A Practical Fuzzy Controller with Q-learning Approach for the Path Tracking of a Walking-aid Robot

[[abstract]]This study tackles the path tracking problem of a prototype walking-aid (WAid) robot which features the human-robot interactive navigation. A practical fuzzy controller is proposed for the path tracking control under reinforcement learning ability. The inputs to the designed fuzzy controller are the error distance and the error angle between the current and the desired position and orientation, respectively. The controller outputs are the voltages applied to the left- and right-wheel motors. A heuristic fuzzy control with the Sugeno-type rules is then designed based on a model-free approach. The consequent part of each fuzzy control rule is designed with the aid of Q-learning approach. The design approach of the controller is presented in detail, and effectiveness of the controller is demonstrated by hardware implementation and experimental results under human-robot interaction environment. The results also show that the proposed path tracking control methods can be easily applied in various wheeled mobile robots.[[conferencetype]]國際[[conferencedate]]20140914~20140917[[booktype]]電子版[[iscallforpapers]]Y[[conferencelocation]]Nagoya, Japa

Control and coordination for a group of mobile robots in unknown environments

This thesis studies the trajectory tracking and cooperative behavior for a team of mobile robots using nonlinear and intelligent algorithms to more efficiently achieve the mission outcome. There are many practical applications where specific tasks are more resourcefully achieved by using a group of mobile robots rather than a single robot. Mobile robots can subdivide and multi-task the mission with speed and accuracy and the ability to be individually modified for precise tasks makes them ideally suited for applications such as search and rescue, exploration or entertainment. When comparing the mission outcome of a group of multi mobile robots (MMR) to that of a single robot, we see that the performance of the MMR group improves the specific task allocation, safety, the time duration required and the system effectiveness to achieve the outcome. In order to create the most effective control algorithm for trajectory tracking, we present three different techniques including Lyapunov technique, intelligent control (fuzzy control) and the exponential version of sliding mode. The developed algorithms instruct a robot to keep moving on their desired trajectory while simultaneously reducing tracking errors. The experimental results when using a single mobile robot are presented to demonstrate the potential and capability of the developed algorithms. In order to coordinate a group of mobile robots to achieve a common outcome, the goal is to create efficient system architecture and a control algorithm that enables them to work both individually and in meaningful robot formations. This is achieved by employing coordination and trajectory tracking techniques with the knowledge derived by the localization of the robots from their environment. Three different hierarchical controllers are presented based on nonlinear and intelligent techniques in order to construct an algorithm that exhibits both group cooperation and coordination for a team of mobile robots. These controllers consist of Lyapunov technique, intelligent control (fuzzy control) and the exponential version of sliding mode. For improved trajectory tracking, each robot is fitted with onboard sensors. When an obstacle is detected by any of the robots’ sensors, they direct that robot to move around the obstacle by changing its velocity and direction. As well as obstacle avoidance, the controllers work to make the MMR group arrive concurrently at their target points by adjusting each of the individual robots’ velocities as they move along their desired trajectories. This means the group will arrive at their destination within the same time duration, regardless of the length of each individual trajectory or number of obstacles that confront each robot. The experimental results obtained using three mobile robots display the performance of these control algorithms in producing a cooperative and coordinated behavior for the robot group

Control of Real Mobile Robot Using Artificial Intelligence Technique

An eventual objective of mobile robotics research is to bestow the robot with high cerebral skill, of which navigation in an unfamiliar environment can be succeeded by using on‐line sensory information, which is essentially starved of humanoid intermediation. This research emphases on mechanical design of real mobile robot, its kinematic & dynamic model analysis and selection of AI technique based on perception, cognition, sensor fusion, path scheduling and analysis, which has to be implemented in robot for achieving integration of different preliminary robotic behaviors (e.g. obstacle avoidance, wall and edge following, escaping dead end and target seeking). Navigational paths as well as time taken during navigation by the mobile robot can be expressed as an optimization problem and thus can be analyzed and solved using AI techniques. The optimization of path as well as time taken is based on the kinematic stability and the intelligence of the robot controller. A set of linguistic fuzzy rules are developed to implement expert knowledge under various situations. Both of Mamdani and Takagi-Sugeno fuzzy model are employed in control algorithm for experimental purpose. Neural network has also been used to enhance and optimize the outcome of controller, e.g. by introducing a learning ability. The cohesive framework combining both fuzzy inference system and neural network enabled mobile robot to generate reasonable trajectories towards the target. An authenticity checking has been done by performing simulation as well as experimental results which showed that the mobile robot is capable of avoiding stationary obstacles, escaping traps, and reaching the goal efficiently

Adaptive Synergetic Controller for Stabilizing the Altitude and Angle of Mini Helicopter

This research proposes ASC (Adaptive Synergetic Controller) for the nonlinear model of MH (Mini Helicopter) to stabilize the desired altitude and angle. The model of MH is highly nonlinear, underactuated and multivariable in nature due to its dynamic uncertainties and restrictions of velocities during the flight. ASC can force the tracking errors of the system states converges to zero in a finite interval of time. The MH system requires smooth controller and fast precise transition response from initial state till the desired state, therefore the parametric calculations and simulations can be done by the proposed ASC algorithm. It is validated that the above simulated results of the proposed controller have a better convergence rate and smoother stability response in order to track the desired altitude and angle when compared with SMC (Sliding Mode Controller). Moreover, it does not need any linearization, transformation and variations in the system model

Navigation Techniques for Control of Multiple Mobile Robots

The investigation reported in this thesis attempt to develop efficient techniques for the control of multiple mobile robots in an unknown environment. Mobile robots are key components in industrial automation, service provision, and unmanned space exploration. This thesis addresses eight different techniques for the navigation of multiple mobile robots. These are fuzzy logic, neural network, neuro-fuzzy, rule-base, rule-based-neuro-fuzzy, potential field, potential-field-neuro-fuzzy, and simulated-annealing- potential-field- neuro-fuzzy techniques. The main components of this thesis comprises of eight chapters. Following the literature survey in Chapter-2, Chapter-3 describes how to calculate the heading angle for the mobile robots in terms of left wheel velocity and right wheel velocity of the robot. In Chapter-4 a fuzzy logic technique has been analysed. The fuzzy logic technique uses different membership functions for navigation of the multiple mobile robots, which can perform obs..

Feasible, Robust and Reliable Automation and Control for Autonomous Systems

The Special Issue book focuses on highlighting current research and developments in the automation and control field for autonomous systems as well as showcasing state-of-the-art control strategy approaches for autonomous platforms. The book is co-edited by distinguished international control system experts currently based in Sweden, the United States of America, and the United Kingdom, with contributions from reputable researchers from China, Austria, France, the United States of America, Poland, and Hungary, among many others. The editors believe the ten articles published within this Special Issue will be highly appealing to control-systems-related researchers in applications typified in the fields of ground, aerial, maritime vehicles, and robotics as well as industrial audiences

Adaptive dynamic programming with eligibility traces and complexity reduction of high-dimensional systems

This dissertation investigates the application of a variety of computational intelligence techniques, particularly clustering and adaptive dynamic programming (ADP) designs especially heuristic dynamic programming (HDP) and dual heuristic programming (DHP). Moreover, a one-step temporal-difference (TD(0)) and n-step TD (TD(λ)) with their gradients are utilized as learning algorithms to train and online-adapt the families of ADP. The dissertation is organized into seven papers. The first paper demonstrates the robustness of model order reduction (MOR) for simulating complex dynamical systems. Agglomerative hierarchical clustering based on performance evaluation is introduced for MOR. This method computes the reduced order denominator of the transfer function by clustering system poles in a hierarchical dendrogram. Several numerical examples of reducing techniques are taken from the literature to compare with our work. In the second paper, a HDP is combined with the Dyna algorithm for path planning. The third paper uses DHP with an eligibility trace parameter (λ) to track a reference trajectory under uncertainties for a nonholonomic mobile robot by using a first-order Sugeno fuzzy neural network structure for the critic and actor networks. In the fourth and fifth papers, a stability analysis for a model-free action-dependent HDP(λ) is demonstrated with batch- and online-implementation learning, respectively. The sixth work combines two different gradient prediction levels of critic networks. In this work, we provide a convergence proofs. The seventh paper develops a two-hybrid recurrent fuzzy neural network structures for both critic and actor networks. They use a novel n-step gradient temporal-difference (gradient of TD(λ)) of an advanced ADP algorithm called value-gradient learning (VGL(λ)), and convergence proofs are given. Furthermore, the seventh paper is the first to combine the single network adaptive critic with VGL(λ). --Abstract, page iv

Contribuições à locomoção de robôs móveis não-holonômicos usando controle fuzzy baseado em modelo

Tese (doutorado) - Universidade Federal de Santa Catarina, Centro Tecnológico, Programa de Pós-Graduação em Engenharia de Automação e Sistemas, Florianópolis, 2015.A locomoção de robôs móveis apresenta desafios no controle da execução de trajetórias, principalmente quando o tipo de robô exibe não-holonomia, pois as restrições de movimento e atuação imprimem, entre outras, redução no conjunto factível de trajetórias. As principais soluções da literatura, aplicáveis e de desempenho comprovado, não apresentam uma forma automática para cálculo de parâmetros de controle que garantam requisitos de desempenho. Este trabalho desenvolve estruturas de locomoção compostas por controladores fuzzy baseados em modelos Takagi-Sugeno (TS-Fuzzy) que são capazes de representar o problema de rastreamento de trajetórias e solucioná-lo com qualidade equivalente às principais técnicas existentes e fornecem, ainda, capacidade de cálculo automático de ganhos que garantem requisitos de desempenho do sistema de controle, ou em caso de não haver solução, acusar inexistência de tais ganhos. Descreve-se uma solução completa de locomoção, composta pelos compensadores propostos e uma técnica de planejamento de locomoção capaz de gerar referências factíveis às limitações de Robôs Móveis Diferenciais (RMDs). Com esta solução foi possível a aplicação prática e a análise de desempenho das estruturas de controle descritas. Os desenvolvimentos teóricos são ilustrados através de aplicações experimentais e simuladas, baseadas na plataforma robótica Powerbot que representa um RMD de médio porte.Abstract : The challenges in locomotion control of non-holonomic mobile robots come from constraints related to sub actuation and trajectory feasibility. The main solutions found in the literature, with proved performance and applicability does not show an automatic method to compute control gains that guarantee global performance requirements. This work develops locomotion structures composed by Takagy-Sugeno fuzzy model based controllers. These structures are capable to represent and solve the trajectory-tracking problem with quality equivalent to the main existent techniques with the capability to compute the controller gains automatically, ensuring performance requirements to the compensator or even evince their absence in case of no solution. The document describes a full locomotion solution, composed by the developed controllers and a methodology of locomotion planning. The planning method is capable of generating feasible references over the locomotion and actuation constraints of the differential mobile robots (DMRs). This solution provides the practical application and performance analysis of the proposed control architectures. The theoretical achievements of this work are illustrated by experimental and simulated scenarios based on the Powerbot robotic platform, witch one represents a DMR of medium size