Fuzzy CMAC Neural Network Research Articles

Adaptive-backstepping force/motion control for mobile-manipulator robot based on fuzzy CMAC neural networks

In this paper, an adaptive backstepping fuzzy cerebellar-model-articulation-control neural-networks control (ABFCNC) system for motion/force control of the mobile-manipulator robot (MMR) is proposed. By applying the ABFCNC in the tracking-position controller, the unknown dynamics and parameter variation problems of the MMR control system are relaxed. In addition, an adaptive robust compensator is proposed to eliminate uncertainties that consist of approximation errors, uncertain disturbances. Based on the tracking position-ABFCNC design, an adaptive robust control strategy is also developed for the nonholonomicconstraint force of the MMR. The design of adaptive-online learning algorithms is obtained by using the Lyapunov stability theorem. Therefore, the proposed method proves that it not only can guarantee the stability and robustness but also the tracking performances of the MMR control system. The effectiveness and robustness of the proposed control system are verified by comparative simulation results.

Adaptive-WFCNNs-backstepping force/motion control system for mobile-manipulator robot

Purpose – The purpose of this paper is to improve the flexibility and tracking errors of the controllers-based neural networks (NNs) for mobile manipulator robot (MMR) in the presence of time-varying uncertainties. Design/methodology/approach – The conventional backstepping force/motion control is developed by the wavelet fuzzy CMAC neural networks (WFCNNs) (for mobile-manipulator robot). The proposed WFCNNs are applied in the tracking-position-backstepping controller to deal with the uncertain dynamics of the controlled system. In addition, an adaptive robust compensator is proposed to eliminate the inevitable approximation errors, uncertain disturbances, and relax the requirement for prior knowledge of the controlled system. Besides, the position tracking controller, an adaptive robust constraint-force is also considered. The online-learning algorithms of the control parameters (WFCNNs, robust term and constraint-force controller) are obtained by using the Lyapunov stability theorem. Findings – The design of the proposed method is determined by the Lyapunov theorem such that the stability and robustness of the control-system are guaranteed. Originality/value – The WFCNNs are more the generalized networks that can overcome the constant out-weight problem of the conventional fuzzy cerebellar model articulation controller (FCMAC), or can converge faster, give smaller approximation errors and size of networks in comparison with FNNs/NNs. In addition, an intelligent-control system by inheriting the advantage of the conventional backstepping-control-system is proposed to achieve the high-position tracking for the MMR control system in the presence of uncertainties variation.

A Novel Real-Time Fault Diagnostic System for Steam Turbine Generator Set by Using Strata Hierarchical Artificial Neural Network

The real-time fault diagnosis system is very great important for steam turbine generator set due to a serious fault results in a reduced amount of electricity supply in power plant. A novel real-time fault diagnosis system is proposed by using strata hierarchical fuzzy CMAC neural network. A framework of the fault diagnosis system is described. Hierarchical fault diagnostic structure is discussed in detail. The model of a novel fault diagnosis system by using fuzzy CMAC are built and analyzed. A case of the diagnosis is simulated. The results show that the real-time fault diagnostic system is of high accuracy, quick convergence, and high noise rejection. It is also found that this model is feasible in real-time fault diagnosis.

Nonlinear Systems Identification via Two Types of Recurrent Fuzzy CMAC

Normal fuzzy CMAC neural network performs well for nonlinear systems identification because of its fast learning speed and local generalization capability for approximating nonlinear functions. However, it requires huge memory and the dimension increases exponentially with the number of inputs. It is difficult to model dynamic systems with static fuzzy CMACs. In this paper, we use two types of recurrent techniques for fuzzy CMAC to overcome the above problems. The new CMAC neural networks are named recurrent fuzzy CMAC (RFCMAC) which add feedback connections in the inner layers (local feedback) or the output layer (global feedback). The corresponding learning algorithms have time-varying learning rates, the stabilities of the neural identifications are proven.

Autonomous biped gait pattern based on Fuzzy-CMAC neural networks

Christophe Sabourina, Kurosh Madania and Olivier Bruneaub Laboratoire Images, Signaux, et Systemes Intelligents (LISSI, EA 3956), Universit e Paris-XII, IUT de Senart, Avenue Pierre Point, 77127 Lieusaint, France E-mail: sabourin@univ-paris12.fr, madani@univ-paris12.fr Laboratoire d Ingenierie des Systemes de Versailles (LISV, EA 4048), Centre Universitaire de Technologie, 10/12 avenue de l’Europe 78140 Velizy, France

Machine degradation analysis using fuzzy CMAC neural network approach

Traditionally, machine condition is described as a dichotomous problem: normal or failure. In fact, machines go through a series of degrading states until failure occurs. Degrading states do not make machines breakdown, but they do decrease performance reliability and increase the potential for faults and failures. In this paper, a neural network approach based on fuzzy cerebellar model articulation controller (FCMAC) is proposed to analyze machine degradation severity. Two kinds of situations are discussed: (1) machine signature from different levels of degradation severity are available to train the network; (2) machine signature from only normal perfect state are available to train the network. A degradation index is developed to reflect the degradation degree. The FCMAC network works as a ‘classifier’ or a ‘condition discriminator’ in these two situations. An example is presented to demonstrate this method and the results show that the FCMAC network is capable of ranking machine degradations quantitatively in both situations. With degradation analysis, maintenance activities can be implemented before failure eventually occurs, thus preventing fatal breakdowns more effectively.

Fuzzy System and CMAC Network with B-Spline Membership/Basis Functions can Approximate A Smooth Function and its Derivatives

In control and other modeling applications, fuzzy system with B-spline membership functions and CMAC neural network with B-spline basis functions are sometimes desired to approximate not only the assigned smooth function as well as its derivatives. In this paper, by designing the fuzzy system and CMAC neural network with B-spline basis functions, we prove that such a fuzzy system and CMAC can universally approximate a smooth function and its derivatives, that is to say, for a given accuracy, we can approximate an arbitrary smooth function by such fuzzy system and CMAC that not only the function is approximated within this accuracy, but its derivatives are approximated as well. The conclusions here provide solid theoretical foundation for their extensive applications.

Variable structure control of electrohydraulic servo systems using fuzzy CMAC neural network

This paper presents a novel variable structure control (VSC) scheme based on the use of fuzzy cerebellar model articulation controller (FCMAC) for the control of electrohydraulic position servo systems. FCMAC is used to develop an adaptive equivalent control law by learning the uncertainties resulting from unmodelled dynamics and external disturbances. This feature makes it possible to use a small gain of the discontinuous control while satisfying the existence condition of the sliding mode in the presence of large uncertainties. As a result, the chattering is suppressed greatly, while the merits of the traditional VSC are maintained. The actual switching function is used to update the FCMAC weights and the existence of the sliding mode is proved. The simulations and experiments on a typical electrohydraulic position servo system are conducted to verify the effectiveness and robustness of the control scheme.

Fuzzy system and CMAC network with B-spline membership/basis functions are smooth approximators

Due to their universal approximation, fuzzy system with B-spline membership functions and CMAC neural network with B-spline basis functions have been extensively used in control. In many practical applications, they are desired to approximate not only the assigned smooth function as well as its derivatives. In this paper, by designing a fuzzy system and CMAC neural network with B-spline basis functions, we prove that such a fuzzy system and CMAC can universally approximate a smooth function and its derivatives, i.e, for a given accuracy, we can approximate an arbitrary smooth function by such a fuzzy system and CMAC that not only the function is approximate within this accuracy, but its derivatives are approximated as well. The conclusions here provide solid theoretical foundation for their extensive applications.

Modeling and intellligent chatter control strategies for a lathe machine

In the active chatter control of machine tools, the most effective way to suppress the chatter is to place the actuator as close as possible to the tool tip. However, in practice, it is almost impossible to put the actuator at the same location of the tool tip. Also, in many machines the cutting tools are usually long and may be flexible. Both of these problems pose serious problems in machine chatter control. In order to control the chatter effectively and efficiently, a systematic methodology is proposed in this paper to deal with the modeling and control system design aspects of this challenging problem. Because of the flexibility effect in the tool shaft, conventional active control approaches may not perform in an efficient and effective manner. Here, two advanced control algorithms (LMS adaptive filter and fuzzy CMAC neural network) are proposed to counteract this problem. Experimental results on a lathe machine are also included. Approximately 20 dB reduction in chatter has been achieved.

Fuzzy CMAC Neural Networks

This article reveals the connection between the cerebellar model arithmetic computer CMAC neural network and fuzzy inference systems. A novel artificial neural network architecture called the fuzzy CMAC neural network is established that achieves the synergistic combination of the preferred features of the CMAC neural network and the fuzzy logic controller. The proposed combination of fuzzy logic and neural net techniques has significant potential to decrease the cost, complexity, and learning time over conventional neural nets. It retains the benefits of learning arbitrary functions through training, but it also captures the benefits of fuzzy logic, which allows heuristic rules to be generated by experts using linguistic variables of meaning to the particular problem domain. We believe that the work herein proposed will produce a generic fuzzy CMAC for a wide range of applications.