Top PDF A novel configuration of two-wheeled self-balancing robot

A novel configuration of two-wheeled self-balancing robot

A novel configuration of two-wheeled self-balancing robot

Izvorni znastveni članak U ovom se istraživanju predstavlja novi projekt samobalansirajućeg robota na dva kotača - two wheeled self-balancing robot (TWSR). Robot je nazvan two-wheeled self-balancing vehicle-pendulum system (TWSVPS). U usporedbi s TWSR, TWSVPS ima jedno klatno (single pendulum (SP)) koje je pasivno spojeno s tijelom robota preko O1 osovine. Teškoća i složenost upravljanja te modeliranje povećani su zbog dodatnog stupnja slobode - degree of freedom (DOF) s klatnom koje rotira oko osovine O1. Zbog jednostavnije analize, TWSVPS se može smatrati vozilom s pokretnim dvostruko obrnutim klatnom. Zbog svoje relativne jednostavnosti, za izvođenje dinamike sustava primijenjena je Lagrangeova dinamička formulacija. Paralelni dvostruki fuzzy upravljač zasnovan na informacijskoj tehnologiji fuzije projektiran je i simuliran u MATLAB-u. Rezultati pokazuju da je metoda izvediva i TWSVPS je izvanredno stabilan u kretanju. Novo razvijena konfiguracija je od velike važnosti u raznim primjenama uključujući samo-balansirajuće robote, kolica za bolesnike na dva kotača, analizu stabilnosti višezglobnih sustava itd.
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On-line NNAC for a Balancing Two-Wheeled Robot Using Feedback-Error-Learning on the Neurophysiological Mechanism

On-line NNAC for a Balancing Two-Wheeled Robot Using Feedback-Error-Learning on the Neurophysiological Mechanism

There are many kinds of two-wheeled self-balancing robot, such as wheeled-inverted pendulum, self-balancing wheelchair, JOE, nBot[1 ][ 2 ][ 3 ][ 4 ], and so on. All of them have ability of keeping balanced themselves and running forward. Many researches [3][ 5 ][ 6 ] on the balancing control are done after decoupling. Essentially, it is a kind of control method based on linear inverted pendulum. A lot of work has been done for such an inverted pendulum system. An adaptive output recurrent cerebellar model articulation controller is utilized to control wheeled inverted pendulums (WIPs)[7]. A mobile robot in [8 ] was controlled using a trajectory tracking algorithm based on a linear statespace model. Grasser et al. [3] developed a dynamic model for designing a mobile inverted pendulum controller using a Newtonian approach and linearization method. In [1], a dynamic model of aWIP was created with wheel motor torques as input, accounting for nonholonomic no-slip constraints. Vehicle pitch and position were stabilized using two controllers. Jung and Kim [9] created a mobile inverted
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Motor Learning Based on the Cooperation of Cerebellum and Basal Ganglia for a Self Balancing Two Wheeled Robot

Motor Learning Based on the Cooperation of Cerebellum and Basal Ganglia for a Self Balancing Two Wheeled Robot

In psychology, operant is a class of behavior that pro- duces consequences by operating (i.e., acting) upon the environment. Operant conditioning (OC) is a technique of behavior modification through reinforcement and punishment. The research about operant conditioning theory [34] was started in 1938 by Skinner, a psychology professor. Its consequence influences the occurrence and form of behavior. Operant conditioning and classical conditioning [35] are two main learning ways of associa- tive learning, and all animals, including human, have these two learning way. Operant conditioning is distin- guished from classical conditioning in that operant con- ditioning deals with the modification of operant behavior. Operant conditioning reflects the relation between be- havior and its outcome, and the learning with OC theory is called operant learning (i.e. instrument learning). Re- cently, researchers apply OC theory in the robot learning and control and have done plenty of experimental studies. For example, Björn Brembs et al. [36] from Germany applies himself to the research of the operant condition- ing in flies (Drosophila) and snails. 'Pure' operant condi- tioning and parallel operant conditioning at the flight simulator were studied. Chengwei, Yao et al. [37] ap- plied OC theory into emotion development and presented an emotion development agent model based on OCC
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Controller Design Of A Two-Wheeled Self Balancing Mobile Robot Base On A Simulation View Point

Controller Design Of A Two-Wheeled Self Balancing Mobile Robot Base On A Simulation View Point

Wheeled robots are the robots that can transport themselves form one place to another with the help of their wheels. A robot with wheeled motion can achieve mechanical term easily and with low cost compared to legged mobile robot. In addition, the control of wheeled moving is generally simpler. Due to these reasons, wheeled robots are becoming one of the most frequently seen robots. The types of wheeled mobile robot that have been developed by other researchers will be introduced at the following section.

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Implementation of Two Wheeled Self Balancing Platform

Implementation of Two Wheeled Self Balancing Platform

parts to the integration of electronic, mechanical and software sections. Because of the need to use the knowledge in the fields of mechanics, electronics, programming and control, this project is extremely interdisciplinary and as such one of the most representative mechatronic problems. The stability of the Self Balancing Robot may be improved if a properly designed gearbox that is having negligible gear backlash is used. Further work includes increasing the level of stabilty of the robot. Also Self balancing robot can be connected with a Bluetooth module thus allowing the robot to be controlled while moving . Robot can be also made to avoid obstacles by adding Ultrasonic sensor.
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Comparison of Control Methods PD, PI, and PID on Two Wheeled Self Balancing Robot

Comparison of Control Methods PD, PI, and PID on Two Wheeled Self Balancing Robot

vertical velocity to obtain an estimate of vertical velocity [7]. The Complementary filter is one type of filter that can be employed to combine measurements or filter the IMU (Inertial Measurement Units) readings, which can set the screen orientation based on tilt and angular rate. The IMU itself consists of two main sensors, namely accelerometer and

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Hybrid Control of a Two-Wheeled Automatic-Balancing Robot with Backlash Feature

Hybrid Control of a Two-Wheeled Automatic-Balancing Robot with Backlash Feature

For this work a custom body is built, the objective of the platform is to realize an automatically self-balancing robot system, like the Segway principle. The platform holds two geared DC motors (MicroMotors - E 192), which are supplied with a 12 volt 2.2 A Lithium Ion battery, controlled by a PWM signal generated by a micro controller (PIC32MX795F512) and amplified by two H-bridges (VNH2SP30). The gear ratio is 1:18 and the backlash is measured to ≈ 1.3 ◦ on the geared side and ≈ 22 ◦ on the motor side. The micro controller is an 80 MIPS 32 bit MCU, with 10 bit ADC and 8 bit PWM. The navigational sensors are an accelerometer (ADXL345), a gyroscope (ITG-3200) and an encoder (AM512BD01). The advantage of this platform, is that it is light, which is causing the gearing to suffer badly from backlash effect. A photo of this self-developed robot is illustrated in Figure 1.
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SELF-BALANCING ROBOT

SELF-BALANCING ROBOT

The input to the controller is the error from the system. The Kp, Ki, and Kd are referred as the proportional, integral, and derivative constants (the three terms get multiplied by these constants respectively). The closed loop control system is also referred to as a negative feedback system. The basic idea of a negative feedback system is that it measures the process output y from a sensor. The measured process output gets subtracted from the reference set point value to produce an error. The error is then fed into the PID controller, where the error gets managed in three ways. The error will be used on the PID controller to execute the proportional term, integral term for reduction of steady state errors, and the derivative term to handle overshoots. After the PID algorithm processes the error, the controller produces a control signal u. The PID control signal then gets fed into the process under control. The process under PID control is the two wheeled robot. The PID control signal will try to drive the process to the desired reference set point value. In the case of the two wheel robot, the desired set-point value is the zero degree vertical position. The PID control algorithm can be modelled in a mathematical representation
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Research on Double Cascade Control  Algorithm for Self Balancing  Two Wheeled Vehicle

Research on Double Cascade Control Algorithm for Self Balancing Two Wheeled Vehicle

There are several control schemes of the self-balancing two-wheeled vehicle at home and abroad. A reference scheme of three PID controllers which are linear combined is given by Freescale Smart Car Competition Com- mittee [1]. The two-wheeled robot JOE developed by the Swiss federal university of technology is designed based on optimal control and state-feedback control [2]. The artificial neural network has been used to construct the adaptive controller for the self-balancing two-wheeled robot [3]. On the basis of the first scheme, this article presents a new method of double cascade PID control. The structure of the control system itself greatly reduces the mutual coupling among balance control, speed control and direction control, so that the parameters of the system are easy to be adjusted, What’s more, compared with state-feedback control and advanced intelligent control, it do not require very precise system model, and the complexity of the control method is reduced.
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Application of model reduction for robust control of self-balancing two-wheeled bicycle

Application of model reduction for robust control of self-balancing two-wheeled bicycle

In recent years, research on self-balancing two-wheeled bicycle has been interested by many scientists. In particular, a difficult problem is the study of self-balancing problem of the robot. To solve the problem of balancing two-wheeled bicycle, there are three basic methods as follows; (a) controlling balance by the flywheel, as in the studies of Beznos [1], Xu [2], and Kim [3]. Lee [4] Gallaspy [5], and Suprapto [6]; Thanh [7], (b) controlling balance by centrifugal force as in the study of Tanaka and Murakami [8], and (c) controlling balance by changing the center of gravity as Lee and Ham's research [9]. Among these three methods, control of balance using the flywheel has the advantage of being responsive and can be balanced even when the vehicle is not moving.
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Dynamic Modeling and Construction of a New Two-Wheeled Mobile Manipulator: Self-balancing and Climbing

Dynamic Modeling and Construction of a New Two-Wheeled Mobile Manipulator: Self-balancing and Climbing

effort, stability control of a two- wheeled mobile manipulator is analyzed on the ramps [24]. To proof the aim of this paper, the Lagrange approach is utilized. Then, Lyapunov method is used to determine stability margins. Position and velocity control process based on the optimal controller is done to satisfy stability margins. As the other effort related to the locomotion on ramp, a dynamical simulation is done in [25, 26]. Extracted equations are used to simulate locomotion on even surfaces or ramps. According to optimal control based on the optimal gains, stability of mobile manipulator is investigated. Juang and Lum [27] considered the dynamic simulation of two-wheeled robots by means of using various PID based controllers. On an uneven surface, climbing can improve ability of locomotion. Mobile robots that can climb obstacles, or stairs have been investigated in order to generalize their missions. As a solution, the wheel of mobile manipulator can be transformed to be in [28], the body is knitted to two parts to elevate easier [29]. In other more complex solution, wheels are armed by active linkages [30]. The hybrid locomotion done by complex leg (linkage and wheel) can improve ability of climbing besides legged locomotion. Some of climbers are armed by passive linkages [31, 32]. In other efforts, 1-DOF link is deformed to the half circle shape [33, 34]. Rhex is a novel platform improved to running and climbing [35]. Some other mobile platforms are specially designed for climbing like Msrox [36, 37]. Msrox is a four-wheeled rover armed by hybrid wheels to climb stairs. The hybrid wheel composition is obtained from installing three wheels on a triangular part. Triangular part is located on each wheel connection point on the main body. In other effort, a four-wheeled robot armed by parallel mechanism is proposed to climb stair [38]. In this research, using of linkage is considered to obtain climber mechanism. As a powerful climber mechanism, a rail mobile manipulator is proposed in a hybrid structure. This mechanism is designed based on compounded duty of mobile part and manipulator part in climbing and locomotion missions [49], [40]. The research reported in [39], [40] is the first inspiration for new idea of current development to improve new mechanism proposed at the previous part of introduction. Hybrid locomotion using arm beside mobile parts is a good solution to climbing.
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Self Balancing Robot

Self Balancing Robot

In the past decade, mobile robots have stepped out of the military and industrial settings, and entered civilian and personal spaces such as hospitals, schools and ordinary homes. While many of these robots for civil applications are mechanically stable, such as Aibo the Sony robotic dog, or four-wheel vacuum cleaners, one that ordinary on-lookers would find awe-inspiring is the Segway personal transport, a mechanically unstable, two-wheel self-balancing vehicle that has seen deployment for law-enforcement, tourism, etc. This vehicle can be rightfully called a robot because, without the sensory capability and intelligent control that accompany every robot, the Segway can never stay upright. While Segway may have been a well-known commercial product, research into the control of such a mechanical system has been diverse. A two-wheel self-balancing robot is very similar to the inverted pendulum, which is an important testbed in control education and research; see, for example [1], [2] . Besides the development of Segway, studies of two-wheel self-balancing robots have been widely reported. For example, JOE [3] and nBot [4] are both early versions complete with inertia sensors, motor encoders and on-vehicle microcontrollers. See also an updated reference at the nBot website [4] . Since then, there has been active research on the control design for such platforms, including classical and linear multivariable control methods [3], [5], [6], [7] ,nonlinear back stepping controls [8], [9] , and combinations of the above [10] . A related and interesting work that is worth mentioning concerns balancing of a four wheeled vehicle on its two side-wheels, using classical control [12] . One of the key enabler for this research in the academia is arguably the increasing affordability of commercial off-the shelf
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The Development of Self Balancing Controller for One Wheeled Vehicles

The Development of Self Balancing Controller for One Wheeled Vehicles

In recent years, because of the surging consciousness of global pollution and energy-shortage crises, automobiles and motorcycles are no longer the best for transportation. In order to fit the daily required and improve above problems, exploring new energy or developing lighter and innovative mobile carriers are beginning to be known as new trends. The earliest two-wheeled balanc- ing robot was published in 1987 by Prof. Yamafuji [1]. From then on, the concerning researches with this topic have been increasing [2-6] and have even been a com- mercialized product. For example, the Human Trans- porter, was developed by Segway Co., U.S.A., which is a very famous two-wheeled balancing vehicle [7,8]. In addition, NASA’s Robonaut, Segway platform puts ro- bots in motion, is now aim in the military projects [9].
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Modelling and control of DWR 1.0 – a two wheeled mobile robot

Modelling and control of DWR 1.0 – a two wheeled mobile robot

A two-wheel mobile robot is one of the applications for the inverted pendulum system. Inverted pendulum is a classical model of under-actuated, non-linear and unstable system. Hence, control of a two-wheel mobile robot is very challenging as it is non-linear, unstable and uncontrollable system. Research and study on the controlling self-balancing two-wheel robot has contributed to the practical interest which is the applications on the vehicle field and autonomous robotics. Many practical systems have been implemented based on the two-wheel self-balancing robot models [1]. Among these applications, Segway PT has been a popular personal transporter since invented in 2001[2].
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Robust Navigational Control of a Two-Wheeled Self-Balancing Robot in a Sensed Environment

Robust Navigational Control of a Two-Wheeled Self-Balancing Robot in a Sensed Environment

ABSTRACT This research presents an improved mobile inverted pendulum robot called Two-wheeled Self-balancing robot (TWSBR) using a Proportional-Derivative Proportional-Integral (PD-PI) robust control design based on 32-bit microcontroller in a sensed environment (SE). The robot keeps itself balance with two wheels and a PD-PI controller based on the Kalman filter algorithm during the navigation process and is able to stabilize while avoiding acute and dynamic obstacles in the sensed environment. The Proportional (P) control is used to implement turn control for obstacle avoidance in SE with ultrasonic waves. Finally, in a SE, the robot can communicate with any of the Internet of Things (IoT) devices (mobile phone or Personal Computer) which have a Java-based transmission application installed and through Bluetooth technology connectivity for wireless control. The simulation results prove the efficiency of the proposed PD-PI controller in path planning, and balancing challenges of the TWSBR under several environmental disturbances. This shows an improved control system as compared to the existing improved Adaptive Fuzzy Controller.
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Two wheeled balancing robot controller designed using pid

Two wheeled balancing robot controller designed using pid

11 enough for robot’s balance. When the appropriate Kp and Ki gain values are chosen for PI controller, it has been observed that the robot can balance itself for a short time and try to maintain its balance by swinging. In addition, when PID controller is applied, the two-wheel robot can stand in upright position longer compare to the previous two cases. This can be happen if only appropriate value of Kp, Ki, and Kd gain are chosen. Meanwhile, Nasir [13] states that PID controller capable to control the nonlinear inverted pendulum system angular and linear position in Matlab Simulink. However, PID controller should be improved so that the maximum overshoot for the linear and angular positions do not have high range as required by the design. W.An [14] claim that Matlab can be used to compare the performance of PID Controller and Linear- quadratic regulator (LQR) in controlling two-wheeled self-balancing robot. It is concluded that LQR has a better performance than PID in self-balancing control in term of the time to achieve the steady state of robot.
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A Comparative Study of Controllers for a Two Wheeled Self-balancing Robot

A Comparative Study of Controllers for a Two Wheeled Self-balancing Robot

Two wheeled self-balancing vehicle based on the concept of an inverted pendulum is built by researchers at the industrial electronics laboratory. SEGWAY PT is such a one machine developed by Dean Kamen, now commercially obtainable as a battery-powered electric vehicle in the market. Researchers and engineers are working to develop techniques to make a dynamically stable system and to guarantee desired performance and robust solution. Many methods are applied and tested on this system platform. Dual-PID and LQR control techniques are designed and tested in Simulink and analysed for vertical balance and position control [1]. There are many past studies about the stabilization and optimization of two-wheeled inverted pendulum robots. They are state feedback control with pole placement method [2], Proportional-Integral- Derivative (PID) and Proportional-Derivative(PD) controllers, LQR [1], [3] , Model Predictive Control (MPC) . Kalman filtering and PID algorithm is used for a two wheeled car [4]. PI control is not satisfactory for a two wheeled self-balancing robot to act in a real time application. Different new research works has found on inverted pendulum techniques in the implementation of bipedal locomotion [6], [7]. This paper presents LQGand H-infinity mixed sensitivity design for a two wheeled self-balancing robot. Section two presents system modeling. Section three presents the control techniques. The simulation results are discussed in section four. Conclusions of the work are drawn in section five.
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Modeling, Simulation, and Optimal Control for Two-Wheeled Self-Balancing Robot

Modeling, Simulation, and Optimal Control for Two-Wheeled Self-Balancing Robot

This paper concerns the two-wheeled self-balancing robot system as the research object, which uses the Newtonian mechanics equation method to derive the dynamic equation. The linear state-space model that approximates the nonlinear system in the region of operation than obtained by assuming the system operates only around an operating point and the signals involved are small signal. Based from the mathematical model of the system, LQR Controller is designed to control the system tilt angle and heading angle so that the system can be controlled to move to a desired position. Performance of control strategy with respect to the output tilt angle ( ) and heading angle ( ) are examined and presented by using matlab / Simulink program. 2. RESEARCH METHOD
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Fabrication, Balancing and Analysis of Two Wheeled Robot

Fabrication, Balancing and Analysis of Two Wheeled Robot

Many people are researching about the two wheeled self balancing robot now a days and their main area is becoming the balancing concept of the robot rather the robotics platform or the path simulation of the robot. The first two wheeled robot popularly known as the segway Human Transporter is inverted by the Dean Kamen an American entrepreneur. It is a commercial vehicle where one person can drive the vehicle with maximum of 20 km per hours. Now a day’s people are using it as roaming purpose in different shopping mall and in Small Park. It is also being used by the police for patrolling purpose in many counties. As it is a commercial product, the safety and performance has taken into deep concentration.
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Controlling of Two Wheeled Self Balancing Robot using PID

Controlling of Two Wheeled Self Balancing Robot using PID

The physics for this robot is simple: the robot stands in two points lined with the wheel, and it tends to fall vertically. The movement of the wheel in the direction of the falling raises the robot to recover the vertical position. The vehicle attempts to correct for an induced lean angle by moving forward or backward, and the goal is to return itself to vertical or at least not fall over. For that objective we have two things to do; on one hand we have to measure the angle of inclination (Roll) of the vehicle, and on the other hand we have to control the motors for going forward or backward to make that angle θ, maintaining a vertical position.
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