Shared-control for the kinematic model of a rear-wheel drive car

This paper presents a shared-control algorithm for the kinematic model of a rear-wheel drive car, for which the set of feasible Cartesian positions is defined by a group of linear inequalities. The shared-control scheme is based on a hysteresis switch and its properties are established by a Lyapunov-like analysis. Simple numerical examples demonstrate the effectiveness of the shared-control law

Shared-control for the kinematic model of a rear-wheel drive car

This paper presents a shared-control algorithm for the kinematic model of a rear-wheel drive car, for which the set of feasible Cartesian positions is defined by a group of linear inequalities. The shared-control scheme is based on a hysteresis switch and its properties are established by a Lyapunov-like analysis. Simple numerical examples demonstrate the effectiveness of the shared-control law

Hierarchical modeling and speed control of networked induction motor systems

This paper proposes a hierarchical modeling method and a fuzzy speed control strategy for nonlinear networked induction motor systems subject to network induced time delay and packets dropout. The networked induction motor control system consists of a networked speed controller and a local controller. Fuzzy gain scheduling is applied on the networked speed controller to guarantee the robustness against complicated variations on the communication network. The state predictor is to compensate the time delay occurred in data transmission in the feedback channel. In stability analysis, the upper allowed limits of the time delay and packets dropout are calculated using the Lyapunov-Krasovskii theorem, respectively. Simulation and experimental results are given to illustrate the effectiveness of the proposed approach

Hierarchical modeling and speed control of networked induction motor systems

This paper proposes a hierarchical modeling method and a fuzzy speed control strategy for nonlinear networked induction motor systems subject to network induced time delay and packets dropout. The networked induction motor control system consists of a networked speed controller and a local controller. Fuzzy gain scheduling is applied on the networked speed controller to guarantee the robustness against complicated variations on the communication network. The state predictor is to compensate the time delay occurred in data transmission in the feedback channel. In stability analysis, the upper allowed limits of the time delay and packets dropout are calculated using the Lyapunov-Krasovskii theorem, respectively. Simulation and experimental results are given to illustrate the effectiveness of the proposed approach

Network-based fuzzy decentralized sliding-mode control for car-like mobile robots

[[abstract]]In this paper, the trajectory tracking of a car-like mobile robot (CLMR) using network-based fuzzy decentralized sliding-mode control (NBFDSMC) is developed. The scaling factors and the coefficients of the sliding surface for the control of the steering angle and forward-backward velocity of a CLMR are adopted by that for the control of two motors. Due to the delay transmission of a signal through an Internet and wireless module, a revision of fuzzy decentralized sliding-mode control (FDSMC) with suitable sampling time (i.e., NBFDSMC) is accomplished by the quality-of-service (QoS). The proposed control can track a reference trajectory without the requirement of a mathematical model. Only the information of the upper bound of system knowledge (including the dynamics of the CLMR, the delay feature of Internet network, and wireless module) is required to select the suitable scaling factors and coefficients of sliding surface such that an excellent performance is obtained. In addition, the stability of the closed-loop system in the presence of time-varying delay is addressed. Finally, a sequence of experiments including the control of unloaded CLMR and the trajectory tracking of CLMR is carried out to consolidate the usefulness of the proposed control system[[notice]]補正完畢[[incitationindex]]SC

Shared-control for systems with constraints

In the thesis we solve the shared-control problem for three classes of systems: a class of linear mechanical systems, mobile robots and rear wheel drive cars, via full state feedback or output feedback while ensuring that all the state constraints on the closed-loop systems are satisfied. To design the feedback controller for a system with state constraints we firstly remove all the constraints by changing the coordinates through a logarithmic function. Then the back-stepping method is used to design the controller and a Lyapunov-like analysis is used to prove stability properties of the closed-loop system. The shared-control algorithm is based on a hysteresis switch which reduces oscillations when changing the control authority from the human operator to the feedback controller or vice-versa. Unlike other shared-control methods, formal properties of the closed-loop systems with the shared-control have been rigorously established. We start the design of the full state-feedback shared-controller with the assumption that the admissible Cartesian configuration set Pa of the system is a time-invariant convex set defined by a group of linear inequalities. Then the results are extended to the design of shared-controllers via output feedback. In the cases in which only output feedback is available, we can solve the problem by either developing an observer or “remodeling” the system. Through system remodeling we are able to deal with any shape of the admissible configuration set Pa, even time-varying ones. Simulation results help to illustrate how the shared-controller works and show its effectiveness. The state of the closed-loop system with the shared-control never violates the constraints. Experiments done on a mobile robot also demonstrate that the shared-control algorithm works well in practice and meets all safety requirements. In addition, the experimental results match the simulation ones, indicating that the modeling approximations are reasonable and suitable.Open Acces

Analysis and Control of Mobile Robots in Various Environmental Conditions

The world sees new inventions each day, made to make the lifestyle of humans more easy and luxurious. In such global scenario, the robots have proved themselves to be an invention of great importance. The robots are being used in almost each and every field of the human world. Continuous studies are being done on them to make them simpler and easier to work with. All fields are being unraveled to make them work better in the human world without human interference. We focus on the navigation field of these mobile robots. The aim of this thesis is to find the controller that produces the most optimal path for the robot to reach its destination without colliding or damaging itself or the environment. The techniques like Fuzzy logic, Type 2 fuzzy logic, Neural networks and Artificial bee colony have been discussed and experimented to find the best controller that could find the most optimal path for the robot to reach its goal position. Simulation and Experiments have been done alike to find out the optimal path for the robot

Navigational Path Analysis of Mobile Robot in Various Environments

This dissertation describes work in the area of an autonomous mobile robot. The objective is navigation of mobile robot in a real world dynamic environment avoiding structured and unstructured obstacles either they are static or dynamic. The shapes and position of obstacles are not known to robot prior to navigation. The mobile robot has sensory recognition of specific objects in the environments. This sensory-information provides local information of robots immediate surroundings to its controllers. The information is dealt intelligently by the robot to reach the global objective (the target). Navigational paths as well as time taken during navigation by the mobile robot can be expressed as an optimisation problem and thus can be analyzed and solved using AI techniques. The optimisation of path as well as time taken is based on the kinematic stability and the intelligence of the robot controller. A successful way of structuring the navigation task deals with the issues of individual behaviour design and action coordination of the behaviours. The navigation objective is addressed using fuzzy logic, neural network, adaptive neuro-fuzzy inference system and different other AI technique.The research also addresses distributed autonomous systems using multiple robot

Fuzzy Controllers

Trying to meet the requirements in the field, present book treats different fuzzy control architectures both in terms of the theoretical design and in terms of comparative validation studies in various applications, numerically simulated or experimentally developed. Through the subject matter and through the inter and multidisciplinary content, this book is addressed mainly to the researchers, doctoral students and students interested in developing new applications of intelligent control, but also to the people who want to become familiar with the control concepts based on fuzzy techniques. Bibliographic resources used to perform the work includes books and articles of present interest in the field, published in prestigious journals and publishing houses, and websites dedicated to various applications of fuzzy control. Its structure and the presented studies include the book in the category of those who make a direct connection between theoretical developments and practical applications, thereby constituting a real support for the specialists in artificial intelligence, modelling and control fields