Intelligent controllers for velocity tracking of two wheeled inverted pendulum mobile robot

Velocity tracking is one of the important objectives of vehicle, machines and mobile robots. A two wheeled inverted pendulum (TWIP) is a class of mobile robot that is open loop unstable with high nonlinearities which makes it difficult to control its velocity because of its nature of pitch falling if left unattended. In this work, three soft computing techniques were proposed to track a desired velocity of the TWIP. Fuzzy Logic Control (FLC), Neural Network Inverse Model control (NN) and an Adaptive Neuro-Fuzzy Inference System (ANFIS) were designed and simulated on the TWIP model. All the three controllers have shown practically good performance in tracking the desired speed and keeping the robot in upright position and ANFIS has shown slightly better performance than FLC, while NN consumes more energy

A Two-Wheeled Vehicle Navigation System Based on a Fuzzy Logic Controller

The paper deals with a two-wheeled vehicle,namely ESG-2 (Extended Segway-like Generation- 2) navigation control system using a fuzzy logic controller. The vehicle employs two wheels left and right independently which are controlled independently using a fuzzy logic controller respectively. The controllers deal with a compact and implementable application for the normal using with a person (human with 60kg weight in average) loaded on the vehicle. A modified infrared-based range sensor system is applied to the vehicle as a tilt sensor and it is incorporated with an accelerometer to control its response in case of the dynamics disturbances. The fuzzy controller runs in tilt-mode while a reference tilt using a potentiometer (as steer system) is taken into account for navigating the vehicle. From the simulation using MATLAB @ and experiments it is obvious that the prototype of ESG-2 is quite challenging to be developed in the future

A Fuzzy LQR PID Control for a Two-Legged Wheel Robot with Uncertainties and Variant Height

This paper proposes a fuzzy LQR PID control for a two-legged wheeled balancing robot for keeping stability against uncertainties and variant heights. The proposed control includes the fuzzy supervisor, LQR, PID, and two calibrations. The fuzzy LQR is conducted to control the stability and motion of the robot while its posture changes with respect to time. The fuzzy supervisor is used to adjust the LQR control according to the robotic height. It consists of one input and one output. The input and output have three membership functions, respectively, to three postures of the robot. The PID control is used to control the posture of the robot. The first calibration is used to compensate for the bias value of the tilting angle when the robot changes its posture. The second calibration is applied to compute the robotic height according to the hip angle. In order to verify the effectiveness of the proposed control, a practical robot with the variant height is constructed, and the proposed control is embedded in the control board. Finally, two experiments are also conducted to verify the balancing and moving ability of the robot with the variant posture

Synchronizing of Stabilizing Platform Mounted on a Two-Wheeled Robot

This paper represents the designing, building, and testing of a self-stabilizing platform mounted on a self-balancing robot. For the self-stabilizing platform, a servo motor is used and for the self-balancing robot, two dc motors are used with an encoder, inertial measurement unit, motor driver, an Arduino UNO microcontroller board. A PID controller is used to control the balancing of the system. The PID controller gains (Kp, Ki, and Kd) were evaluated experimentally. The value of the tilted angle from IMU was fed to the PID controller to control the actuated motors for balancing the system. For the self-stabilizing control part, whenever the robot tilted, it maintained the horizontal position by rotating that much in the opposite direction

A novel configuration of two-wheeled self-balancing robot

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.A novel design of the two-wheeled self-balancing robot (TWSR) was presented in this research. The robot was called two-wheeled self-balancing vehicle-pendulum system (TWSVPS). Compared with TWSR, the TWSVPS has a single pendulum (SP), which was passively jointed to the robot body through the O1 axis. The difficulty and complexity of control and modelling were increased due to the additional degree of freedom (DOF) which was offered by the SP rotated around the O1 axis. To simplify the analysis, the TWSVPS can be considered to be the mobile double inverted pendulum. Lagrangian dynamic formulation is used to derive the system dynamics due to its relative simplicity. The parallel double fuzzy controller based on information fusion technology was designed and simulated in MATLAB. The results show that the method is feasible and TWSVPS has excellent moving stability. The new developed configuration is of great importance in various applications including self-balance robots, wheelchairs on two wheels, stability analysis of multi links system etc

Particle swarm optimization and spiral dynamic algorithm-based interval type-2 fuzzy logic control of triple-link inverted pendulum system: A comparative assessment

This paper presents investigations into the development of an interval type-2 fuzzy logic control (IT2FLC) mechanism integrated with particle swarm optimization and spiral dynamic algorithm. The particle swarm optimization and spiral dynamic algorithm are used for enhanced performance of the IT2FLC by finding optimised values for input and output controller gains and parameter values of IT2FLC membership function as comparison purpose in order to identify better solution for the system. A new model of triple-link inverted pendulum on two-wheels system, developed within SimWise 4D software environment and integrated with Matlab/Simulink for control purpose. Several tests comprising system stabilization, disturbance rejection and convergence accuracy of the algorithms are carried out to demonstrate the robustness of the control approach. It is shown that the particle swarm optimization-based control mechanism performs better than the spiral dynamic algorithm-based control in terms of system stability, disturbance rejection and reduce noise. Moreover, the particle swarm optimization-based IT2FLC shows better performance in comparison to previous research. It is envisaged that this system and control algorithm can be very useful for the development of a mobile robot with extended functionality