Robust Control Theory Based Performance Investigation of an Inverted Pendulum System using Simulink

In this paper, the performance of inverted pendulum have been Investigated using robust control theory. The robust controllers used in this paper are H∞ Loop Shaping Design Using Glover McFarlane Method and mixed H∞ Loop Shaping Controllers. The mathematical model of Inverted Pendulum, a DC motor, Cart and Cart driving mechanism have been done successfully. Comparison of an inverted pendulum with H∞ Loop Shaping Design Using Glover McFarlane Method and H∞ Loop Shaping Controllers for a control target deviation of an angle from vertical of the inverted pendulum using two input signals (step and impulse). The simulation result shows that the inverted pendulum with mixed H∞ Loop Shaping Controller to have a small rise time, settling time and percentage overshoot in the step response and having a good response in the impulse response too. Finally the inverted pendulum with mixed H∞ Loop Shaping Controller shows the best performance in the overall simulation result

ROS-based Controller for a Two-Wheeled Self-Balancing Robot

In this article, a controller based on a Robot Operating System (ROS) for a two-wheeled self-balancing robot is designed. The proposed ROS architecture is open, allowing the integration of different sensors, actuators, and processing units. The low-cost robot was designed for educational purposes. It used an ESP32 microcontroller as the central unit, an MPU6050 Inertial Measurement Unit sensor, DC motors with encoders, and an L298N integrated circuit as a power stage. The mathematical model is analyzed through Newton-Euler and linearized around an equilibrium point. The control objective is to self-balance the robot to the vertical axis in the presence of disturbances. The proposed control is based on a bounded saturation, which is lightweight and easy to implement in embedded systems with low computational resources. Experimental results are performed in real-time under regulation, conditions far from the equilibrium point, and rejection of external disturbances. The results show a good performance, thus validating the mechanical design, the embedded system, and the control scheme. The proposed ROS architecture allows the incorporation of different modules, such as mapping, autonomous navigation, and manipulation, which contribute to studying robotics, control, and embedded systems

Semi-Adaptive Control Systems on Self-Balancing Robot using Artificial Neural Networks

A self-balancing type of robot works on the principle of maintaining the balance of the load's position to remains in the center. As a consequence of this principle, the driver can go forward reverse the vehicle by leaning in a particular direction. One of the factors affecting the control model is the weight of the driver. A control system that has been designed will not be able to balance the system if the driver using the vehicle exceeds or less than the predetermined weight value. The main objective of the study is to develop a semi-adaptive control system by implementing an Artificial Neural Network (ANN) algorithm that can estimate the driver's weight and use this information to reset the gain used in the control system. The experimental results show that the Artificial Neural Network can be used to estimate the weight of the driver's body by using 50-ms-duration of tilt sensor data to categorize into three defined classes that have been set. The ANN algorithm provides a high accuracy given by the results of the confusion matrix and the precision calculations, which show 99%.A self-balancing type of robot works on the principle of maintaining the balance of the load's position to remains in the center. As a consequence of this principle, the driver can go forward reverse the vehicle by leaning in a particular direction. One of the factors affecting the control model is the weight of the driver. A control system that has been designed will not be able to balance the system if the driver using the vehicle exceeds or less than the predetermined weight value. The main objective of the study is to develop a semi-adaptive control system by implementing an Artificial Neural Network (ANN) algorithm that can estimate the driver's weight and use this information to reset the gain used in the control system. The experimental results show that the Artificial Neural Network can be used to estimate the weight of the driver's body by using 50-ms-duration of tilt sensor data to categorize into three defined classes that have been set. The ANN algorithm provides a high accuracy given by the results of the confusion matrix and the precision calculations, which show 99%

Fuzzy adaptive control of a two-wheeled inverted pendulum

Recently, the two-wheeled inverted pendulum has drawn the attention of robotic community in view of a plethora of applications, such as transport vehicles: Segway, teleconferencing robots, and electronic network-vehicle. As a widely-used personal transportation vehicle, a two-wheeled inverted pendulum robot has the advantages of small size and simple structure. Moreover, with the advent of modern control technology, these kinds of platforms with safety features and sophisticated control functions can be cost down, so that they have high potential to satisfy stringent requirements of various autonomous service robots with high speed. At the same time, it is of great interest from control point of view as the inverted pendulum is a complicated, strongly coupled, unstable and nonlinear system. Therefore, it is an ideal experimental platform for various control theories and experiments. To understand such a complex system, the Lagrangian equation has been introduced to develop a dynamic model. And following the mathematical model, linear quadratic regulator control and fuzzy adaptive method are proposed for upright stabilization, velocity control and position control of the system. However, sometimes these kinds of robots need to move on a slope, so an advanced linear quadratic regulator controller and a modified fuzzy adaptive controller have been proposed to achieve position control on a slope for the robot while stabilizing its body in balance. In addition, trajectory tracking control using proportional integral derivative control and sliding mode control with fuzzy adaptive backstepping method is also designed to make the robot autonomously navigate in two dimensional plane. Simulation results indicate that the proposed controllers are capable of providing appropriate control actions to steer the vehicle in desired manners. Then, a couple of real time experiments have been conducted to verify the the effectiveness of the developed control strategies

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

Decoupled fuzzy sliding-mode balance control of wheeled inverted pendulums using an 8-bit microcontroller

Fuzzy control, sliding-mode control, decoupled sliding surface, microcontroller, wheeled inverted pendulum[[abstract]]A wheeled inverted pendulum (WIP) system is a typical unstable complex nonlinear system widely utilized for educational purposes and control research. The dynamic of a WIP system can be represented as two second-order subsystems which represent the angle of body and the position of wheel. This paper proposes a decoupled fuzzy sliding-mode balance control (DFSBC) system based on a time-varying sliding surface for a WIP system.[[incitationindex]]EI[[conferencetype]]國際[[conferencedate]]20120314~20120316[[booktype]]電子版[[iscallforpapers]]Y[[conferencelocation]]China,Hong Kon

Decoupled fuzzy sliding-mode balance control of wheeled inverted pendulums using an 8-bit microcontroller

[[abstract]]A wheeled inverted pendulum (WIP) system is a typical unstable complex nonlinear system widely utilized for educational purposes and control research. The dynamic of a WIP system can be represented as two second-order subsystems which represent the angle of body and the position of wheel. This paper proposes a decoupled fuzzy sliding-mode balance control (DFSBC) system based on a time-varying sliding surface for a WIP system. A decoupled sliding surface which includes the information of two-subsystem is designed to make the state trajectories of both subsystems move toward their sliding surface and then simultaneously approach zeros. The control effort of a WIP system is generated based on the idea that the state can quickly reach the decoupled sliding surface without large overshoot. Moreover, the slope of the decoupled sliding surface is adjusted by a fuzzy system, whose fuzzy rules are constructed based on the idea that the convergence time of the state trajectories can be reduced. Finally, an 8-bit microcontroller-based WIP system is setup. Experimental results show that the proposed DFSBC system can achieve favorable balance control response for the simultaneous control of the angle of body and the position of wheel.[[conferencetype]]國際[[conferencedate]]20120314~20120316[[booktype]]紙本[[iscallforpapers]]Y[[conferencelocation]]Hong Kon

Interval type-2 fuzzy logic control optimize by spiral dynamic algorithm for two-wheeled wheelchair

The reconfiguration of the two-wheeled wheelchair system with movable payload has been investigated within the current study towards permitting multi-task operations; through enhanced maneuverability on a flat surface under the circumstances of disturbance rejections during forward and backward motions, as well as motions on the inclined surface for uphill and downhill motions; while having height extensions of the wheelchair’s seat. The research study embarks on three objectives includes developing Interval Type-2 Fuzzy Logic Control (IT2FLC) as the control system, design a Spiral Dynamic Algorithm (SDA) for IT2FLC in stabilizing the designed double-link twowheeled wheelchair system, and optimize the input-output gains and control parameters. The two-wheeled system gives lots of benefits to the user such as less space needed to turn the wheelchair, able to move in the narrow spaces, having eye-to-eye contact with normal people, and can reach stuff on the higher shelve. However, the stability of the twowheeled system will produce high fluctuations due to the uncertainties while stabilizing the system in the upright position. Indirectly, it also caused the long travelled distance and high magnitude of tilt angle and torque. Thus, IT2FLC has been proposed as the compatible control strategy for disturbance rejections to overcome uncertainties for enhanced system stability in the upright position. Basically, IT2FLC uses a Type-2 Fuzzy Set (T2FS) and its membership function (MFs) composed of the lower MFs, upper MFs, and footprint of uncertainty (FOU). This is the reason that IT2FLC possessing the ability to handle cases of nonlinearities and uncertainties that occur in the system. Therefore, any disturbances that give at the back of the seat can be eliminated using the proposed controller, IT2FLC. Additionally, SDA implemented within the control strategy to acquire optimal values of the IT2FLC input-output control gains and parameters of its MFs further accommodated extensive fluctuations of the two-wheeled system; thus, ensuring a safe and comfortable experience among users via shorter traveled distance and lower magnitude of torques following disruptions. The two-wheeled wheelchair is designed using SimWise 4D software to subduing shortcomings of a linearized mathematical model where lengthy equation with various assumptions is required to represent the proposed system; without forgoing its nonlinearity and complexity. Moreover, a 70kg payload was also included to embody an average user, in simulating vertical extensions of the system from 0.11m to 0.25m. The completed model is then integrated with Matlab/Simulink for control design and performance evaluation through visualized simulations. The research has been compared to the previous controllers, Fuzzy Logic Control Type-1 (FLCT1), in gauging improvements and performance superiority. The significance of SDA-IT2FLC as the stability controller within the investigated system has been confirmed through current findings, which outperformed that of its predecessors (IT2FLC and FLCT1). Such results are supported through a significant reduction in traveled distance, tilt, and control torques, following a recorded 5.6% and 33.3% improvements on the stability of the system, to the performance of heuristically-tuned IT2FLC; as well as a 60% and 94% improvements in angular positions on the system, as compared to the FLCT1. Moreover, a 95.4% reduction in torques has been recorded for SDA-IT2FLC, as compared to that of FLCT1. Ultimately, SDAIT2FLC has demonstrated promising outcomes over its predecessors on maintaining the system’s stability in an upright position in terms of faster convergence and a significant reduction in traveled distance, tilt and control torques, proving itself as the robust controller for a double-link two-wheeled wheelchair with movable payload system

New players in intelligent transportation: Autonomous Segway in a dynamic environment

This paper heralds a mathematical treatment of Segways as autonomous robots for personal transportation and deliveries and courier services in constrained dynamic environments from a bird’s-eye view. New velocity-based stabilizing controllers of an autonomous nonholonomic two-wheeled self-balancing personalized Segway robot are extracted from a total potential developed by employing the Lyapunov-based Control Scheme (LbCS) for navigation in a partially known environment. Velocity controllers’ cost and time effectiveness and efficiency result from the interaction of the three prominent pillars of LbCS: smoothest, shortest, and safest path for motion planning. Furthermore, the autonomous personal transporter has an obstacle avoidance sensor with a limited detection range ideal for fast navigation in dynamic environments with narrow corridors, tracks, and pathways. This also successfully facilitates navigation in a partially known environment where the sensors only receive and avoid static and dynamic obstacles in a limited range. The results are numerically validated, and the efficacy of the new controllers is exemplified via computer simulations, which illustrate the forward, backward, and zero-turn radius maneuvers of the Segway robot. Introducing the particular autonomous personal transporter would contribute to transportation systems of smart cities