Kuo-Hsiang Cheng

Fuzzy–Neural Sliding-Mode Control for DC–DC Converters Using Asymmetric Gaussian Membership Functions

A fuzzy-neural sliding-mode (FNSM) control system is developed to control power electronic converters. The FNSM control system comprises a neural controller and a compensation controller. In the neural controller, an asymmetric fuzzy neural network is utilized to mimic an ideal controller. The compensation controller is designed to compensate for the approximation error between the neural controller and the ideal controller. An online training methodology is developed in the Lyapunov sense; thus, the stability of the control system can be guaranteed. Finally, to investigate the effectiveness of the FNSM control scheme, it is applied to control a pulsewidth-modulation-based forward dc-dc converter. Experimental results show that the proposed FNSM control system is found to achieve favorable regulation performances even under input-voltage and load-resistance variations

Recurrent neuro-fuzzy hybrid-learning approach to accurate system modeling

A recurrent neuro-fuzzy approach with RO-LSE hybrid learning algorithm to the problem of system modeling is proposed in the paper. The proposed recurrent neuro-fuzzy system possesses six layers of neural network to perform the fuzzy inference. The recurrent structure is formed using lagged membership-grade signals as internal feedbacks to the layer of membership functions of fuzzy sets, and it is expected having great potential to trace the temporal change of signals. Fuzzy sets with time-varying kernels have excellent property, with that the input-output mapping of the neuro-fuzzy system is no longer fixed but time varying. In this study, a new parameter learning approach is proposed for NFS with good learning convergence, in which the hybrid RO-LSE learning algorithm is utilized for the update of parameters. The well-known random optimization (RO) method is used to update the parameters of the premise parts of the proposed system, and the method of least square estimation (LSE) to update those of the consequent parts. The hybrid algorithm is found useful, and it has shown fast convergence of parameter learning for the proposed system. Three examples are used to demonstrate the brilliancy of the proposed approach. Excellent performance of the proposed approach in modeling accuracy and learning convergence is observed.

Robust wavelet-based adaptive neural controller design with a fuzzy compensator

In this paper, a robust wavelet-based adaptive neural control (RWANC) with a PI type learning algorithm is proposed. The proposed RWANC system is composed of a wavelet neural controller and a fuzzy compensation controller. The wavelet neural control is utilized to approximate an ideal controller and the fuzzy compensation controller with a fuzzy logic system in it is used to remove the chattering phenomena of conventional sliding-mode control completely. In the RWANC, the learning algorithm is derived based on the Lyapunov function, thus the closed-loop systems stability can be guaranteed. The chaotic system control has become an emerging topic in engineering community since the uncontrolled system displays complex, noisy-like and unpredictable behavior. Therefore, the proposed RWANC approach is applied to a second-order chaotic nonlinear system to investigate the effectiveness. Through the simulation results, the proposed RWANC scheme can achieve favorable tracking performance and the convergence of the tracking error and control parameters can be accelerated by the developed PI adaptation learning algorithm.

Recurrent fuzzy-neural approach for nonlinear control using dynamic structure learning scheme

In this paper, a dynamic recurrent fuzzy neural network (DRFNN) with a structure learning scheme is proposed. The structure learning scheme consists of two learning phases: the node-constructing phase and the node-pruning phase, which enables the DRFNN to determine the nodes dynamically to achieve optimal network structure. Then, a self-structuring recurrent fuzzy neural network control (SRFNNC) system via the DRFNN approach is developed. The SRFNNC system is composed of a neural controller and a compensation controller. The neural controller using a DRFNN to mimic an ideal controller is the main controller, and the compensation controller is designed to compensate the difference between the neural controller and the ideal controller. In the SRFNNC system, all the parameters are evolved based on the Lyapunov function to ensure the system stability. Finally, to investigate the effectiveness of the proposed SRFNNC system, it is applied to control a second-order chaotic nonlinear system. A comparison between a fixed-structuring recurrent fuzzy neural network control and the proposed SRFNNC is made. Through the simulation results, the advantages of the proposed SRFNNC method can be observed.

Pseudoerror-based self-organizing neuro-fuzzy system

The novel concept of pseudoerrors for a self-organizing neuro-fuzzy system (SO-NFS) is proposed for tracking control problem. To demonstrate the proposed approach, an example of motion control of an auto-warehousing crane system is illustrated, which can move back and forth in x,y, and z directions to access and store cargoes. The proposed SO-NFS shows excellent performance in control of the crane system for different loading conditions and varying distances in all directions.

CMAC-based neuro-fuzzy approach for complex system modeling

A cerebellar model arithmetic computer (CMAC)-based neuron-fuzzy approach for accurate system modeling is proposed. The system design comprises the structure determination and the hybrid parameter learning. In the structure determination, the CMAC-based system constitution is used for structure initialization. With the advantage of generalization of CMAC, the initial receptive field constitution is formed in a systematic way. In the parameter learning, the random optimization algorithm (RO) is combined with the least square estimation (LSE) to train the parameters, where the premises and the consequences are updated by RO and LSE, respectively. With the hybrid learning algorithm, a compact and well-parameterized CMAC can be achieved for the required performance. The proposed work features the following salient properties: (1) good generalization for system initialization; (2) derivative-free parameter update; and (3) fast convergence. To demonstrate potentials of the proposed approach, examples of SISO nonlinear approximation, MISO time series identification/prediction, and MIMO system mapping are conducted. Through the illustrations and numerical comparisons, the excellences of the proposed work can be observed.

Self-structuring fuzzy-neural backstepping control with a B-spline-based compensator

Abstract To relieve the burdens of network controller design and approximation error bound determination, a self-structuring fuzzy-neural backstepping control system (SSFNBS) with a B-spline-based compensator is proposed. In this paper, a network-identification-based control is represented where the self-structuring fuzzy neural network-based (SSFNN) is used as the observer to approximate the controlled system dynamics. To balance the tradeoff between the structure efficiency and the identification accuracy, a structure learning mechanism of the node-adding process and the node-pruning process is introduced. On the other hand, the B-spline-based compensator is introduced to dispel the effect of approximation error. With the adoption of B-spline functions, the compensation controller can be given in a recurrent way based on the introduction of knot vector and the drawbacks of the conventional compensation controllers can be freed. With the introduction of the B-spline function, the proposed SSFNBS features the following advantages: (1) the capability of network-based controller is improved, (2) the design of the compensation controller can be easily established based on the characteristics of the B-spline function, (3) the stability of closed-loop control system is guaranteed by the means of Lyapunov function with the tuning law of the B-spline-based compensator. To investigate the capabilities of the proposed approach, the SSFNBS is applied to the nonlinear system, chaotic system, and wing rock motion control problems. Through the simulation results the advantages of the proposed SSFNBS can be observed.

Hybrid learning neuro-fuzzy approach for complex modeling using asymmetric fuzzy sets

A hybrid learning neuro-fuzzy system with asymmetric fuzzy sets (HLNFS-A) is proposed in this paper. The learning methods of random optimization (RO) and least square estimation (LSE) are used in hybrid way to train the system parameters of HLNFS-A to achieve stable and fast convergence. In the HLNFS-A, the premise and the consequent parameters are updated by RO and LSE, respectively. With the proposed asymmetric fuzzy sets (AFS), the neuro-fuzzy system can capture the essence of nonlinear property of dynamic system, when used in the application of modeling. To demonstrate the feasibility and the potential of the proposed approach, an example of chaotic time series for system identification and prediction is given to verify the nonlinear mapping capability of the HLNFS-A. The experimental results show that the proposed HLNFS-A can achieve excellent performance for system modeling

Soft computing approach to adaptive noise filtering

A soft computing filtering approach is proposed for adaptive noise cancellation. The goal of noise cancellation is to extract the desired signal from its noise-corrupted version, using the proposed neuro-fuzzy system (NFS) as an adaptive filter. Traditional linear filtering may not be good enough to handle with the noise complexity. In the study, the NFS filter is trained in hybrid way using the well-known random optimization (RO) method and the least squares estimate (LSE) method for the noise canceling problem. The premises and the consequents of the NFS are updated for their parameters using the RO and the LSE, respectively. With the hybrid learning algorithm, the proposed approach has moderate computation and the training of the NFS filter is fast convergence. An example of noise cancellation by the proposed adaptive NFS filter is illustrated and the result is discussed. The NFS filter has stable filtering performance for noise cancellation.

Hybrid Evolutionary Soft-Computing Approach for Unknown System Identification

A hybrid evolutionary neuro-fuzzy system (HENFS) is proposed in this paper, where the weighted Gaussian function (WGF) is used as the membership function for improved premise construction. With the WGF, different types of the membership functions (MFs) can be accommodated in the rule base of HENFS. A new hybrid algorithm of random optimization (RO) algorithm incorporated with the least square estimation (LSE) is presented. Based on the hybridization of RO-LSE, the proposed soft-computing approach overcomes the disadvantages of other widely used algorithms. The proposed HENFS is applied to chaos time series identification and industrial process modeling to verify its feasibility. Through the illustrations and comparisons the impressive performances for unknown system identification can be observed.