Ching-Hung Lee

Identification and control of dynamic systems using recurrent fuzzy neural networks

Proposes a recurrent fuzzy neural network (RFNN) structure for identifying and controlling nonlinear dynamic systems. The RFNN is inherently a recurrent multilayered connectionist network for realizing fuzzy inference using dynamic fuzzy rules. Temporal relations are embedded in the network by adding feedback connections in the second layer of the fuzzy neural network (FNN). The RFNN expands the basic ability of the FNN to cope with temporal problems. In addition, results for the FNN-fuzzy inference engine, universal approximation, and convergence analysis are extended to the RFNN. For the control problem, we present the direct and indirect adaptive control approaches using the RFNN. Based on the Lyapunov stability approach, rigorous proofs are presented to guarantee the convergence of the RFNN by choosing appropriate learning rates. Finally, the RFNN is applied in several simulations (time series prediction, identification, and control of nonlinear systems). The results confirm the effectiveness of the RFNN.

Tracking control of unicycle-modeled mobile robots using a saturation feedback controller

The tracking control problem with saturation constraint for a class of unicycle-modeled mobile robots is formulated and solved using the backstepping technique and the idea from the LaSalles invariance principle. A global result is presented in which several constraints on the linear and the angular velocities of the mobile robot from recent literature are dropped. The proposed controller can simultaneously solve both the tracking and regulation problems of a unicycle-modeled mobile robot. With the proposed control laws, the robot can globally follow any path specified by a straight line, a circle or a path approaching the origin using a single controller. As demonstrated, the circular and parallel parking control problem are solved using the proposed controller. Computer simulations are presented which confirm the effectiveness of the proposed tracking control law. Practical experimental results validate the simulations.

An Efficient Interval Type-2 Fuzzy CMAC for Chaos Time-Series Prediction and Synchronization

This paper aims to propose a more efficient control algorithm for chaos time-series prediction and synchronization. A novel type-2 fuzzy cerebellar model articulation controller (T2FCMAC) is proposed. In some special cases, this T2FCMAC can be reduced to an interval type-2 fuzzy neural network, a fuzzy neural network, and a fuzzy cerebellar model articulation controller (CMAC). So, this T2FCMAC is a more generalized network with better learning ability, thus, it is used for the chaos time-series prediction and synchronization. Moreover, this T2FCMAC realizes the un-normalized interval type-2 fuzzy logic system based on the structure of the CMAC. It can provide better capabilities for handling uncertainty and more design degree of freedom than traditional type-1 fuzzy CMAC. Unlike most of the interval type-2 fuzzy system, the type-reduction of T2FCMAC is bypassed due to the property of un-normalized interval type-2 fuzzy logic system. This causes T2FCMAC to have lower computational complexity and is more practical. For chaos time-series prediction and synchronization applications, the training architectures with corresponding convergence analyses and optimal learning rates based on Lyapunov stability approach are introduced. Finally, two illustrated examples are presented to demonstrate the performance of the proposed T2FCMAC.

Stabilization of nonlinear nonminimum phase systems: adaptive parallel approach using recurrent fuzzy neural network

In this paper, an adaptive parallel control architecture to stabilize a class of nonlinear systems which are nonminimum phase is proposed. For obtaining an on-line performance and self-tuning controller, the proposed control scheme contains recurrent fuzzy neural network (RFNN) identifier, nonfuzzy controller, and RFNN compensator. The nonfuzzy controller is designed for nominal system using the techniques of backstepping and feedback linearization, is the main part for stabilization. The RFNN compensator is used to compensate adaptively for the nonfuzzy controller, i.e., it acts like a fine tuner; and the RFNN identifier provides the systems sensitivity for tuning the controller parameters. Based on the Lyapunov approach, rigorous proofs are also presented to show the closed-loop stability of the proposed control architecture. With the aid of the RFNN compensators, the parallel controller can indeed improve system performance, reject disturbance, and enlarge the domain of attraction. Furthermore, computer simulations of several examples are given to illustrate the applicability and effectiveness of this proposed controller.

Systems identification using type-2 fuzzy neural network (type-2 FNN) systems

This paper presents a type-2 fuzzy neural network system (type-2 FNN) and its learning algorithm using back-propagation algorithm. In our previous results, the FNN system using type-1 fuzzy logic systems (FLS) is called type-1 FNN system. It has the properties of parallel computation scheme, easy to implement, fuzzy logic inference system, and parameters convergence. For considering the fuzzy rules uncertainties, we use the type-2 FLSs to develop a type-2 FNN system. The type-2 fuzzy sets let us model and minimize the effects of uncertainties in rule-based fuzzy logic systems (FLSs). In this paper, the previous results of type-1 FNN are extended to a type-2 one. In addition, the corresponding learning algorithm is derived by back-program algorithm. Several examples are presented to illustrate the effectiveness of our approach.

Tuning of PID controllers for unstable processes based on gain and phase margin specifications: a fuzzy neural approach

This paper presents a PID tuning method for unstable processes using an adaptive-network-based-fuzzy-inference system (ANFIS) for given gain and phase margin (GPM) specifications. PID tuning methods are widely used to control stable processes. However, PID controller for unstable processes is less common. In this paper, the PID controller parameters can be determined by the ANFIS. Because the definitions of gain and phase margin equations are complex, an analytical tuning method for achieving specified the gain and phase margins is not yet available. In this paper, the ANFIS is adopted to identify the relationship between the gain-phase margin specifications and the PID controller parameters. Then, it is used to automatically tune the PID controller parameters for different gain and phase margin specifications so that neither numerical methods nor graphical methods need be used. A simple method is also developed to estimate the stabilizing region of PID controller parameters and valid region for gain-phase margin. Even for unreasonable specifications, out of the valid region, the ANFIS can still find suitable PID controller to guarantee the stability of the closed-loop system. Simulation results show that the ANFIS can achieve the specified values efficiently.

Efficient collision-free path-planning of multiple mobile robots system using efficient artificial bee colony algorithm

This paper aims to propose a novel design approach for on-line path planning of the multiple mobile robots system with free collision. Based on the artificial bee colony (ABC) algorithm, we propose an efficient artificial bee colony (EABC) algorithm for solving the on-line path planning of multiple mobile robots by choosing the proper objective function for target, obstacles, and robots collision avoidance. The proposed EABC algorithm enhances the performance by using elite individuals for preserving good evolution, the solution sharing provides a proper direction for searching, the instant update strategy provides the newest information of solution. By the proposed approach, the next positions of each robot are designed. Thus, the mobiles robots can travel to the designed targets without collision. Finally, simulation results of illustration examples are introduced to show the effectiveness and performance of the proposed approach.

Recurrent neuro fuzzy control design for tracking of mobile robots via hybrid algorithm

This paper proposes a TSK-type recurrent neuro fuzzy system (TRNFS) and hybrid algorithm- GA_BPPSO to develop a direct adaptive control scheme for stable path tracking of mobile robots. The TRNFS is a modified model of the recurrent fuzzy neural network (RFNN) to obtain generalization and fast convergence. The TRNFS is designed using hybridization of genetic algorithm (GA), back-propagation (BP), and particle swarm optimization (PSO), called GA_BPPSO. For the tracking control of mobile robot, two TRNFSs are designed to generate the control inputs by direct adaptive control scheme and hybrid algorithm GA_BPPSO. Through simulation results, we demonstrate the effectiveness of our proposed controller.

Performance enhancement for neural fuzzy systems using asymmetric membership functions

This paper proposes a method to enhance the performance of interval-valued neural fuzzy systems using asymmetric membership functions (called IVNFS-As). Each asymmetric interval-valued membership function is constructed from parts of four Gaussian functions. The proposed IVNFS-As can capture the essence of nonlinearities in dynamic systems. In addition, the Lyapunov theorem is used to demonstrate the convergence of IVNFS-As, and the corresponding learning algorithm is derived using the gradient method. The asymmetric interval-valued membership functions improve the approximation accuracy of simulation results and reduce the computational complexity. The effectiveness of our approach is demonstrated by results obtained for nonlinear system identification, adaptive control, and chaotic-time-series prediction.

An adaptive type-2 fuzzy neural controller for nonlinear uncertain systems

This article proposes a new control scheme using type-2 fuzzy neural network (type-2 FNN) and adaptive filter for controlling nonlinear uncertain systems. This type-2 FNN model combines the advantages of type-2 fuzzy logic systems and neural networks. The type-2 FNN system has the ability of universal approximation, that is, identification of nonlinear dynamic systems. We herein adopt it to develop a novel control scheme for nonlinear uncertain systems. The proposed control scheme consists of a PD-type adaptive FNN controller and a pre-filter. The adaptive filter is used to provide better performance under transient response and to treat the problem of disturbance attenuation. The tuning parameters for the filter and the type-2 FNN controller will change according to the learning algorithm. By the Lyapunov stability theorem, the convergence of parameters is given in order to guarantee the stability of nonlinear uncertain systems. The effectiveness of the proposed controller is demonstrated by simulated results.