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

Terminal Sliding Mode Control of Mobile Wheeled Inverted Pendulum System with Nonlinear Disturbance Observer

A terminal sliding mode controller with nonlinear disturbance observer is investigated to control mobile wheeled inverted pendulum system. In order to eliminate the main drawback of the sliding mode control, “chattering” phenomenon, and for compensation of the model uncertainties and external disturbance, we designed a nonlinear disturbance observer of the mobile wheeled inverted pendulum system. Based on the nonlinear disturbance observer, a terminal sliding mode controller is also proposed. The stability of the closed-loop mobile wheeled inverted pendulum system is proved by Lyapunov theorem. Simulation results show that the terminal sliding mode controller with nonlinear disturbance observer can eliminate the “chattering” phenomenon, improve the control precision, and suppress the effects of external disturbance and model uncertainties effectively

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

Design and Control of a Two-Wheeled Robotic Walker

This thesis presents the design, construction, and control of a two-wheeled inverted pendulum (TWIP) robotic walker prototype for assisting mobility-impaired users with balance and fall prevention. A conceptual model of the robotic walker is developed and used to illustrate the purpose of this study. A linearized mathematical model of the two-wheeled system is derived using Newtonian mechanics. A control strategy consisting of a decoupled LQR controller and three state variable controllers is developed to stabilize the platform and regulate its behavior with robust disturbance rejection performance. Simulation results reveal that the LQR controller is capable of stabilizing the platform and rejecting external disturbances while the state variable controllers simultaneously regulate the system’s position with smooth and minimum jerk control. A prototype for the two-wheeled system is fabricated and assembled followed by the implementation and tuning of the control algorithms responsible for stabilizing the prototype and regulating its position with optimal performance. Several experiments are conducted, confirming the ability of the decoupled LQR controller to robustly balance the platform while the state variable controllers regulate the platform’s position with smooth and minimum jerk control

Motion Planning

Motion planning is a fundamental function in robotics and numerous intelligent machines. The global concept of planning involves multiple capabilities, such as path generation, dynamic planning, optimization, tracking, and control. This book has organized different planning topics into three general perspectives that are classified by the type of robotic applications. The chapters are a selection of recent developments in a) planning and tracking methods for unmanned aerial vehicles, b) heuristically based methods for navigation planning and routes optimization, and c) control techniques developed for path planning of autonomous wheeled platforms

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