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This paper studies a novel adaptive fractional-order non-singular terminal sliding mode (FO-NTSM) control strategy for omnidirectional mobile robot manipulator (OMRM) with unknown parameters and external disturbances. Firstly, we adopt the fuzzy wavelet neural networks (FWNNs) to estimate the dynamic uncertainty of the OMRM because it has superior function approximation capability. Secondly, we design the adaptive NTSM controller to attenuate external disturbances by virtue of adjusting the weights of the FWNNs online. Moreover, we obtain a fractional-order (FO) control criterion, which speed up the convergence of the algorithm. In addition, we prove the globally robust stability of the OMRM control system through a designed Lyapunov function. Finally, simulation and experiment researches indicate the feasible and validity of the presented method. • By applying fractional calculus, a novel adaptive sliding mode controller with FO is proposed to investigate the trajectory tracking for OMRM system in present of unknown parameters and external disturbances. • The superior combination of FWNNs and robust techniques not only largely overcomes the model uncertainty of the controlled object, but also is more robust to external unfavorable factors. • The designed controller is applied to a 3-DOF OMRM, accordingly. It is verified by experiments that the actual control performances are considerable. [ABSTRACT FROM AUTHOR]

This paper focuses on fuzzy wavelet neural networks-based adaptive finite-time self-triggered fault-tolerant control design with assured tracking performance for a quadrotor unmanned aerial vehicle subject to unknown actuator faults. First, an improved finite-time performance function is integrated with the command-filtered backstepping control framework to assure the scheduled tracking performance, which can effectively constrain the transient fluctuations of the tracking error at fault occurrence. Then, two compensation mechanisms are developed to weaken the adverse impact induced by actuator faults and filter errors. Further, a fuzzy wavelet neural adaptive self-triggered fault-tolerant controller with guaranteed performance is designed to improve the fault insensitivity and tracking accuracy of the controlled vehicle, where the fuzzy wavelet neural networks are employed to approximate the unknown nonlinearities and the control signals are allowed to achieve irregular updating only when the pre-specified trigger protocol is violated. Stability analysis proves that the designed controller guarantees the attitude and position subsystems are practical finite-time stable, and the tracking errors are strictly confined to a preassigned region and never cross its allowed bounds. Finally, the validity of the developed control scheme is confirmed by the simulation results. [ABSTRACT FROM AUTHOR]

Not only adaptive predictive control of switched systems is a computationally intensive procedure, it also involves various challenges in addressing the problems of robust stabilization and precise tracking. This study proposes new strategies to deal with the aforementioned issues (namely safe and precise control alongside with reduction of computational burden). The first contribution of this work is reduction of conservatism for described class of systems. Control of switched systems with undetectable switching signals is often conducted in worst case switching configuration to ensure robustness, which potentially results in conservative design. The issue of conservativeness is intensified in multi input‐multi output (MIMO) dynamical systems due to increased dimensions. However, attaining a robust control scheme for all switching configurations while ensuring precise response is inherently paradoxical. To overcome this issue, this study proposes a new dual‐mode algorithm where control modes corresponding to safety and precision are activated at appropriate stages of system response. This is conducted based on incorporation of an adaptive fuzzy‐wavelet neural network identification scheme in predictive control of MIMO switched systems. However, as convergence of the adaptive algorithm to actual system is attained after a finite period of time, a safe‐mode control algorithm is proposed to maintain quality of transient response in convergence period. In other words, the proposed algorithm operates in safe and precise control modes to ensure robust stability in the convergence period and non‐conservative design in steady‐state. Second major contribution of the work is reduction of calculation burden based on incorporation of a suboptimal control algorithm. To this end, we propose a predictive control scheme based on a suboptimal gradient‐descent based controller, calculating feasible stabilizing inputs instead of optimal inputs. Effects of dynamical variations are incorporated in the model predictive control framework for increased compatibility with high‐speed switching dynamics. Then, based on incorporation of dual‐mode algorithm, precise steady‐state performance is attained while preventing notable perturbations in dynamical discontinuities at switching. [ABSTRACT FROM AUTHOR]

Newborns with health complications have great difficulty in regulating the body temperature due to distinct factors, which include the high metabolism rate and low weight. In this context, neonatal incubators help maintaining good health conditions because they provide a thermally-neutral environment, which is adequate to ensure the least energy expenditure by the newborn. In the last decades, artificial neural networks (ANNs) have been established as one of the main tools for the identification of nonlinear systems. Among the various approaches used in the identification process, the fuzzy wavelet neural network (FWNN) can be regarded as a prominent technique, consisting of the combination of wavelet neural network (WNN) and adaptive network-based fuzzy inference system (ANFIS). This work proposes the use of FWNN to infer the temperature and humidity values inside the incubator in order to certify the equipment operation. Results obtained with the analyzed neural system have shown the generalization and inference capacities of FWNNs, thus allowing their application to practical tasks aiming to increase the efficiency of incubators. [ABSTRACT FROM AUTHOR]

In this paper we use a real-coding Genetic Algorithm to evolve three different architectures of Fuzzy Wavelet Neural Networks (FWNNs) and to compare their performances when predicting financial time series (stock exchange indexes) by means of NARX(na, nb, nk) models. FWNNs are designed by hybridization of Neural Networks, Fuzzy Inference Systems and Wavelet Functions as substitutes of Sigmoidal Activation Functions. The following FWNN architectures are evolved and compared: a summation-type FWNN, a multiplication-type FWNN and an extended version of the latter with both nonlinear and linear terms in the consequent part. Hybridization can be viewed as a way to overcome the limitations of individual techniques and to combine their benefits. It allows obtaining networks with the explanatory capabilities of fuzzy systems, a small number of neurons in the hidden layer, a fast training speed and a more precise and quick convergence. We use Matlab for implementing the algorithms and validate their predictive performance on data from Bucharest Stock Exchange. [ABSTRACT FROM AUTHOR]

A direct nonaffine hybrid control methodology is proposed for a generic hypersonic flight models based on fuzzy wavelet neural networks (FWNNs). The addressed strategy extends the previous indirect nonaffine control approaches stemming from simplified models of affine formulations. To cope with nonaffine effects on control design, analytically invertible models are constructed and then novel hybrid controllers are developed directly using nonaffine models. Furthermore, by employing FWNNs to devise adaptive terms, inversion errors are canceled via fuzzy neural approximations. In addition, robust terms are designed to achieve larger stable region in comparison with earlier work using Lyapunov synthesis. Finally, numerical simulation results from a hypersonic flight vehicle model are given to clarify the efficiency of the proposed direct nonaffine control scheme in the presence of parametric uncertainties. [ABSTRACT FROM AUTHOR]

In this paper, a robust adaptive control method based on Dynamic Structure Fuzzy Wavelet Neural Networks (FWNNs) system is presented for trajectory tracking control of industrial robot manipulators (IRM) with uncertainties and disturbances via adaptive sliding mode control (SMC). Four layer FWNNs in the Dynamic structure FWNNs is constructed on the basis of fuzzy rules which associates with wavelet function in the consequent part, to compensate for structured and unstructured uncertainties and model complex processes. However, it is difficult to design a suitable control scheme to achieve the required approximation errors, such as friction forces, external disturbances error and parameter variations. To deal with the mentioned problems, all the parameters of the Dynamic structure FWNNs system are tuned on-line by an adaptive learning algorithm, and adaptive robust control laws are determined by Lyapunov stability theorem. By using Dynamic structure FWNNs, this control system could achieve desired tracking performance, the stability and robustness of the closed-loop manipulators system are guaranteed. In addition, the simulations and experimental performed on a three-link IRM are provided in comparison with wavelet network control (WNC) and adaptive Fuzzy control (AFC) to demonstrate the effectiveness and robustness of the proposed Dynamic structure FWNNs methodology. [ABSTRACT FROM AUTHOR]

Food product safety is one of the most promising areas for the application of electronic noses. Their application in this domain is mainly focused on quality control, freshness evaluation, shelf-life analysis and authenticity assessment. In this paper, the performance of a portable electronic nose has been evaluated in monitoring the spoilage of beef fillets stored either aerobically or under modified atmosphere packaging, at different storage temperatures. A novel multi-output fuzzy wavelet neural network architecture has been developed, which incorporates a clustering pre-processing stage for the definition of fuzzy rules. The dual purpose of the proposed modelling approach is not only to classify beef samples in the relevant quality class (i.e. fresh, semi-fresh and spoiled), but also to predict their associated microbiological population, based on total viable counts. For the case of aerobic packaging, model was able to classify correctly 67 out of 70 aerobic samples (95.71%), while successful identification of microbial counts resulted in a 4.57% standard error of prediction. However, under modified atmosphere packaging scenario, results were rather inferior, as proposed model achieved a 92.95% classification rate (66 out of 71 samples), while the standard error of prediction of microbial counts was increased to 5.74%. In comparison to these results, prediction performances of models used extensively in the area of Food Microbiology, such as MLP and PLS, revealed their deficiencies, while ANFIS and SVM models revealed their robustness in providing acceptable prediction performances for either aerobic or MAP packaging conditions. Results evaluation indicated that the proposed modelling scheme could be considered as a valuable detection methodology in food microbiology. [ABSTRACT FROM AUTHOR]

In this paper, we propose an adaptive position tracking system and a force control strategy for nonholonomic mobile robot manipulators, which incorporate the merits of Fuzzy Wavelet Neural Networks (FWNNs). In general, it is not easy to adopt a model-based method to achieve this control object due to the uncertainties of mobile robot manipulators control system, such as unknown dynamics, disturbances and parameter variations. To solve this problem, an adaptive FWNNs control scheme with the online learning ability is utilized to approximate the unknown dynamics without the requirement of prior system information. In addition, an adaptive robust compensator is proposed to eliminate uncertainties that consist of approximation errors, disturbances, optimal parameters and higher order terms in Taylor series. According to adaptive position tracking control design, an adaptive robust control strategy is also considered for nonholonomic constraint force. The design of adaptive online learning algorithms is derived using Lyapunov stability theorem. Therefore, the proposed controllers prove that they not only can guarantee the stability of mobile robot manipulators control system but also guarantee tracking performance. The effectiveness and robustness of the proposed method are demonstrated by comparing simulations and experimental results that are implemented in an indoor cleaning crawler-type mobile robot manipulators system. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents a robust adaptive sliding-mode control (RASMC) scheme for a class of condenser-cleaning mobile manipulator (CCMM) in the presence of parametric uncertainties and external disturbances. The development of control system is based on the fuzzy wavelet neural network (FWNN). First, a dynamic model is obtained in view of the practical CCMM system. Second, the FWNN is used to identify the unstructured system dynamics directly due to its ability to approximate a nonlinear continuous function to arbitrary accuracy. Using learning ability of neural networks, RASMC can coordinately control the condenser-cleaning mobile platform and the mounted manipulator with different dynamics efficiently. The implementation of the control algorithm is dependent on the adaptive sliding-mode control. Finally, based on the Lyapunov stability theory, the stability of the whole control system, the boundedness of the neural networks weight estimation errors, and the uniformly ultimately boundedness of the tracking error are all strictly guaranteed. Moreover, simulation results validate the superior control performance of the proposed adaptive control method. [Copyright &y& Elsevier]

Load forecasting is a critical element of power system operation, involving prediction of the future level of demand to serve as the basis for supply and demand planning. This paper presents the development of a novel clustering-based fuzzy wavelet neural network (CB-FWNN) model and validates its prediction on the short-term electric load forecasting of the Power System of the Greek Island of Crete. The proposed model is obtained from the traditional Takagi-Sugeno-Kang fuzzy system by replacing the THEN part of fuzzy rules with a "multiplication" wavelet neural network (MWNN). Multidimensional Gaussian type of activation functions have been used in the IF part of the fuzzyrules. A Fuzzy Subtractive Clustering scheme is employed as a pre-processing technique to find out the initial set and adequate number of clusters and ultimately the number of multiplication nodes in MWNN, while Gaussian Mixture Models with the Expectation Maximization algorithm are utilized for the definition of the multidimensional Gaussians. The results corresponding to the minimum and maximum power load indicate that the proposed load forecasting model provides significantly accurate forecasts, compared to conventional neural networks models. [ABSTRACT FROM AUTHOR]

An output feedback backstepping sliding mode control scheme was developed for precision positioning of a strict single-input and single-output (SISO) non-smooth nonlinear dynamic system that could compensate for deadzone, dynamic friction, uncertainty and estimations of immeasurable states. An adaptive fuzzy wavelet neural networks (FWNNs) technique was used to provide improved approximation ability to the system uncertainty. The adaptive laws were derived for application to estimate the deadzone and friction parameters using recursive backstepping controller design procedures. In addition, the sliding mode control method was also combined to enforce the robustness of the output feedback backstepping controller against disturbance. The Lyapunov stability theorem was used to prove stability of the proposed control system. The usefulness of the proposed control system was verified by simulations and experiments on a robot manipulator in the presence of a deadzone and friction in the actuator. [ABSTRACT FROM AUTHOR]

Abstract: Power load forecasting is an essential tool for energy management systems. Accurate load forecasting supports power companies to make unit commitment decisions and schedule maintenance plans appropriately. In addition to minimizing the power generation costs, it is also important for the reliability of energy systems. This research study presents the implementation of a novel fuzzy wavelet neural network model on an hourly basis, and validates its performance on the prediction of electricity consumption of the power system of the Greek Island of Crete. In the proposed framework, a multiplication wavelet neural network has replaced the classic linear model, which usually appears in the consequent part of a neurofuzzy scheme, while subtractive clustering with the aid of the Expectation–Maximization algorithm is being utilized in the definition of fuzzy rules. The results related to the minimum and maximum load using metered data obtained from the power system of the Greek Island of Crete indicate that the proposed forecasting model provides significantly better forecasts, compared to conventional neural networks models applied on the same dataset. [Copyright &y& Elsevier]

Abstract: In view of the defects of the prediction model based on neural network, such as when doing prediction of nonlinear sequence, it is likely to fall into local hypo-strong point, and the rate of training is very slow. This paper presents a fuzzy wavelet neural network (FWNN) approach for annual electricity consumption in high energy consumption city. It is claimed that, due to high fluctuations of energy consumption in high energy consumption cities, conventional regression models do not forecast energy consumption correctly and precisely. Although ANNs have been typically used to forecast short term consumptions, this paper shows that it is a more precise approach to forecast annual electricity consumption. Furthermore, the FWNN approach based on ANN is used to show it can estimate the annual consumption with less error. Actual data from high energy consuming (intensive) from 1983 to 2003 is used to illustrate the applicability of the FWNN approach. This is the first study to present an algorithm based on the ANN and wavelet for forecasting long term electricity consumption in high energy consuming city. The prediction effect of wavelet neural network prediction model is proved in matlab7.0 simulation environment. A better prediction result is gained, and the defect of falling into local hypo-strongpoint is overcome, at the same time, the rate of training is raised compared with the prediction model based on artificial neural network. The calculation result shows that the presented model is effective. [Copyright &y& Elsevier]

This paper presents fuzzy wavelet neural network (FWNN) models for prediction and identification of nonlinear dynamical systems. The proposed FWNN models are obtained from the traditional Takagi-Sugeno–Kang fuzzy system by replacing the THEN part of fuzzy rules with wavelet basis functions that have the ability to localize both in time and frequency domains. The first and last model use summation and multiplication of dilated and translated versions of single-dimensional wavelet basis functions, respectively, and in the second model, THEN parts of the rules consist of radial function of wavelets. Gaussian type of activation functions are used in IF part of the fuzzy rules. A fast gradient-based training algorithm, i.e., the Broyden-Fletcher–Goldfarb-Shanno method, is used to find the optimal values for unknown parameters of the FWNN models. Simulation examples are also given to compare the effectiveness of the models with the other known methods in the literature. According to simulation results, we see that the proposed FWNN models have impressive generalization ability. [ABSTRACT FROM PUBLISHER]