Wei-Shou Chan

T-S Fuzzy Model-Based Adaptive Dynamic Surface Control for Ball and Beam System

In this paper, the balance control of a ball and beam system is considered. Based on the T-S fuzzy modeling, the dynamic model of the ball and beam system is formulated as a strict feedback form with modeling errors. Then, an adaptive dynamic surface control (DSC) is utilized to achieve the goal of ball positioning subject to parameter uncertainties. The robust stability of the closed-loop system is preserved by using the Lyapunov theorem. In addition to simulation results, the proposed T-S fuzzy model-based adaptive dynamic surface controller is applied to a real ball and beam system for practical evaluations. Simulation and experimental results illustrate that the proposed control scheme has much better performance than that of conventional DSC. Furthermore, parameter uncertainties and external disturbance are considered to highlight the robustness of the proposed control scheme.

Fuzzy Formation Control and Collision Avoidance for Multiagent Systems

This paper aims to investigate the formation control of leader-follower multiagent systems, where the problem of collision avoidance is considered. Based on the graph-theoretic concepts and locally distributed information, a neural fuzzy formation controller is designed with the capability of online learning. The learning rules of controller parameters can be derived from the gradient descent method. To avoid collisions between neighboring agents, a fuzzy separation controller is proposed such that the local minimum problem can be solved. In order to highlight the advantages of this fuzzy logic based collision-free formation control, both of the static and dynamic leaders are discussed for performance comparisons. Simulation results indicate that the proposed fuzzy formation and separation control can provide better formation responses compared to conventional consensus formation and potential-based collision-avoidance algorithms.

Effects of magnetomechanical vibrations and bending stresses on three-phase three-leg transformers with amorphous cores

This paper explores the influence of bending stresses on the magnetic characteristics of three-phase transformers with amorphous cores. Different types of core structures, including C-cores and toroidal cores, and their magnetic properties are compared using VSM and XRD. The losses in the magnetic core of the three-phase transformer are analyzed using the finite element analysis for both design and measurement. In addition, experimental results indicated that amorphous-core transformers with rectangular corners had higher audible noise and vibration intensities. This is because the condensed distribution of magnetic flux lines in the corners of the core may create high magnetic inductions associated with high magnetostriction. Finally, experiments with three-phase amorphous-core transformers were performed to study the effects of magnetism and magnetostriction on their performance in terms of core loss, vibration, and audible noise.

Adaptive dynamic surface control for fault-tolerant multi-robot systems

This paper presents a new robust adaptive control method for multi-robot systems, where the kinematic model of a differentially driven wheeled mobile robot is considered. Particularly, the situations involving partial loss of actuator effectiveness are addressed. Distributed controllers are derived based on dynamic surface control techniques over networked multiple robots. In addition, adaptive mechanisms are applied to estimate the bounds of effectiveness factor and uncertainty bounds. The robust stability of the multi-robot systems are preserved by using the Lyapunov theorem. The proposed controller can make the robots reach a desired formation following a designate trajectory. Simulation results indicate that the proposed control scheme has superior responses compared to conventional dynamic surface control.

Interval type-2 fuzzy neural network for ball and beam systems

An interval type-2 fuzzy neural network (IT2FNN) is developed for the position control of ball-and-beam systems to confront the noise. A T2FNN consists of a type-2 fuzzy linguistic process as the antecedent part and multi-layer neural network as the consequent part. The developed IT2FNN combines the merits of an interval type-2 fuzzy logic system and a neural network. Furthermore, the parameter-learning of the IT2FNN, which is based on the gradient decent method using adaptation law, is performed on line. Simulation results show that the dynamic behaviors of the proposed IT2FNN control system are more effective and robust with regard to uncertainties than the interval type-2 fuzzy logic control scheme.

Leader-following formation control of multi-robot systems with adaptive fuzzy terminal sliding-mode controller

This paper focuses on the design of a formation controller for multi-robot dynamic systems using adaptive fuzzy terminal sliding-mode techniques. The dynamic model of differential wheeled robots is considered. To achieve finite time leader-follower formation control, a fuzzy terminal sliding-mode controller is derived based on graph theory and consensus algorithm. Moreover, an adaptive law is provided to estimate the bounds of unknown uncertainties. Both simulation and experimental results are applied to validate the formation performance. It is indicated that the proposed adaptive control scheme can provide better leader-following formation responses of networked multiple robots.

Design of adaptive neural fuzzy formation controller for multi-robot systems

This paper aims to investigate the formation control of multi-robot systems, where the first-order kinematic model of a differential wheeled robot is considered. Based on the graph theory and consensus algorithm, an adaptive neural fuzzy formation controller is designed with the capability of on-line learning. The learning rules of controller parameters can be derived from the analyzing of Lyapunov stability. Simulations are adopted to verify the feasibility of proposed techniques. From simulation results, the proposed adaptive neural fuzzy controller can provide better formation responses compared to conventional consensus algorithm.

Formation control for uncertain multiple Euler-Lagrange systems with dynamic surface control and interval type-2 neuro-fuzzy networks

This paper presents a distributed adaptive formation control method for a class of uncertain multiple Euler-Lagrange systems. The proposed approach is based on the graph theory and an adaptive dynamic surface control, where the system uncertainties are approximately modelled by interval type-2 neuro-fuzzy networks. In this study, the robust stability of the closed-loop system is guaranteed by the Lyapunov theorem, and all agents reach a desired formation following a designated trajectory. In addition to simulation example, the proposed method is applied to each agents of Euler-Lagrange dynamics for performance evaluations. Simulation results indicate that the proposed control scheme has superior responses compared to distributed dynamic surface formation control.

Adaptive robust dynamic surface control of magnetic levitation systems

In this paper, an adaptive robust dynamic surface controller (ARDSC) is designed to position the steel ball of a magnetic levitation system. The proposed dynamic surface controller is utilized to overcome the problem of explosion of terms associated with the backstepping method. The presence of modeling errors that are uncertainties of physical parameters is considered in this paper. The adaptive mechanism can deal with the model uncertainties and the analysis of the control stability is given. Simulation results are included to indicate the effectiveness and robustness of the provided controller.