Adaptive Fuzzy Sliding Mode Control for Brushless Doubly Fed Machine

A new adaptive fuzzy sliding-mode control (FSMC) scheme was proposed, which incorporated the fuzzy logic into sliding-mode control using fuzzy logic to approximate the equivalent control and the upper bound of uncertainty which involved the disturbance and approximation error, thus the influence of modeling error was reduced and the gain of sliding-mode control part was more fitting, such that the chattering effects could be alleviated . Besides, inspired by the thought of fully tuned RBF neural networks, a fully tuned fuzzy logic was proposed which enhanced the approximating ability of fuzzy logic system. Lyapunov stability theorem was used to prove the stability of the system and the adaptive laws were deduced. Finally, simulation results of some BTT missile were included to illustrate the effectiveness of the adaptive fuzzy sliding-mode control scheme.

The increasing use of nonlinear loads entails serious electrical energy quality problems in power grids. Active power filter could lead to significant improvement in harmonic compensation performance. In this paper, an adaptive second-order sliding mode fuzzy control based on linear feedback strategy for a three-phase active power filter is proposed to eliminate harmonics. This scheme is constituted by adaptive controller, second-order sliding mode controller, fuzzy controller, and linear feedback controller. The linear feedback controller can compensate the system’s nonlinearities and make the new system have a linear behavior between the input and output. Moreover, the second-order sliding mode controller could transfer the discontinuities in the controller and reduce system chattering. Simulation results illustrate the proposed controller has better performance compared with fuzzy control and adaptive sliding mode fuzzy control.

In this paper, an adaptive fuzzy sliding-mode control (AFSMC) system is investigated for an islanded inverter to achieve a high-performance power supply. A sliding mode control (SMC) law is designed initially to obtain both the voltage tracking error and the current tracking error of the inverter involved, to realize both the output-voltage regulation and the current protection with global stability. Moreover, to deal with uncertainties in the practical inverter system without the chattering phenomenon, an adaptive fuzzy system embedded with a self-adjustive translation width is developed to replace the switch term of the SMC. In addition, the adaptation laws, derived from the Lyapunov stability theorem, adjust the AFSMC parameters online to guarantee optimal and robust performance. Furthermore, the superior control performance of the proposed AFSMC is verified by a numerical simulation in MATLAB, producing experimental results on the prototype in comparison with the conventional SMC.

EP-based kinematic control and adaptive fuzzy sliding-mode dynamic control for wheeled mobile robots

An effective method, adaptive fuzzy sliding-mode (SM) PI Control to design full-bridge inverter is proposed in this paper. For practical applications, sliding mode control (SMC) with chattering phenomenon is a large hindrance. The modified model of the PI controller is built by the plant extension scheme, in order to relieve the chattering. Also, the fuzzy logic is applied to find the appropriate parameter values of the PI controller, and can adapt to plant parameter variations and external load disturbances. The proposed method has been realized for the actual full-bridge inverters controlled by DSP and CPLD. Simulation and experimental results of the closed-loop system show that the proposed method yields system performance improvement, such as low total harmonic distortion (THD), fast dynamic response, reduced steady-state error, and reduced chattering.

This work presents a new design for adaptive fuzzy sliding-mode control based on two methodologies, namely H∞ control and sliding-mode control, and its control effectiveness. This is achieved by implementing a control scheme for vibration control of a vehicle with a seat suspension on which a magnetorheological damper is installed. The sliding surface of sliding-mode control is analysed by separation into two matrices: a Hurwitz-constants matrix and a constant matrix. These matrices are the basis for establishing the proposed control scheme combined with the H∞ technique. The control scheme consisting of the combination of H∞ control and sliding-mode control is reinforced by a new robustness function featuring an exponential function. In this work, a fuzzy logic model, which is well known to be an excellent model for uncertain dynamic systems, is integrated with the proposed control algorithm. The fuzzy logic model adopted in this work is an interval type-2 fuzzy model featuring fast computation of the output. The effectiveness of the proposed control scheme is evaluated through both computer simulations and experimental realization on a vehicle with a seat suspension which is equipped with a magnetorheological damper. In addition, in this work, two existing adaptive controllers are modified and implemented for comparative work with the proposed control scheme. It is shown that the proposed control scheme exhibits a much better vibration control performance than the two existing adaptive controllers do.

A new adaptive fuzzy sliding mode control scheme is proposed, which combines the fuzzy logic with sliding-mode control together using fuzzy logic system to approximate the difference of the equivalent control between the nominal and the real system. Lyapunov stability theorem is used to prove the stability of the system and the adaptive laws are deduced which possess the characteristics of sliding-mode control. In addition, for better control performance, the upper bound of the uncertainty is estimated. Finally, the correctness and effectiveness of the method have been verified by simulation using nonlinear overload model of pitch channel of some BTT missile.

This paper intends to design and develop an adaptive fuzzy sliding mode controller (SMC) for robotic manipulator. Since it is not viable to pair the SMC operations with the system model every time, this paper adopts a Fuzzy Inference System (FIS) to replace the system model. It effectively achieves the experimentation in two phases. Accordingly, in the first phase, it attains the accurate features of the system model based on varied samples to characterize the robotic manipulator. In the second stage, it represents the derived fuzzy rules based on adaptive fuzzy membership functions. Moreover, it establishes the self-adaptiveness using Grey Wolf Optimization (GWO) to attain the adaptive fuzzy membership functions. The analysis distinguishes the efficiency of the adopted technique with the optimal investigational scheme and the traditional schemes such as SMC, Fuzzy SMC (FSMC) and GWO-SMC. Moreover, the comparative analysis is also performed by including the noise and validates the effectiveness of the proposed and conventional models. Index Terms : Sliding Mode Control, Robot manipulators, Controller, Noise.

Depth control of remotely operated underwater vehicles using an adaptive fuzzy sliding mode controller

PNEUMATIC ISOLATORS ARE PROMISING CANDIDATES FOR INCREASING THE QUALITY OF THE PRECISION INSTRUMENTS. FOR THIS PURPOSE, HOWEVER, HIGHER PERFORMANCE OF SUCH ISOLATORS IS A PREREQUISITE. IN THIS PAPER AN ADAPTIVE FUZZY SLIDING MODE CONTROLLER IS PROPOSED TO IMPROVE THE PERFORMANCE OF A PNEUMATIC ISOLATOR IN THE LOW FREQUENCY RANGE, I.E., WHERE THE PASSIVE TECHNIQUES HAVE OBVIOUS SHORTCOMINGS. THE MAIN IDEA IS TO COMBINE THE ADAPTIVE FUZZY CONTROLLER WITH ADAPTIVE PREDICTOR AS A NEW TIME DELAY CONTROL TECHNIQUE. THE ADAPTIVE FUZZY SLIDING MODE CONTROL AND THE ADAPTIVE FUZZY PREDICTOR HELP TO CIRCUMVENT THE INPUT DELAY AND NONLINEARITIES IN SUCH ISOLATORS. THE MAIN ADVANTAGE OF THE PROPOSED METHOD IS THAT THE CLOSED-LOOP SYS-TEM STABILITY IS GUARANTEED UNDER CERTAIN CONDITIONS. SIMULATION RESULTS ARE COMPARED TO THE RESULTS OF TDC TECHNIQUE TO VERIFY THE EFFECTIVENESS OF THE PROPOSED METHOD.

This paper investigates the position regulation problem of permanent magnet synchronous motor (PMSM) servo system based on adaptive fuzzy sliding mode control (AFSMC) method. Adaptive fuzzy sliding mode control is designed by using sliding surface as input of fuzzy controller, and adaptive fuzzy controller to approximate the equivalent control law, thus overcome the impact of model inaccuracy and external disturbances. Then, sliding model controller is designed to compensate the error between the equivalent control law and the fuzzy controller by using adaptive method to estimate the upper bound of the approximation error, meanwhile, adaptive fuzzy sliding mode control can weaken the chattering and improve the robustness of the system, make the motor position reach the reference value in finite time, obtaining a better tracking precision. Simulation results illustrate that the proposed control scheme has much better performance than that of conventional PI.

Echo-state network based adaptive fuzzy sliding-mode consensus control scheme for nonlinear multi-agent systems with external disturbances.

Since the hydraulic actuating suspension system has nonlinear and time-varying behavior, it is difficult to establish an accurate model for designing a model-based controller. Here, an adaptive fuzzy sliding mode controller is proposed to suppress the sprung mass position oscillation due to road surface variation. This intelligent control strategy combines an adaptive rule with fuzzy and sliding mode control algorithms. It has online learning ability to deal with the system time-varying and nonlinear uncertainty behaviors, and adjust the control rules parameters. Only eleven fuzzy rules are required for this active suspension system and these fuzzy control rules can be established and modified continuously by online learning. The experimental results show that this intelligent control algorithm effectively suppresses the oscillation amplitude of the sprung mass with respect to various road surface disturbances.

A reduced-order approach to the adaptive fuzzy sliding mode control of the constrained manipulator is proposed. Based on the Udwadia–Kalaba motion constraint equation, the dynamic equation of the constrained manipulator with both ideal and non-ideal constraints is obtained. Considering the uncertainty of the terminal non-ideal constrained force and the chattering phenomenon of sliding mode control, the adaptive fuzzy and the sliding mode control method are combined to control the constrained manipulator. Because the system is constrained, the model order reduction method is innovatively used in the control algorithm. The stability of the system is proved by Lyapunov theorem. For demonstrating the effectiveness of the control algorithm, the 2-degree-of-freedom manipulator is taken as the research object. Finally, the high-precision control of the manipulator is achieved and the chattering phenomenon caused by the sliding mode control is weakened.

In this work, a new adaptive motion control scheme for robust performance control of robot manipulators is presented. The proposed scheme is designed by combining the fuzzy logic control with the sliding mode control based on extended Kalman filter. Fuzzy logic controllers have been used successfully in many applications and were shown to be superior to the classical controllers for some nonlinear systems. Sliding mode control is a powerful approach for controlling nonlinear and uncertain systems. It is a robust control method and can be applied in the presence of model uncertainties and parameter disturbances, provided that the bounds of these uncertainties and disturbances are known. We have designed a new adaptive Sliding Mode Fuzzy Control (SMFC) method that requires only position measurements. These measurements and the input torques are used in an extended Kalman filter (EKF) to estimate the inertial parameters of the full nonlinear robot model as well as the joint positions and velocities. These estimates are used by the SMFC to generate the input torques. The combination of the EKF and the SMFC is shown to result in a stable adaptive control scheme called trajectory-tracking adaptive robot with extended Kalman (TAREK) method. The theory behind TAREK method provides clear guidelines on the selection of the design parameters for the controller. The proposed controller is applied to a two-link robot manipulator. Computer simulations show the robust performance of the proposed scheme.