Chin-Wang Tao

Adaptive fuzzy terminal sliding mode controller for linear systems with mismatched time-varying uncertainties

A new design approach of an adaptive fuzzy terminal sliding mode controller for linear systems with mismatched time-varying uncertainties is presented in this paper. A fuzzy terminal sliding mode controller is designed to retain the advantages of the terminal sliding mode controller and to reduce the chattering occurred with the terminal sliding mode controller. The sufficient condition is provided for the uncertain system to be invariant on the sliding surface. The parameters of the output fuzzy sets in the fuzzy mechanism are adapted on-line to improve the performance of the fuzzy sliding mode control system. The bounds of the uncertainties are not required to be known in advance for the presented adaptive fuzzy sliding mode controller. The stability of the fuzzy control system is also guaranteed. Moreover, the chattering around the sliding surface in the sliding mode control can be reduced by the proposed design approach. Simulation results are included to illustrate the effectiveness of the proposed adaptive fuzzy terminal sliding mode controller.

A Novel Fuzzy-Sliding and Fuzzy-Integral-Sliding Controller for the Twin-Rotor Multi-Input–Multi-Output System

In this paper, a novel fuzzy-sliding and fuzzy-integral-sliding controller (FSFISC) is designed to position the yaw and pitch angles of a twin-rotor multi-input-multi-output system (TRMS). With the coupling effects, which are considered as the uncertainties, the highly coupled nonlinear TRMS is pseudodecomposed into a horizontal subsystem and a vertical subsystem (VS). The proposed FSFISC consists of a fuzzy-sliding controller and an FISC for the horizontal and the VSs, respectively. The reaching conditions and the stability of the TRMS with the proposed controller are guaranteed. Simulation results are included to indicate that TRMS with the presented FSFISC can greatly alleviate the chattering effect and remain robust to the external disturbances. In addition, the performance comparisons with the proportional-integral-differential (PID) approach using a modified real-value-type genetic algorithm are provided to show that the FSFISC has better performance in the aspects of error and control indexes.

Fuzzy Sliding-Mode Formation Control for Multirobot Systems: Design and Implementation

This paper mainly addresses the decentralized formation problems for multiple robots, where a fuzzy sliding-mode formation controller (FSMFC) is proposed. The directed networks of dynamic agents with external disturbances and system uncertainties are discussed in consensus problems. To perform a formation control and to guarantee system robustness, a novel formation algorithm combining the concepts of graph theory and fuzzy sliding-model control is presented. According to the communication topology, formation stability conditions can be determined so that an FSMFC can be derived. By Lyapunov stability theorem, not only the system stability can be guaranteed, but the desired formation pattern of a multirobot system can be also achieved. Simulation results are provided to demonstrate the effectiveness of the provided control scheme. Finally, an experimental setup for the e-puck multirobot system is built. Compared to first-order formation algorithm and fuzzy neural network formation algorithm, it shows that real-time experimental results empirically support the promising performance of desire.

Design of a parallel distributed fuzzy LQR controller for the twin rotor multi-input multi-output system

In this paper, a helicopter-like twin rotor multi-input multi-output system (TRMS) is decoupled and is fuzzy Takagi-Sugeno modeled with the complex nonlinear functions simplified as the propositional combination of linear functions. The design procedures of the fuzzy Takagi-Sugeno model of TRMS are detailed. Based on the derived fuzzy Takagi-Sugeno model, parallel distributed fuzzy LQR controller are designed to control the positions of the pitch and yaw angles in TRMS. The stability of the TRMS system with the proposed fuzzy controllers is discussed. Moreover, simulation results are included to indicate the effectiveness of the presented parallel distributed fuzzy LQR controllers for the TRMS.

Robust fuzzy control for a plant with fuzzy linear model

A robust complexity reduced proportional-integral-derivative (PID)-like fuzzy controllers is designed for a plant with fuzzy linear model. The plant model is described with the experts linguistic information involved. The linguistic information for the plant model is represented as fuzzy sets. In order to design a robust fuzzy controller for a plant model with fuzzy sets, an approach is developed to implement the best crisp approximation of fuzzy sets into intervals. Then, Kharitonovs Theorem is applied to construct a robust fuzzy controller for the fuzzy uncertain plant with interval model. With the linear combination of input variables as a new input variable, the complexity of the fuzzy mechanism of PID-like fuzzy controller is significantly reduced. The parameters in the robust fuzzy controller are determined to satisfy the stability conditions. The robustness of the designed fuzzy controller is discussed. Also, with the provided definition of relative robustness, the robustness of the complexity reduced fuzzy controller is compared to the classical PID controller for a second-order plant with fuzzy linear model. The simulation results are included to show the effectiveness of the designed PID-like robust fuzzy controller with the complexity reduced fuzzy mechanism.

Design of a Fuzzy Controller With Fuzzy Swing-Up and Parallel Distributed Pole Assignment Schemes for an Inverted Pendulum and Cart System

In this paper, the inverted pendulum and cart system is effectively approximated by a Takagi-Sugeno (T-S) fuzzy model in a small range of angle near its equilibrium state. According to the proposed (T-S) fuzzy model of the inverted pendulum and cart system, a fuzzy controller designed with the parallel distributed pole assignment scheme is adopted to position the pendulum and cart at the desired states. The nonlinear friction is also considered. Moreover, a fuzzy swing-up controller is developed to swing up the pendulum on a limited rail under the constraints of control actions. Further, the stability of the inverted pendulum and cart system with the fuzzy parallel distributed pole assignment controller is studied. Simulation results are included to indicate the effectiveness and robustness of the proposed fuzzy controller.

Simplified type-2 fuzzy sliding controller for wing rock system

Wing rock is a highly nonlinear phenomenon in which aircrafts with slender delta wings undergo limit cycle roll oscillations at high angles of attack. A simplified type-2 fuzzy sliding controller is designed for suppressing wing rock phenomena and tracking the desired trajectories. To reduce the computational complexity of a type-reducer, the end points of a type-reduced set are approximated by the outputs of two standard fuzzy sliding mechanisms in the proposed simplified type-2 fuzzy sliding controller. Furthermore, the sliding modes of the fuzzy sliding control system are guaranteed. Simulation results are included to show the effectiveness of the proposed simplified type-2 fuzzy sliding controller.

Fuzzy sliding-mode control for ball and beam system with fuzzy ant colony optimization

This paper mainly addresses the balance control of a ball and beam system, where a pair of decoupled fuzzy sliding-mode controllers (DFSMCs) are proposed. The DFSMC has the advantage of reducing the design complexity, in which the coupling dynamics of the state-error dynamics are considered as disturbance terms. Stability analysis of the ball and beam system with DFSMCs is also discussed in detail. To further improve the control performance, an improved ant colony optimization (ACO) is proposed to optimize the controller parameters. The proposed ACO algorithm has the enhanced capability of fuzzy pheromone updating and adaptive parameter tuning. The proposed ACO-optimized scheme is utilized to tune the parameters of the fuzzy sliding-mode controllers for a real ball-and-beam system. Compared to some conventional ACO algorithms, simulation and experimental results all indicate that the proposed scheme can provide better performance in the aspect of convergence rate and accuracy.

Fuzzy hierarchical swing-up and sliding position controller for the inverted pendulum--cart system

In this paper, a fuzzy hierarchical swing-up and sliding position controller is proposed for the swing-up and position controls of an inverted pendulum-cart system. The proposed fuzzy hierarchical swing-up and sliding position controller includes a fuzzy switching controller, a fuzzy swing-up controller, and a twin-fuzzy-sliding-position controller. The fuzzy switching controller is designed to smoothly switch between the swing-up and position controls. With the inverted pendulum-cart system decomposed into a pendulum subsystem and a cart subsystem, fuzzy sliding position controllers are presented to robustly balance the pendulum and cart at the desired positions. The sliding mode and stability of the fuzzy sliding position control systems are guaranteed. Moreover, an adaptive mechanism is provided to on-line adjust the parameters in the fuzzy sliding position controllers. Simulation results are included to illustrate the effectiveness of the proposed fuzzy hierarchical swing-up and sliding position controller.

Design of fuzzy controllers with adaptive rule insertion

In this paper, an approach of designing adaptive fuzzy controllers is presented to systematically develop efficient and effective rules for fuzzy controllers. The proposed fuzzy controllers are first designed with two basic fuzzy if-then rules. Then according to the design requirements of the fuzzy control system, new fuzzy if-then rules are inserted into the rule-base structure of the fuzzy controller. Initially the inserted fuzzy rules are redundant in the sense that the resultant input-output mapping of the fuzzy rules remains intact. After that the parameters of the membership functions for the fuzzy sets of the newly added fuzzy rules are trained on-line to minimize predefined cost functions. Thus, efficient fuzzy controllers can be systematically designed. Simulations for linear, nonlinear, and delayed systems are provided to show the effectiveness of the proposed approach.