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![Figure 13. The robot tracks a reference pitch angle ( Ore! = 5 [deg]) •...](profile/Gia-Minh-Thao-Nguyen/publication/238008621/figure/fig3/AS:650819997274122@1532178987479/The-robot-tracks-a-reference-pitch-angle-Ore-5-deg-The-robot-moves-to-the-set_Q320.jpg)
[The 2010 IEEE International Forum on Strategic Technology (IFOST 2010), pp.76-81]. This paper presents a method to design and control a two-wheeled self-balancing robot and it focus on hardware description, signal processing, discrete Kalman filter algorithm, system modelling and PID backstepping controller design. In the system, signals from angl...
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Citations
... One example is a two-wheeled balancing robot that can utilize such technology to achieve a higher level of intelligence [1]. Balance Robot is one type of robot that is able to stand upright premises using two wheels on both sides, balance robot has characteristics with fast dynamics, unstable, and nonlinear [2] . The first balancing robot system was announced by Dean Kamen in 2001 as the SEGWAY, known as the "first self-propelled electric Transporter". ...
At first, robots were created to replace routine human work, requiring a high level of precision, and also to replace humans in dangerous situations. Robots that have the ability to maintain self-balance are called Self Balancing Robots. The Self Balancing Robot is the result of the development of an inverted pendulum model which is positioned on top of a wheeled train. The robot is controlled using a remote control using NRF 24L01 which can be controlled remotely. And to find out the situation in running the robot which is controlled from the remote using To detect the tilt angle of the robot, the MPU6050 sensor is used which is a module that integrates three sensors, namely the gyroscope and accelerometer on three different axes.
... Also Thao et.al. presents a method to design and control a TWSBR with backstepping controller and discrete Kalman filter algorithm with objective of designing a closed loop controller that stabilize the robot while try to keep the motion of robot to track a reference signal [5].Also, Jinfeng Qiu et. al. presents a nonlinear dynamic model for a self-balancing robot using Euler-Lagrange method and Maple software. ...
... The system identification is challenging on itself as it is mere compromise between accuracy and complexity of identified model. Several studies had been performed to identify the motor parameters by different techniques including heuristic approach [10]- [13] and least square methods [5], [14]- [17]. In this study, we have used the Two-Wheeled Self-Balancing Robot (TWSBR) consist of two motors at the end to shift the position to keep the pendulum upright. ...
... V out (s) V s,in (s) = 12 1 + 0.00116S (5) were, V out is output voltage to motor and V s,in is the input signal referencing ±12 volts in driver. ...
Two-Wheeled Self-Balancing Robot (TWSBR) has many applications and is an important research topic in robotics and control engineering. The principle behind the operation of a TWSBR is an inverted pendulum. The mathematical modelling of the TWSBR is still challenging as it includes non linear components to operate. This study aims the analysis of a two-wheeled, micro-controller based fabricated TWSBR. The linear mathematical model of each of the components of TWSBR has been obtained. Some of the components has been modelled using experimental model identification method while some are represented analytical approximations. A PID controller has been used to balance the TWSBR in upright position. The overall system has been simulated in MATLAB/SIMULINK. The result from the simulation is compared with real time hardware setup of the system to validate the mathematical model and stability of the TWSBR. The investigation has shown adequate accuracy in the models of components used in TWSBR. Moreover, the simulated results shows similar behaviour to that of hardware realization of the robot. This study may help on several research related to system identification, mathematical modeling and controller design of TWSBR.
... The so-called self-balancing control system is characterized by taking different displacement directions including the system itself as the control object, and making the system react to the change of parameters through the output of the control system program, so as to achieve a certain control range and make the corresponding displacement operate in a stable state, that is, the self-balancing control technology [4]. The control system with the center of gravity on top and the pivot point on the bottom, which appears in the most primitive balancing car, is a basic self-balancing control system. ...
The balance car is one of the types of balance robots that keep the machine in balance according to the constant adjustment of the sensors and controllers of the machine itself. As a basic balancing robot, the two wheeled self balancing robot not only has the characteristics of simple structure and program, small volume, but also can realize the rotation movement in a small radius, which proves that the motion of the balancing vehicle itself is flexible, and it is very suitable as one of the tools of urban traffic. As a new tool, balance car is in the stage of rapid development. On the premise of briefly comparing and analyzing the principles, characteristics and backgrounds of different balance robots, this research summarizes the development trend, advantages and disadvantages of balance cars in the current market, and provides targeted development suggestions based on objective analysis.
... It is NMP after linearizing about the upright equilibrium point. Due to its under-actuated nature, this platform is widely used for controller design and implementation studies, including PID controllers [14][15][16][17][18][19], fuzzy logic controllers [20][21][22], Fuzzy combined with PID [23], adaptive control [24], and linear quadratic regulation (LQR) control [19,[25][26][27]. There are many articles that have investigated complex and strong control methodologies to NMP systems, such as [28]. ...
Early analytical work showed that it is possible to discretize a continuous-time non-minimum phase (NMP) system using special sample and hold inputs (SHIs) and obtain a discrete-time minimum phase (MP) system with a higher sampling rate than that of conventional zero-order sample and hold. As a result, high-gain discrete-time control law can be used to further improve closed-loop system performance since the resulting discrete-time system is MP. In this paper, this technique is adopted for performance improvement on a mini-Segway (MS), a developed robot equipped with an extremely low-cost microcontroller (Arduino). A dual-loop control tuning method was developed to optimize the overall closed-loop system performance. The system performance improvement is demonstrated by the reduction of the mini-Segway oscillation magnitude under idle conditions. The experimental results show that the low-cost microcontroller can be used for the dual-loop SHI control scheme, and the MS cart displacement oscillation magnitude is significantly reduced by more than 65% over the baseline controller. The main contribution of this paper is implementing the proposed sample and hold input (SHI) scheme into an extremely low-cost micro-controller (Arduino) and demonstrating that low-cost microcontrollers can effectively utilize the proposed SHI.
... Researchers have offered traditional controls such as Soft PD (Proportional-Derivative), PID (Proportional-Integral-Derivative), PD+I, and LQR (Linear Quadratic Regulator) as solutions to solve the self-balancing problems [8][9][10][11]. Such techniques require manual calibration and, thus, are based on several trials and errors to reach optimum controller values, resulting in their failure to achieve most of the time-optimal qualities. ...
Human body balance is a gradual formation through repetition of actions, trial and error, and improving the mechanism of muscular-skeletal architecture for adapting to the demands of the environment. In the learning process, sensory receptors continuously send signals to the brain, then the brain to muscles and make a new signals pathway. Each time the body performs an action, millions of new synaptic connections are formed, and repetitive actions strengthen connections. So, a balanced body reuses the learned mechanism without performing any complex calculations. In contrast, the balance problem of a self-balancing robot has been solved by many different control algorithms. In this work, we propose a novel way to balance a two-wheeled self-balancing robot using bio-realistic Spiking Neural Networks (SNNs) to learn self-balancing, which is closely related to the way babies learn. To accomplish this, the gaussian shaped sensory neuronal population is connected with motor neurons through Spike-Timing-Dependent Plasticity (STDP) based synapses, further controlled with dopamine neurons. The key aspects of this approach are its bio-realistic nature and zero dependencies on data for adopting a new behavior compared to Deep Reinforcement Learning. Furthermore, this biologically-inspired mechanism can be used to improve the methodology for programming the robots to mimic Biological Intelligence.
... There are many effective control methods available for the TWSB robot with nonlinear kinetics, such as the method using a proportional integral derivative (PID) controller [2], the optimal controller based on the linear-quadratic regulator (LQR) [3], [4], the robust-adaptive control [5], the backstepping control [6], [7], the sliding mode control [8], the fuzzy logic control structure [9] and the control system based on a neural network [10]. However, there is a lot of noise during outdoor operation that negatively affects the control system's quality. ...
This article presents the sliding control method combined with the selfadjusting neural network to compensate for noise to improve the control system's quality for the two-wheel self-balancing robot. Firstly, the dynamic equations of the two-wheel self-balancing robot built by Euler–Lagrange is the basis for offering control laws with a neural network of noise compensation. After disturbance-compensating, the sliding mode controller is applied to control quickly the two-wheel self-balancing robot reached the desired position. The stability of the proposed system is proved based on the Lyapunov theory. Finally, the simulation results will confirm the effectiveness and correctness of the control method suggested by the authors.
... There has been a lot of control algorithms for this object. In [2,3] using traditional PID controllers are presented with various methods of determining controller parameters. The studies [4,5] built the controller according to LQR method but did not consider the energy consumption through control signals. ...
In the paper, we present a method of synthesizing the controller with optimum energy for two-wheeled self-balancing vehicles, based on the multipurpose consumption function. The authors select the multipurpose consumption function based on the quasi-optimality and minimum of energy. The synthesis of control laws based on the analytical design of aggregated controllers (ADAR) with manifold of quasi-optimality. The results of the proposed method are simulated and compared to other methods.
... PID controllers have been used for various tasks, but in robotics its main purpose is stabilizing motion [30]. Nguyen Gia Minh Thao et. ...
In this thesis a new system for generating stand up motions for bipedal robots from the prone and supine position is presented. The system uses quintic splines to generate trajectories for each of the four end-effectors. By applying PID control to the robots inertia measurement unit, the robots centre of pressure is kept inside the support polygon during the motion and the overall stability is increased. Through an IK solver, the spline goals are transformed into motor goals and then applied to the robot. The evaluation of the proposed system shows significant improvements over the conventionally used keyframe approach, both in success rate and execution time.
... Meanwhile, most systems nowadays require high-sensitivity sensors to increase its system performances [2] [3]. In example, high-sensitivity accelerometer and gyro are needed in robot systems [4] [5] [6], quad-rotors [7] and maglev systems [8]. These sensors are commonly being uncovered from external disturbances such as wind pressure and vibration of rotors. ...
... There are various types of Kalman Filter, such as standard Kalman Filter [4], Extended Kalman Filter [22], Unscented Kalman Filter [23] and Ensemble Kalman Filter [24]. Standard Kalman Filter is the simplest while the other types are modified for more complicated tasks. ...
Most systems nowadays require high-sensitivity sensors to increase its system performances. However, high-sensitivity sensors, i.e. accelerometer and gyro, are very vulnerable to noise when reading data from environment. Noise on data-readings can be fatal since the real measured-data contribute to the performance of a controller, or the augmented system in general. The paper will discuss about designing the required equation and the parameter of modified Standard Kalman Filter for filtering or reducing the noise, disturbance and extremely varying of sensor data. The Kalman Filter equation will be theoretically analyzed and designed based on its component of equation. Also, some values of measurement and variance constants will be simulated in MATLAB and then the filtered result will be analyzed to obtain the best suitable parameter value. Then, the design will be implemented in real-time on Arduino to reduce the noise of IMU (Inertial Measurements Unit) sensor reading. Based on the simulation and real-time implementation result, the proposed Kalman filter equation is able to filter signal with noises especially if there is any extreme variation of data without any information available of noise frequency that may happen to sensor- reading. The recommended ratio of constants in Kalman Filter is 100 with measurement constant should be greater than process variance constant.
... Penelitian tentang robot keseimbangan telah banyak dilakukan oleh peneliti sebelumnya dengan menggunakan berbagai macam pengendali seperti LQR (Linear Quadratic Regulator) [2], PD (Proporsional Derivatif) [3], PID (Proporsional Integral Derivatif) [4], Neural Network Control [5], Backstepping [1]. Namun, penelitian tersebut masih sebatas pada simulasi. ...
Robot Keseimbangan memiliki dinamika yang cepat, tidak stabil, dan nonlinearsehingga memerlukan pengendali yang sesuai. Robot keseimbanganmenggunakan sensor accelerometer untuk mengukur perubahan sudut. Sifatsensor tersebut adalah sangat sensitif dan bernoise sehingga memerlukanmetode untuk mengurangi noise tersebut. Pada penelitian ini digunakanpengendali PID karena memiliki respon yang cepat dalam menanggapi perubahansudut dan mudah untuk diterapkan. Sementara untuk mengurangi noie sensordigunakan metode kalman filter. Hasil pengujian menunjukkan bahwametode kalman filter mampu untuk mengurangi noise pada sensor accelerometer.Nilai parameter kalman filter sangat mempengaruhi hasil filter sehinggamemerlukan penentuan nilai yang tepat. Nilai matriks variasi proses haruslebih besar daripada nilai matriks variasi pengukuran. Nilai parameter kalmanfilter yang terbaik adalah matriks variasi proses R = 10 dan matriks variasipengukuran Q = 0; 01. Pengendali PID dapat menstabilkan robot pada posisitegak. Nilai parameter terbaik pengendali PID adalah Kp = 20, Ki = 1, danKd = 20.
