Parameters Of Fuzzy Logic Systems Research Articles

Fuzzy Adaptive Asymptotic Control for a Class of Large-Scale High-Order Unknown Nonlinear Systems

This paper studies the asymptotic control problem of a class of large-scale high-order nonlinear systems (LSHONSs), and an asymptotic fuzzy adaptive dynamic surface controller is developed. Unknown nonlinear terms are learned online by fuzzy logic systems (FLSs) such that the accurate nonlinear model is released in the controller design procedure, where the parameters of FLSs are updated by developing adaptive laws. To compensate for the “boundary error” caused by the dynamic surface control method where a linear filter is added in the backstepping procedure to handle the “explosion of complexity” problem, a nonlinear filter is proposed to eliminate the boundary layer error. Some simulations are given to demonstrate the effectiveness of the proposed algorithm.

Design and application of Nagar-Bardini structure-based interval type-2 fuzzy logic systems optimized with the combination of backpropagation algorithms and recursive least square algorithms

This paper designs a kind of Nagar-Bardini structure based on interval type-2 (IT2) fuzzy logic systems for permanent magnetic drive uncertain parameter forecasting. For each fuzzy rule, the consequent, antecedent and input measurement of IT2 membership functions (MFs) are selected as the Gaussian type-2 MFs with uncertain standard deviations. Backpropagation algorithms and recursive least square algorithms are used to optimize the antecedent, input measurement and consequent parameters of fuzzy logic system (FLS) forecasters, respectively. Compared with the corresponding singleton and non-singleton type-1 (T1) FLSs, two Monte Carlo simulation instances on the basis of data of PMD process show the effectiveness and superiority of non-singleton IT2 FLSs according to the convergence analysis.

Robust multi-input multi-output adaptive fuzzy terminal sliding mode control of deep brain stimulation in Parkinson\u2019s disease: a simulation study

Deep brain stimulation (DBS) has become an effective therapeutic solution for Parkinson’s disease (PD). Adaptive closed-loop DBS can be used to minimize stimulation-induced side effects by automatically determining the stimulation parameters based on the PD dynamics. In this paper, by modeling the interaction between the neurons in populations of the thalamic, the network-level modulation of thalamic is represented in a standard canonical form as a multi-input multi-output (MIMO) nonlinear first-order system with uncertainty and external disturbances. A class of fast and robust MIMO adaptive fuzzy terminal sliding mode control (AFTSMC) has been presented for control of membrane potential of thalamic neuron populations through continuous adaptive DBS current applied to the thalamus. A fuzzy logic system (FLS) is used to estimate the unknown nonlinear dynamics of the model, and the weights of FLS are adjusted online to guarantee the convergence of FLS parameters to optimal values. The simulation results show that the proposed AFTSMC not only significantly produces lower tracking errors in comparison with the classical adaptive fuzzy sliding mode control (AFSMC), but also makes more robust and reliable outputs. The results suggest that the proposed AFTSMC provides a more robust and smooth control input which is highly desirable for hardware design and implementation.

Fuzzy logic control with genetic membership function parameters optimization for the output regulation of a servomechanism with nonlinear backlash

The paper presents a hybrid architecture, which combines Type-1 or Type-2 fuzzy logic system (FLS) and genetic algorithms (GAs) for the optimization of the membership function (MF) parameters of FLS, in order to solve to the output regulation problem of a servomechanism with nonlinear backlash. In this approach, the fuzzy rule base is predesigned by experts of this problem. The proposed method is far from trivial because of nonminimum phase properties of the system. The simulation results illustrate the effectiveness of the optimized closed-loop system.

Design and implementation of an adaptive fuzzy logic-based controller for wheeled mobile robots

In this paper, a control structure that makes possible the integration of a kinematic controller and an adaptive fuzzy controller for trajectory tracking is developed for nonholonomic mobile robots. The system uncertainty, which includes mobile robot parameter variation and unknown nonlinearities, is estimated by a fuzzy logic system (FLS). The proposed adaptive controller structure represents an amalgamation of nonlinear processing elements and the theory of function approximation using FLS. The real-time control of mobile robots is achieved through the online tuning of FLS parameters. The system stability and the convergence of tracking errors are proved using the Lyapunov stability theory. Computer simulations are presented which confirm the effectiveness of the proposed tracking control law. The efficacy of the proposed control law is tested experimentally by a differentially driven mobile robot. Both simulation and results are described in detail.

An artificial intelligence system for a complex electromagnetic field problem. II. Method implementation and performance analysis

For pt. I see ibid., vol. 35, no. 1, p. 516-22 (1999). An artificial intelligence system has been developed to determine the electromagnetic field in the complex problem of a faulty overhead transmission line above earth and a buried pipeline. The amplitude and phase of the magnetic vector potential (MVP) in the earth around the pipeline neighborhood, including the pipeline itself, are calculated, The performance of the trained fuzzy logic system (FLS) described in Part I was tested extensively for various configurations of the above electromagnetic field problem, differing significantly from the cases used for training. The trained FLS parameters required to calculate the electromagnetic field by simple formulas are also presented.