shown. All simulation results show that the performance of non-singleton fuzzy logic controllers is better than that of singleton fuzzy logic controllers. The NGT2FLCs get the best performance in the presence of uncertainties and external disturbances.

Integrating negative dependencies assessment during mechatronics conceptual design using fuzzy logic and quantitative graph theory

This paper introduces a product-related dependency index that can be used during the conceptual design of mechatronic systems. The index uses fuzzy assessment of adverse effects dependencies combined with quantitative graph theory to compute the level of detrimental interactions present in a system. The paper also illustrates how the assessment of dependencies can be performed during early design stages and how components are differently affected by a specific adverse effect. Furthermore, the practicality of the new dependency index is exemplified with two case studies: a design experiment on a self-balancing robot and the conceptual design of a robotic arm for aerial manipulation. The design experiment demonstrates how adverse effects affect the performance of a mechatronic system and how the new dependency index is able to follow the decrease or increase of performance of the system. Then, the design of the robotic arm shows that decision-making can be improved when using this new dependency index as a criterion, as it allows one to select concepts for which less detrimental interactions exist. In both case studies, the index is shown to facilitate the design process as it can lead to fewer redesign loops which would result in reducing the development time and costs.

Dynamic balance control of two-wheeled self-balancing pendulum robot based on adaptive machine learning

To solve the problem of balance control in dynamic movement of two-wheeled self-balancing pendulum robot, a dynamic balance control method based on adaptive machine learning is proposed based on theoretical analysis of the cause of balance. Firstly, a kinematics model of two-wheeled self-balancing pendulum robot is established. Through numerical calculation and analysis, the root cause for dynamic balance of two-wheeled self-balancing pendulum robot is obtained. On this base, adaptive machine learnings are proposed to control the dynamic balance of two-wheeled self-balancing pendulum robot. The possible lateral movement of robot caused by dynamic balance control is analyzed. Finally, balance simulation test is conducted, which shows that the robot can easily lose balance and overturn without adaptive machine learning. The comparison of simulation test has verified that the proposed dynamic balance control method can effectively control the dynamic balance of two-wheeled self-balancing pendulum robot.

Navigational Control Analysis of Two-Wheeled Self-Balancing Robot in an Unknown Terrain Using Back-Propagation Neural Network Integrated Modified DAYANI Approach

SummaryThe present paper discusses on development and implementation of back-propagation neural network integrated modified DAYANI method for path control of a two-wheeled self-balancing robot in an obstacle cluttered environment. A five-layered back-propagation neural network has been instigated to find out the intensity of various weight factors considering seven navigational parameters as obtained from the modified DAYANI method. The intensity of weight factors is found out using the neural technique with input parameters such as number of visible intersecting obstacles along the goal direction, minimum visible front obstacle distances as obtained from the sensors, minimum left side obstacle distance within the visible left side range of the robot, average of left side obstacle distances, minimum right side obstacle distance within the visible right side range of the robot, average of right side obstacle distances and goal distance from the robot’s probable next position. Comparison between simulation and experimental exercises is carried out for verifying the robustness of the proposed controller. Also, the authenticity of the proposed controller is verified through a comparative analysis between the results obtained by other existing techniques with the current technique in an exactly similar test scenario and an enhancement of the results is witnessed.