Balancing a Segway robot using LQR controller based on genetic and bacteria foraging optimization algorithms

A two-wheeled single seat Segway robot is a special kind of wheeled mobile robot, using it as a human transporter system needs applying a robust control system to overcome its inherent unstable problem. The mathematical model of the system dynamics is derived and then state space formulation for the system is presented to enable design state feedback controller scheme. In this research, an optimal control system based on linear quadratic regulator (LQR) technique is proposed to stabilize the mobile robot. The LQR controller is designed to control the position and yaw rotation of the two-wheeled vehicle. The proposed balancing robot system is validated by simulating the LQR using Matlab software. Two tuning methods, genetic algorithm (GA) and bacteria foraging optimization algorithm (BFOA) are used to obtain optimal values for controller parameters. A comparison between the performance of both controllers GA-LQR and BFO-LQR is achieved based on the standard control criteria which includes rise time, maximum overshoot, settling time and control input of the system. Simulation results suggest that the BFOA-LQR controller can be adopted to balance the Segway robot with minimal overshoot and oscillation frequency

Genetic programming for the automatic design of controllers for a surface ship

In this paper, the implementation of genetic programming (GP) to design a contoller structure is assessed. GP is used to evolve control strategies that, given the current and desired state of the propulsion and heading dynamics of a supply ship as inputs, generate the command forces required to maneuver the ship. The controllers created using GP are evaluated through computer simulations and real maneuverability tests in a laboratory water basin facility. The robustness of each controller is analyzed through the simulation of environmental disturbances. In addition, GP runs in the presence of disturbances are carried out so that the different controllers obtained can be compared. The particular vessel used in this paper is a scale model of a supply ship called CyberShip II. The results obtained illustrate the benefits of using GP for the automatic design of propulsion and navigation controllers for surface ships

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

Evolutionary approaches to robot path planning

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.The ultimate goal in robotics is to create machines which are more independent and rely less on humans to guide them in their operation. There are many sub-systems which may be present in such a robot, one of which is path planning — the ability to determine a sequence of positions or configurations between an initial and goal position within a particular obstacle cluttered workspace. Many classical path planning techniques have been developed, but these tend to have drawbacks such as their computational requirements; the suitability of the plans they produce for a particular application; or how well they are able to generalise to unseen problems. In recent years, evolutionary based problem solving techniques have seen a rise in popularity, possibly coinciding with the improvement in the computational power afforded researches by successful developments in hardware. These techniques adopt some of the features of natural evolution and mimic them in a computer. The increase in the number of publications in the areas of Genetic Algorithms (GA) and Genetic Programming (GP) demonstrate the success achieved when applying these techniques to ever more problem areas. This dissertation presents research conducted to determine whether there is a place for Evolutionary Approaches, and specifically GA and GP, in the development of future path planning techniques

MODELLING AND CONTROL OF MULTI-FINGERED ROBOT HAND USING INTELLIGENT TECHNIQUES

Research and development of robust multi-fingered robot hand (MFRH) have been going on for more than three decades. Yet few can be found in an industrial application. The difficulties stem from many factors, one of which is that the lack of general and effective control techniques for the manipulation of robot hand. In this research, a MFRH with five fingers has been proposed with intelligent control algorithms. Initially, mathematical modeling for the proposed MFRH has been derived to find the Forward Kinematic, Inverse Kinematic, Jacobian, Dynamics and the plant model. Thereafter, simulation of the MFRH using PID controller, Fuzzy Logic Controller, Fuzzy-PID controller and PID-PSO controller has been carried out to gauge the system performance based parameters such rise time, settling time and percent overshoot

AutoTuning Environment for Static Obstacle Avoidance Methods Applied to USVs

This work is focused on reactive Static Obstacle Avoidance (SOA) methods used to increase the autonomy of Unmanned Surface Vehicles (USVs). Currently, there are multiple approaches to avoid obstacles, which can be applied to different types of USV. In order to assist in the choice of the SOA method for a particular vessel and to accelerate the pretuning process necessary for its implementation, this paper proposes a new AutoTuning Environment for Static Obstacle Avoidance (ATESOA) methods applied to USVs. In this environment, a new simplified modelling of a LIDAR (Laser Imaging Detection and Ranging) sensor is proposed based on numerical simulations. This sensor model provides a realistic environment for the tuning of SOA methods that, due to its low load computation, is used by evolutionary algorithms for the autotuning. In order to analyze the proposed ATESOA, three SOA methods were adapted and implemented to consider the measurements given by the LIDAR model. Furthermore, a mathematical model is proposed and evaluated for using as USV in the simulation enviroment. The results obtained in numerical simulations show how the new ATESOA is able to adjust the SOA methods in scenarios with different obstacle distributions

Learning in behavioural robotics

The research described in this thesis examines how machine learning mechanisms can be used in an assembly robot system to improve the reliability of the system and reduce the development workload, without reducing the flexibility of the system. The justification foi' this is that for a robot to be performing effectively it is frequently necessary to have gained experience of its performance under a particular configuration before that configuration can be altered to produce a performance improvement. Machine learning mechanisms can automate this activity of testing, evaluating and then changing.From studying how other researchers have developed working robot systems the activities which require most effort and experimentation are:-• The selection of the optimal parameter settings. • The establishment of the action-sensor couplings which are necessary for the effective handling of uncertainty. • Choosing which way to achieve a goal.One way to implement the first two kinds of learning is to specify a model of the coupling or the interaction of parameters and results, and from that model derive an appropriate learning mechanism that will find a parametrisation for that model that will enable good performance to be obtained. From this starting point it has been possible to show how equal, or better performance can be obtained by using iearning mechanisms which are neither derived from nor require a model of the task being learned. Instead, by combining iteration and a task specific profit function it is possible to use a generic behavioural module based on a learning mechanism to achieve the task.Iteration and a task specific profit function can also be used to learn which behavioural module from a pool of equally competent modules is the best at any one time to use to achieve a particular goal. Like the other two kinds of learning, this successfully automates an otherwise difficult test and evaluation process that would have to be performed by a developer. In doing so effectively, it, like the other learning that has been used here, shows that instead of being a peripheral issue to be introduced to a working system, learning, carried out in the right way, can be instrumental in the production of that working system