Self-balancing Robot Research Articles

Low cost two-wheels self-balancing robot for control education

This paper presents an experimental, Arduino based, low cost self-balancing robot developed at the University of Seville for control education. The main idea is that the students can learn electronics, computer programming, modeling, control and signal processing by means of the construction and control of this robot. The resulting model is a multivariable unstable nonlinear system with non-minimum phase zero. Experimental results obtained by students of the University of Seville are included to demonstrate possibilities of the prototype.

Design and implementation of fuzzy-PD controller based on relation models: A cross-entropy optimization approach

In this paper, an intelligent system use fuzzy-PD controller based on relation models is developed for a two-wheeled self-balancing robot. Scaling factors of the fuzzy-PD controller are optimized by a Cross-Entropy optimization method. A linear Quadratic Regulator is designed to bring a comparison with the fuzzy-PD controller by control quality parameters. The controllers are ported and run on STM32F4 Discovery Kit based on the real-time operating system. The experimental results indicate that the proposed fuzzy-PD controller runs exactly on embedded system and has desired performance in term of fast response, good balance and stabilize.

A novel configuration of two-wheeled self-balancing robot

A novel configuration of two-wheeled self-balancing robot

Building embedded quasi-time-optimal controller for two-wheeled self-balancing robot

In this paper, two-wheeled self-balancing robot problem is introduced, and a quasi-time-optimal approach is applied to synthesize the control law. The result is compared with that of other methods. The process for designing and building the testting model, implementing the synthesized control law is also described.

An Improvement on Data-Driven Pole Placement for State Feedback Control and Model Identification

The recently proposed data-driven pole placement method is able to make use of measurement data to simultaneously identify a state space model and derive pole placement state feedback gain. It can achieve this precisely for systems that are linear time-invariant and for which noiseless measurement datasets are available. However, for nonlinear systems, and/or when the only noisy measurement datasets available contain noise, this approach is unable to yield satisfactory results. In this study, we investigated the effect on data-driven pole placement performance of introducing a prefilter to reduce the noise present in datasets. Using numerical simulations of a self-balancing robot, we demonstrated the important role that prefiltering can play in reducing the interference caused by noise.

Design and experimental evaluation of robust controllers for a two-wheeled robot

ABSTRACTThe paper presents the design and experimental evaluation of two alternative μ-controllers for robust vertical stabilisation of a two-wheeled self-balancing robot. The controllers design is based on models derived by identification from closed-loop experimental data. In the first design, a signal-based uncertainty representation obtained directly from the identification procedure is used, which leads to a controller of order 29. In the second design the signal uncertainty is approximated by an input multiplicative uncertainty, which leads to a controller of order 50, subsequently reduced to 30. The performance of the two μ-controllers is compared with the performance of a conventional linear quadratic controller with 17th-order Kalman filter. A proportional-integral controller of the rotational motion around the vertical axis is implemented as well. The control code is generated using Simulink® controller models and is embedded in a digital signal processor. Results from the simulation of the closed-loop system as well as experimental results obtained during the real-time implementation of the designed controllers are given. The theoretical investigation and experimental results confirm that the closed-loop system achieves robust performance in respect to the uncertainties related to the identified robot model.

Research on Two-Wheeled Self-Balancing Robot Control Strategy Based on LQR-Fuzzy Algorithm

Research on Two-Wheeled Self-Balancing Robot Control Strategy Based on LQR-Fuzzy Algorithm

LQ-Servo Design with Reduced Order Observer for Two-wheeled Self-balancing Robot based on HIL Simulation

In this paper, an LQ-servo design method based on reduced order observer is proposed in order to control the two-wheeled self balancing robot. We employ reduced order observer, which estimates the states that cannot be accessible by the direct measurements from sensors in order to make its implementation on real time digital processor simpler and cost-effective. The performance index of LQ-servo controller is determined such that overshoot and oscillation of the controlled system in time domain are minimized. The effectiveness and performance of the proposed control system are shown via numerical simulations. It is also shown from hardware-in-loop simulator that the proposed design for self balancing robot reflects the real time behavior quite well.

A LOW-COST, REAL-TIME ALGORITHM FOR EMBEDDED DEVICES BASED ON FREERTOS KERNEL

A low-cost, real-time methodology for embedded devices based on well-known open source kernel - freeRTOS is presented in this study. The real-time algorithm designed consists of three main steps. Firstly, the algorithm is designed and evaluated by utilizing Matlab/Simulink toolboxes. Secondly, the generic embedded C code is generated by Matlab program. Finally, freeRTOS Tasks code is utilized based on C code generated to build and run on embedded targets. This real-time algorithm is demonstrated on a two-wheeled self-balancing robot which is employed a fuzzy PID self-tuning controller. The designed controller is executed on a famous ARM Cortex M4 core microcontroller STM32F407VTG. The experimental results show that algorithm designed operated well on embedded systems. The tracking position and rotation angle response are so good with low steady error (i.e. 0.01 [m] and less than 1.15 degrees, respectively) while stabilizing the two-wheeled at the upright. The real-time system designed is a low cost methodology and suitable for embedded system designers.

A modified dynamical formulation for two-wheeled self-balancing robots

Two-wheeled self-balancing robot, moving on a horizontal plane, may be presented by a set of highly coupled nonlinear differential equations. In the recent literatures and in the commonly used two-wheeled self-balancing robots, the control algorithms are designed based on the mathematical models with simplified structure. In these models, a nonlinear coupling term is usually neglected, whereas it has significant effects on the dynamic behavior of the system. In this paper, the mathematical representation of two-wheeled self-balancing robots, including this new term, is derived using both Kane’s and Lagrangian methods. The significant effect of the new term on the response of the system is shown by presenting the behavior of the system under different conditions and by comparing it with the system models when this term is neglected. Then sliding-mode control techniques are used to derive the controllers. The controller objective is to drive the two-wheeled self-balancing robot to the desired path as well as to make the robot stable. By some simulations, the behavior of the robot with the proposed controller is discussed. It is shown that if the nonlinear coupling term is ignored in designing the controller, the controller cannot compensate its effect.

The Design of Fuzzy Adaptive PID Controller of Two-Wheeled Self-Balancing Robot

The Design of Fuzzy Adaptive PID Controller of Two-Wheeled Self-Balancing Robot

Monitoring and control system for a twowheeled self-balancing robot modeled in 3D

El diseno y el control de robot autobalanceado sobre dos ruedas es constituido como un importante avance tecnologico para la movilidad de transporte urbano del futuro, por lo tanto, es una alternativa viable de solucion al sistema de transporte inteligente (ITS). Este robot es considerado, en particular, como un problema de excelente referencia para los estudios de control, debido a la tarea compleja de mantener en equilibrio su estructura, por consiguiente, se elabora un sistema realimentado de monitoreo y de control en tiempo real, basado en una red inalambrica, para sintonizar su punto de equilibrio y verificar el funcionamiento del sistema, por medio de la integracion del protocolo ZigBee (estandar IEEE 802.15.4), con el entorno grafico de programacion LabVIEW, con un modelado 3D, que permite visualizar la dinamica del movimiento en el robot. En este articulo, se presenta un prototipo del robot autobalanceado, con la caracteristica de ser un vehiculo remoto, para la gestion de movilidad en espacios reducidos.

The research on the Application of Fuzzy Immune PD Algorithm in the Two-Wheeled and Self-Balancing Robot System

The fuzzy immune PD controller is designed in view of the nonlinear and model uncertainty characteristics of the two-wheeled and self-balancing robot, and the simulation study is carried out. The simulation results show that the designed control system has the advantages of small overshoot amount, short adjustment time and strong anti-jamming capability compared with the conventional fuzzy PD controller. It is suitable for the balance of the robot control system whose mathematical model is difficult to determine and when the parameter changes.

Adaptive fuzzy backstepping controller design for uncertain underactuated robotic systems

In this paper, the control problem of underactuated systems with mismatched and matched uncertainties is addressed. Adaptive fuzzy backstepping controller is proposed to solve the problem, ensuring the robustness against uncertainties and disturbances. Taking a general class of underactuated robotic systems into account, the nonlinear dynamical equations are first transformed to the so-called cascade form and then, an adaptive-based controller is constructed using the capability of fuzzy logic to tackle the perturbations. From the analytic point of view, the closed loop stability is ensured using the Lyapunov stability theorem. To demonstrate the effectiveness of the method, the proposed controller has been applied to a two-wheeled self-balancing robot with three degrees of freedom, and also to a pendubot with two degrees of freedom. In order to highlight the superiority of the proposed algorithm, the performance is compared with that of an existing robust strategy.

Design of Two-Wheeled Self-Balancing Robot Control System

The main controller STM32F103VET6 was used as the core of the system. The change of angle and angular velocity was detected by accelerometer and gyroscope built-in six axis attitude sensor MPU6050. The double closed-loop control was used to regulate the speed of DC motor JGA25-371, so as to adjust the posture of the robot. Test shows that the whole system design is simple, good stability and anti-jamming.

The Control Policy for Self-Balancing Robot

Self-balancing robot can stand on with two wheels. As the robot will vibrated during the angle adjusted dynamically,how to solve the angle sample and process noise is critical to system stability.This paper presents the hardware architecture of balance robot controller, discussed the data fusion and filter plus PID algorithm and its parameter selection,illustrate infrared communication and its protocol.

Multi-Legged Robot that Can be Assembled by Two-Wheeled Self-Balance Robots

In this paper, a multi-legged robot that can be assembled by self-balance robots that have two multi-degree-of-freedom limbs with wheel installed on the end of each limb is proposed. This type of robot has a more efficient way to travel all kinds of territory: not like normal muti-legged robots which has to use legs to move on flat surface like a roads, this type of robot can move like vehicle using wheels on the end of its legs; and not like two wheels self-balance vehicles which cannot travel on rocky surface, this type of robot can use multi-degree-of-freedom limbs to walk on complex surface. And by separate to several single robots, this type of robot can move through some narrow territory. And by combined as a multi-legged robot, this type of robot can transport much heavier payload than a single self-balance robot.

LQR+PID Control and Implementation of Two-Wheeled Self-Balancing Robot

A two-wheeled self-balancing robot system is designed, which uses TMS320LF2407DSP as the controller and Mio-x AHRS module as the main sensor. The dynamic model of robot is analyzed by state space model and transfer function. Robot’s physical system control is guided by the result from simulation experiments. A LQR+PID control method is put forward by comparing advantages and disadvantages of LQR control and PID control. The experiments result shows that this control method cuts down the design cycle, and also has a strong robustness. It is suitable for the multi-variable, high-rank, strongly coupling, non-linear two-wheeled self-balancing robot control.

Design of Two-Wheeled Self-Balanced Robot Based on Kalman Filter

Using the chip MC9S12XS128, two-wheeled self-balancing robot control system is designed. Its posture information is detected by accelerometer MMA7260 and gyro NEC-03, multi inertial sensor data fusion is realized by Kalman filter, posture data optimal estimation is gotten, and the accuracy of posture sensing system is improved. Using integral separation PID control algorithm, controlling the left and right motors are accelerated or decelerated, self-balancing control of two-wheeled robot is achieved. The experimental results show that, using the hardware platform MC9S12XS128, Kalman filter algorithm has high efficiency and posture data fusion is accurate and reliable, requirements which are posture optimal estimation and inclination data real-time feedback are met, and the system is stable and can accurately and quickly realize self-balancing control of two-wheeled robot.

Two-Wheeled Self-Balancing Robot Systems Using Fuzzy Immune Algorithm

For two-wheeled self-balancing robot nonlinear model uncertainties and other characteristics, the design of fuzzy immune PD controller and a simulation study. The simulation results show that the control system than the conventional fuzzy PD controller with a small overshoot, adjust the time is short, anti-interference ability, etc., applicable to the mathematical model and the parameter change is difficult to determine two-wheeled self-balancing robot control system.