Figure 4 - uploaded by Adnan Osmanović
Content may be subject to copyright.
Contexts in source publication
Context 1
... dynamic model of the used robot is mainly based on the inverted pendulum, where its center of mass is above the pivot point (Fig. ...
Context 2
... m i , I i are masses and moments of inertia of elements, i ∈ {1, 2, 3, 4, 5}, and r 0 , r 1 , d 2 , d 3 , d 4 and d 5 are parameters presented in Fig. 4. In (3) we have included the kinetic energy of elements due to their translation and rotation. Using tables for calculation of the moment of inertia and the parallel axis theorem [21], we can approximate moments of inertia for each element shown in Fig. 4. These approximations are expressed as follows: Based on the model shown in Fig. ...
Context 3
... i ∈ {1, 2, 3, 4, 5}, and r 0 , r 1 , d 2 , d 3 , d 4 and d 5 are parameters presented in Fig. 4. In (3) we have included the kinetic energy of elements due to their translation and rotation. Using tables for calculation of the moment of inertia and the parallel axis theorem [21], we can approximate moments of inertia for each element shown in Fig. 4. These approximations are expressed as follows: Based on the model shown in Fig. 4 the following equations are ...
Context 4
... in Fig. 4. In (3) we have included the kinetic energy of elements due to their translation and rotation. Using tables for calculation of the moment of inertia and the parallel axis theorem [21], we can approximate moments of inertia for each element shown in Fig. 4. These approximations are expressed as follows: Based on the model shown in Fig. 4 the following equations are ...
Similar publications
Citations
... When an object is accelerating, the acceleration of the device can be determined by the inertia force, which is directly proportional to the acceleration. Similarly, when an object is in rotation, the gyroscopic force will come into play to ascertain the angular velocity of the device [6][7]. Therefore, the IMU sensor can be applied for any application in navigation, virtual reality, and sports [8]. ...
Nowadays, the research on a two-wheeled self-balancing robot is an active area of research especially in terms of design as well as control to continue the innovation applications of robots in the future. Most of the two-wheeled self-balancing robots are designed based on an inverted pendulum system for stability and maneuverability. The aim of this paper is to propose the fuzzy PD controller to control and maintain its balance on the two wheels. A sensor of the Inertial Measurement Unit (IMU) was used as an input to evaluate and obtain the position and orientation of the robot. The control algorithms for the robot also are designed to keep the pendulum upright. Then, the fuzzy PD concept was applied to correct the error between the desired set point and the actual tilt angle position to adjust the speed of the motor accordingly. The results obtained from this controller were capable of maintaining the balancing of the robot by using an experimental method of PID tuning. The prototype of the two-wheeled self-balancing robot was implemented with Arduino Uno and a fuzzy PD controller. However, the limitation of the project is the longer size and heavier weight of the robot are less stable, then a better controller is needed to balance the robot.
... The linear control approaches on the other hand are relatively simpler in terms of their implementations on hardware. The most popular methods are proportional-integral-derivative (PID) [22]- [24] and linear quadratic regulator (LQR) schemes [25]. Nevertheless, a notable downside of the PID control algorithm for controlling the TWSBR is the difficulty of parameter tuning [26], [27]. ...
p>A two-wheeled self-balancing robot (TWSBR) is an underactuated system that is inherently nonlinear and unstable. While many control methods have been introduced to enhance the performance, there is no unique solution when it comes to hardware implementation as the robot’s stability is highly dependent on accuracy of sensors and robustness of the electronic control systems. In this study, a TWSBR that is controlled by an embedded NI myRIO-1900 board with LabVIEW-based control scheme is developed. We compare the performance between proportional-integral-derivative (PID) and linear quadratic regulator (LQR) schemes which are designed based on the TWSBR’s model that is constructed from Newtonian principles. A hybrid PID-LQR scheme is then proposed to compensate for the individual components’ limitations. Experimental results demonstrate the PID is more effective at regulating the tilt angle of the robot in the presence of external disturbances, but it necessitates a higher velocity to sustain its equilibrium. The LQR on the other hand outperforms PID in terms of maximum initial tilt angle. By combining both schemes, significant improvements can be observed, such as an increase in maximum initial tilt angle and a reduction in settling time.</p
