Robotics is currently a rapid growing branch of technology due to the high demand for seamless and automatic system. During recent year robotics has also made a breakthrough in personal transportation with the segway. Twowheeledrobot is flexible vehicle that balance on two wheels and can be controlled through the motion of tilting in the direction on which to travel. Conceptually, it can be considered a modern adaptation of the inverted pendulum, which is an upside down pendulum with its centre of mass located above its pivoted point. The goal of the classical inverted pendulum is to keep the pendulum stable by applying correct amount of force or torque to its base, cancelling out any downward acceleration that the gravitational force has o the pendulum. However, unlike the classical pendulum, the segway personal transporter, with the help of an embedded control system and a movable base, is also able to freely drive and turn in all directions and maintains balance at all speed, this is achieved with the help of responsive sensor and advance control algorithm.
Abstract—Research on balancingtwo-wheeledrobot has gained interest among researchers due to its highly nonlinear characteristic. The objective of this project is to control the stability of a two-wheeled EV3 Lego robot and maintain in the upright position while performing linear motion as well steering right/left and moving up/down a ramp. In this project, a two- wheeledbalancing Lego EV3 robot is modelled in a state space and controlled by a PID controller. The robot is controlled in real time via Matlab/Simulink interface using Graphical User Interface (GUI) and the robot performance can be observed wirelessly at the same time using Wi-Fi connection between the robot and MATLAB. The two-wheeled EV3 Lego robot is able to stay in the upright position while performing steering and going up/down the ramp. The analysis of the system plant has been made in terms of overshoot, settling time of tilt angle stabilization using simulation approach and successfully controlled using real two-wheeled EV3 hardware.
The goal of  was to develop a controlled robot that can move with only two wheels. They elaborately discussed the design and evaluation of a robotic chassis through the appli- cation of Lego Mindstorm NXT , and to be controlled by the AVR ATMega16 microcontroller. Their experiment shows that a robot chassis must address stability and mechan- ical issues.  invented a famous balancingrobot called Segway, which can keep its balance with someone’s standing on its platform. It uses brushless DC electric motors in the wheels powered by lithium-ion batteries with encoders and gyroscopic sensors to check the pitch in order to be upright. JOE is a self-balancingrobot based on the inverted pendulum which was designed by the Industrial Electronics Laboratory at the Swiss Federal Institute of Technology. It was con- trolled by a Digital Signal Processor (DSP) IC and used the feedback of a controller. When running, the maximum of speeds reaches 1.5m/s. The paper called ‘Two-wheel self- balanced car based on Kalman filtering and PID algorithm’ presented by  and  shows that a low-cost acceleration
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  was started in 1938 by Skinner, a psychology professor. Its consequence influences the occurrence and form of behavior. Operant conditioning and classical conditioning  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.  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.  ap- plied OC theory into emotion development and presented an emotion development agent model based on OCC
An important feature of the Central Nervous System (CNS) is its remarkable ability to adapt to changes both in the environment and inside the body. Motor nervous system includes motor center (MC) and PNS (Peripheral Nervous System). The whole nervous system can also be called sensorimotor nervous system. Motor nervous system is composed of three-level hierarchical structure and two auxiliary monitor systems. From low to high the three-level hierarchical structure is respectively the spinal cord (SC), brainstem (BS) and cerebral cortex (CC), while two auxiliary monitor systems mainly are the cerebellum and ganglion nuclear group in the forebrain area as the core. These brain areas related to motor control form interconnected circuit, and deal with all kinds of information processing of motion and postures. Figure 1(a) shows the regulation and control mode of behavior central; Figure 1(b) shows the global network of central nervous system linking the cerebellum, the basal ganglia and the cerebral cortex. the CB, the BG, and the CC exist for different types of learning.
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.
vertical velocity to obtain an estimate of vertical velocity . 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
Abstract— Two-wheeledbalancingrobot is a mobile robot that has helped various human’s jobs such as the transportations. To control stability is still be the challenges for researchers. Three equations are obtained by analyzing the dynamics of the robot with the Newton approach. To control three degrees of freedom (DOF) of the robot, PIDs is tuned automatically and optimized by multivariable Modified Particle Swarm Optimization (MPSO). Some parameters of the PSO process are modified to be a nonlinear function. The inertia weight and learning factor variable on PSO are modified to decreasing exponentially and increasing exponentially, respectively. The Integral Absolute Error (IAE) and Integral Square Error (ISE) evaluate the error values. The performances of MPSO and PSO classic are tested by several Benchmark functions. The results of the Benchmark Function show that Modified PSO proposed to produce less error and overshoot. Therefore, the MPSO purposed are implemented to the plant of balancingrobot to control the angle, the position, and the heading of the robot. The result of the simulation built shows that the MPSO – PID can make the robot moves to the desired positions and maintain the stability of the angle of the robot. The input of distance and angle of the robot are coupling so MPSO needs six variable to optimize the PID parameters of balancing and distance control.
In this work, a two-wheeled mobile robot has been successfully developed, and this paper present development of the mathematical model. The mathematical model for the robot has to be developed based on the available system and hardware. By using the system identification approach, analysis of the physical platform, and the fundamental laws of mechanics, a non- linear model of the system can be developed. This is useful as various control techniques can be tested and simulated by using the developed model.
This section is based on three main efforts. The first case is simulation of the robot on flat surface using Matlab Simulink (Sim-Mechanic) and its control by means of Eq. 9. The control panel of Simulink is provided in Fig. 25 of Appendix. In the second test, it is simulated in the dynamic simulation part of SolidWorks to show the ability of robot in ramp climbing. The last effort is done experimentally. In these stages, two parts are provided. The first is obstacle crossing and the second is ramp climbing. At the first stage, robot starts to move by initial condition (v = 0, a = constant and x = 0) for 8 seconds, see Fig. 9. The aim is monitoring the fluctuation of main body with respect to the fixed coordinate. Manipulator motors are supposed to be fixed. The results of angular displacement are illustrated in Fig. 10-a, for various damping coefficient of the wheel joint (B = 0.6, 1.2, 2). By choosing B = 2 according to Fig. 10-a, Fig. 10-b illustrates fluctuation of the main body with respect to the fixed coordinate system for various linear accelerations of the wheel (a = 1, 3, 5 m/s 2 ).
The TWIP mobile robot has many outputs, tilt angle, tilt rate, position and velocity. Yaw angle and the yaw rate are also considered depending on the application. The horizontal velocity of the robot is taken as the output of the robot since it is the desired manipulated variable. The data was taken when in closed loop form since the robot is open loop unstable. Simple PID controller was used for balancing and the model was simulated in MATLAB Simulink environment and the data for the neural network training was acquired and use for training the inverse model as shown in Figure 4. A two layered feedforward back propagation network with 10 weights was used. A sigmoid transfer function in the first layer and purelin transfer function in the last layer were chosen. Levenberg-Marquardt back propagation algorithm was used in the training of the network. An MSE of 0.16779 was achieved after 252 iterations.
There are several control schemes of the self-balancingtwo-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 . The two-wheeledrobot JOE developed by the Swiss federal university of technology is designed based on optimal control and state-feedback control . The artificial neural network has been used to construct the adaptive controller for the self-balancingtwo-wheeledrobot . 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.
The paper has developed, modeled a two-wheeled self-balancing bicycle model and designed a robust controller to control the balance of two-wheeled bicyle. The paper also introduces the stochastic balanced truncation algorithm based on Schur analysis and applies this algorithm to reduce the high order robust controller using to control the balance of two-wheeled bicyle. In particularly, the reduced 4 th and 5 th order controller can replace the original controller (30 th -order) while the performance of the control system is ensured. Using the reduced controller simplify the program, so the computational time is reduced. Therefore, the system respose is improved, and the requirements in real-time application are met. The simulation results show the correctness of the model reduction algorithm and the robust control algorithm of two-wheeled self-balancingtwo-wheeled bicycle.
large areas and negotiate stair-like obstacles. But, jump- ing is passive, and there is no control of airborne attitude. In 2008, Kikuchi et al.  introduced a wheeled-based robot that climbs up and down stairs dynamically. A spring-loaded movable upper body mass allows their robot to land softly and double-hop in midair. Kikuchi robot consists of a statically stable wheel base. One of the drawbacks of this is that the robot has to land with minimum body tilt angle to ensure a successful landing. Safe landing is not guaranteed if there exists any exter- nal disturbances during airborne. Another jumping robot named iHop is a transformable twowheeledrobot. In hoping mode, it uses both wheels as weights and has a lockable hopping mechanism. iHop pushes its wheels upward while balancing on the central chassis. iHop exhibits hoping capability but it is not shown that the robot is capable of climbing up or down step terrains.
A balancing transporter is a platform attached to a set of two independent wheels that is controlled by a DC motor. The platform attached to the wheel to make the system behave as inverted pendulum. Figure 3.2 shows the design layout block diagram to these features which is a common problem in engineering controls and processes to test a different control systems. Tilt angle measurement is implemented by inertial measurement unit (IMU) that consisting of gyroscope and accelerometer. Variable used to control steering motion steer either left or right. Gain variable is used to tune the signal response between the controller and the motor driver. Board controller process the input signal with a complementary filter and converting to speed PWM depending on an angle measurement. The angle manipulated by the controller to estimate the correct speed to compensate the platform and send it to the motor driver module by serial communication protocol. At the same time, the data from the controller processed and transmitted via wireless communication to Matlab GUI for performance analysis. The main goal of this process is to move the wheel in a certain position while keeping the center of mass of the system in an upright position.
As regards the corridor and wall-following navigation problem, some control algorithms based on artificial vision have been proposed. In , image processing is used to detect perspective lines to guide the robot along the centre axis of the corridor. In , two lateral cameras mounted on the robot are used, and the optical flow is computed to
The robot comprises of single foam platform mounted on the two DC motors of 300 rpm. The HW Hi- watt 9v battery is mounted on the top of the foam sheet. The Arduino Uno and L298N are mounted on the foam sheet . The IMU sensor was kept at lower platform made of a thin strip of foam kept over the two DC motors in between the so as to keep it as immune to vibrations as possible. The wheels used were generic robot wheels.
This project concerns the development of a mobile robot with a platform, which can be levelled using PID controller. The main objective is to control the flatness of the platform efficiently with a low cost hardware without limiting the strength and performance of the whole system. There are various stages that have been used to stabilize the platform such as modelling the system, obtaining the data from sensors and determining how the control algorithms will be implemented. V.J. Van Doren (2009) suggested a twowheeledrobot to perform the balancing and control of mobile robots. In this project the Proportional, Integral, Derivative (PID) has been implemented to control the flatness of a mobile robot platform. PID has proven to be popular among the control engineering community. As stated by the author of article Vance J. Van Doren (2009), “For more than 60 years after the introduction of Proportional-Integral-Derivative controllers, remain the workhorse of industrial process control”
It can be concluded that, after implementation of this project, the stability of Auto-BalancingTwoWheeled Inverted Pendulum Robot can be achieved with improved response time. We have considered the system of robot as a classical control system and proposed the algorithmic approach to stabilize the system. Through this paper we are implementing Kalman filter algorithm and PID controller digital control algorithm on a micro-controller, which gives cost-effective option for solving Inverted Pendulum control system problem with reduced oscillations and improved stability. Our emphasis is on achieving zero degree vertical equilibrium of robot body when it is in rest or when it is in a straight line motion, in shortest possible time. Further study can be done on achieving the stability of robot while rotating about its vertical axis.
The word balance means the inverted pendulum is in equilibrium state, which its position is like standing upright 90 degrees. However, the system itself is not balance, which means it keeps falling off, away from the vertical axis. Therefore, a gyro chip is needed to provide the angle position of the inverted pendulum or robot base and input into the microcontroller, which the program in itself is a balancing algorithm. The experiment mainly uses PID controller, having gainsK p , K i and K d . PID provides correction between desired value and actual value. The difference between input and